Implications of freshwater flux data from the CMIP5 multimodel output across a set of Northern Hemisphere drainage basins

NASA Astrophysics Data System (ADS)

Bring, Arvid; Asokan, Shilpa M.; Jaramillo, Fernando; Jarsjö, Jerker; Levi, Lea; Pietroń, Jan; Prieto, Carmen; Rogberg, Peter; Destouni, Georgia

2015-06-01

The multimodel ensemble of the Coupled Model Intercomparison Project, Phase 5 (CMIP5) synthesizes the latest research in global climate modeling. The freshwater system on land, particularly runoff, has so far been of relatively low priority in global climate models, despite the societal and ecosystem importance of freshwater changes, and the science and policy needs for such model output on drainage basin scales. Here we investigate the implications of CMIP5 multimodel ensemble output data for the freshwater system across a set of drainage basins in the Northern Hemisphere. Results of individual models vary widely, with even ensemble mean results differing greatly from observations and implying unrealistic long-term systematic changes in water storage and level within entire basins. The CMIP5 projections of basin-scale freshwater fluxes differ considerably more from observations and among models for the warm temperate study basins than for the Arctic and cold temperate study basins. In general, the results call for concerted research efforts and model developments for improving the understanding and modeling of the freshwater system and its change drivers. Specifically, more attention to basin-scale water flux analyses should be a priority for climate model development, and an important focus for relevant model-based advice for adaptation to climate change.

The Southern Ocean in the Coupled Model Intercomparison Project phase 5

PubMed Central

Meijers, A. J. S.

2014-01-01

The Southern Ocean is an important part of the global climate system, but its complex coupled nature makes both its present state and its response to projected future climate forcing difficult to model. Clear trends in wind, sea-ice extent and ocean properties emerged from multi-model intercomparison in the Coupled Model Intercomparison Project phase 3 (CMIP3). Here, we review recent analyses of the historical and projected wind, sea ice, circulation and bulk properties of the Southern Ocean in the updated Coupled Model Intercomparison Project phase 5 (CMIP5) ensemble. Improvements to the models include higher resolutions, more complex and better-tuned parametrizations of ocean mixing, and improved biogeochemical cycles and atmospheric chemistry. CMIP5 largely reproduces the findings of CMIP3, but with smaller inter-model spreads and biases. By the end of the twenty-first century, mid-latitude wind stresses increase and shift polewards. All water masses warm, and intermediate waters freshen, while bottom waters increase in salinity. Surface mixed layers shallow, warm and freshen, whereas sea ice decreases. The upper overturning circulation intensifies, whereas bottom water formation is reduced. Significant disagreement exists between models for the response of the Antarctic Circumpolar Current strength, for reasons that are as yet unclear. PMID:24891395

Probabilistic regional climate projection in Japan using a regression model with CMIP5 multi-model ensemble experiments

NASA Astrophysics Data System (ADS)

Ishizaki, N. N.; Dairaku, K.; Ueno, G.

2016-12-01

We have developed a statistical downscaling method for estimating probabilistic climate projection using CMIP5 multi general circulation models (GCMs). A regression model was established so that the combination of weights of GCMs reflects the characteristics of the variation of observations at each grid point. Cross validations were conducted to select GCMs and to evaluate the regression model to avoid multicollinearity. By using spatially high resolution observation system, we conducted statistically downscaled probabilistic climate projections with 20-km horizontal grid spacing. Root mean squared errors for monthly mean air surface temperature and precipitation estimated by the regression method were the smallest compared with the results derived from a simple ensemble mean of GCMs and a cumulative distribution function based bias correction method. Projected changes in the mean temperature and precipitation were basically similar to those of the simple ensemble mean of GCMs. Mean precipitation was generally projected to increase associated with increased temperature and consequent increased moisture content in the air. Weakening of the winter monsoon may affect precipitation decrease in some areas. Temperature increase in excess of 4 K was expected in most areas of Japan in the end of 21st century under RCP8.5 scenario. The estimated probability of monthly precipitation exceeding 300 mm would increase around the Pacific side during the summer and the Japan Sea side during the winter season. This probabilistic climate projection based on the statistical method can be expected to bring useful information to the impact studies and risk assessments.

More robust regional precipitation projection from selected CMIP5 models based on multiple-dimensional metrics

NASA Astrophysics Data System (ADS)

Qian, Y.; Wang, L.; Leung, L. R.; Lin, G.; Lu, J.; Gao, Y.; Zhang, Y.

2017-12-01

Projecting precipitation changes is challenging because of incomplete understanding of the climate system and biases and uncertainty in climate models. In East Asia where summer precipitation is dominantly influenced by the monsoon circulation and the global models from Coupled Model Intercomparison Project Phase 5 (CMIP5), however, give various projection of precipitation change for 21th century. It is critical for community to know which models' projection are more reliable in response to natural and anthropogenic forcings. In this study we defined multiple-dimensional metrics, measuring the model performance in simulating the present-day of large-scale circulation, regional precipitation and relationship between them. The large-scale circulation features examined in this study include the lower tropospheric southwesterly winds, the western North Pacific subtropical high, the South China Sea Subtropical High, and the East Asian westerly jet in the upper troposphere. Each of these circulation features transport moisture to East Asia, enhancing the moist static energy and strengthening the Meiyu moisture front that is the primary mechanism for precipitation generation in eastern China. Based on these metrics, 30 models in CMIP5 ensemble are classified into three groups. Models in the top performing group projected regional precipitation patterns that are more similar to each other than the bottom or middle performing group and consistently projected statistically significant increasing trends in two of the large-scale circulation indices and precipitation. In contrast, models in the bottom or middle performing group projected small drying or no trends in precipitation. We also find the models that only reasonably reproduce the observed precipitation climatology does not guarantee more reliable projection of future precipitation because good simulation skill could be achieved through compensating errors from multiple sources. Herein the potential for more robust projections of precipitation changes at regional scale is demonstrated through the use of discriminating metric to subsample the multi-model ensemble. The results from this study provides insights for how to select models from CMIP ensemble to project regional climate and hydrological cycle changes.

Study Variability of Seasonal Soil Moisture in Ensemble of CMIP5 Models Over South Asia During 1950-2005

NASA Astrophysics Data System (ADS)

Fahim, A. M.; Shen, R.; Yue, Z.; Di, W.; Mushtaq Shah, S.

2015-12-01

Moisture in the upper most layer of soil column from 14 different models under Coupled Model Intercomparison Project Phase-5 (CMIP5) project were analyzed for four seasons of the year. Aim of this study was to explore variability in soil moisture over south Asia using multi model ensemble and relationship between summer rainfall and soil moisture for spring and summer season. GLDAS (Global Land Data Assimilation System) dataset set was used for comparing CMIP5 ensemble mean soil moisture in different season. Ensemble mean represents soil moisture well in accordance with the geographical features; prominent arid regions are indicated profoundly. Empirical Orthogonal Function (EOF) analysis was applied to study the variability. First component of EOF explains 17%, 16%, 11% and 11% variability for spring, summer, autumn and winter season respectively. Analysis reveal increasing trend in soil moisture over most parts of Afghanistan, Central and north western parts of Pakistan, northern India and eastern to south eastern parts of China, in spring season. During summer, south western part of India exhibits highest negative trend while rest of the study area show minute trend (increasing or decreasing). In autumn, south west of India is under highest negative loadings. During winter season, north western parts of study area show decreasing trend. Summer rainfall has very week (negative or positive) spatial correlation, with spring soil moisture, while possess higher correlation with summer soil moisture. Our studies have significant contribution to understand complex nature of land - atmosphere interactions, as soil moisture prediction plays an important role in the cycle of sink and source of many air pollutants. Next level of research should be on filling the gaps between accurately measuring the soil moisture using satellite remote sensing and land surface modelling. Impact of soil moisture in tracking down different types of pollutant will also be studied.

Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degree global warming

NASA Astrophysics Data System (ADS)

Thober, S.; Kumar, R.; Wanders, N.; Marx, A.; Pan, M.; Rakovec, O.; Samaniego, L. E.; Sheffield, J.; Wood, E. F.; Zink, M.

2017-12-01

Severe river floods often result in huge economic losses and fatalities. Since 1980, almost 1500 such events have been reported in Europe. This study investigates climate change impacts on European floods under 1.5, 2, and 3 K global warming. The impacts are assessed employing a multi-model ensemble containing three hydrologic models (HMs: mHM, Noah-MP, PCR-GLOBWB) forced by five CMIP5 General Circulation Models (GCMs) under three Representative Concentration Pathways (RCPs 2.6, 6.0, and 8.5). This multi-model ensemble is unprecedented with respect to the combination of its size (45 realisations) and its spatial resolution, which is 5 km over entire Europe. Climate change impacts are quantified for high flows and flood events, represented by 10% exceedance probability and annual maxima of daily streamflow, respectively. The multi-model ensemble points to the Mediterranean region as a hotspot of changes with significant decrements in high flows from -11% at 1.5 K up to -30% at 3 K global warming mainly resulting from reduced precipitation. Small changes (< ±10%) are observed for river basins in Central Europe and the British Isles under different levels of warming. Projected higher annual precipitation increases high flows in Scandinavia, but reduced snow water equivalent decreases flood events in this region. The contribution by the GCMs to the overall uncertainties of the ensemble is in general higher than that by the HMs. The latter, however, have a substantial share of the overall uncertainty and exceed GCM uncertainty in the Mediterranean and Scandinavia. Adaptation measures for limiting the impacts of global warming could be similar under 1.5 K and 2 K global warming, but has to account for significantly higher changes under 3 K global warming.

Multidecadal Variability in Surface Albedo Feedback Across CMIP5 Models

NASA Astrophysics Data System (ADS)

Schneider, Adam; Flanner, Mark; Perket, Justin

2018-02-01

Previous studies quantify surface albedo feedback (SAF) in climate change, but few assess its variability on decadal time scales. Using the Coupled Model Intercomparison Project Version 5 (CMIP5) multimodel ensemble data set, we calculate time evolving SAF in multiple decades from surface albedo and temperature linear regressions. Results are meaningful when temperature change exceeds 0.5 K. Decadal-scale SAF is strongly correlated with century-scale SAF during the 21st century. Throughout the 21st century, multimodel ensemble mean SAF increases from 0.37 to 0.42 W m-2 K-1. These results suggest that models' mean decadal-scale SAFs are good estimates of their century-scale SAFs if there is at least 0.5 K temperature change. Persistent SAF into the late 21st century indicates ongoing capacity for Arctic albedo decline despite there being less sea ice. If the CMIP5 multimodel ensemble results are representative of the Earth, we cannot expect decreasing Arctic sea ice extent to suppress SAF in the 21st century.

Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations

DOE PAGES

Mehran, Ali; AghaKouchak, Amir; Phillips, Thomas J.

2014-02-25

Numerous studies have emphasized that climate simulations are subject to various biases and uncertainties. The objective of this study is to cross-validate 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations of precipitation against the Global Precipitation Climatology Project (GPCP) data, quantifying model pattern discrepancies and biases for both entire data distributions and their upper tails. The results of the Volumetric Hit Index (VHI) analysis of the total monthly precipitation amounts show that most CMIP5 simulations are in good agreement with GPCP patterns in many areas, but that their replication of observed precipitation over arid regions and certain sub-continentalmore » regions (e.g., northern Eurasia, eastern Russia, central Australia) is problematical. Overall, the VHI of the multi-model ensemble mean and median also are superior to that of the individual CMIP5 models. However, at high quantiles of reference data (e.g., the 75th and 90th percentiles), all climate models display low skill in simulating precipitation, except over North America, the Amazon, and central Africa. Analyses of total bias (B) in CMIP5 simulations reveal that most models overestimate precipitation over regions of complex topography (e.g. western North and South America and southern Africa and Asia), while underestimating it over arid regions. Also, while most climate model simulations show low biases over Europe, inter-model variations in bias over Australia and Amazonia are considerable. The Quantile Bias (QB) analyses indicate that CMIP5 simulations are even more biased at high quantiles of precipitation. Lastly, we found that a simple mean-field bias removal improves the overall B and VHI values, but does not make a significant improvement in these model performance metrics at high quantiles of precipitation.« less

Monsoons and ITCZ in TRACMIP, the Tropical Rain belts with an Annual cycle and Continent - Model Intercomparison Project

NASA Astrophysics Data System (ADS)

Biasutti, M.; Voigt, A.; Scheff, J.

2016-12-01

TRACMIP consists of a set of five experiments performed by an ensemble of GCMs and conceived as a link in the hierarchy between the CFMIP/CMIP5 Aqua experiments and the CMIP5 comprehensive simulations. The basic configuration is an aquaplanet AGCM coupled to a slab ocean. By using interactive sea-surface temperatures and seasonally-varying insolation TRACMIP fills the gap between Aquaplanets with prescribed SSTs and fully-coupled realistic CMIP5 simulations. Adding to the basic Aquaplanet configuration a highly-idealized tropical continent allows the investigation of the role of zonal asymmetries in the dynamics of the ITCZ and of the source of the observed differences between land convection and monsoon circulations on one hand, and oceanic convection in the ITCZ and the Warm Pool on the other. Finally, by including both key forcings of the future (greenhouse gases) and of the Holocene (orbital changes in insolation), TRACMIP contributes to the "past to future (P2F)" efforts to connect the climate response to different forcings via a basic understanding of the mechanisms at play. TRACMIP includes the participation of both CMIP5 comprehensive climate models and a simplified model that neglects cloud and water-vapor radiative feedbacks, thus allowing a more direct connection between GCMs results and theoretical studies of tropical rain belt dynamics. We will present preliminary results from the ensemble, aiming to examine the mechanisms controlling tropical precipitation in the context of forced variability. First and foremost, we are interested in the largest forced variation: the annual cycle. We will draw out the similarities and the distinctions between the climatologies of the oceanic and continental rain bands, study the ways in which the two interact with each other, and investigate the extent to which established zonal-mean ITCZ frameworks contain information about regional rainfall characteristics. Second, we will investigate the response to quadrupling the CO2 concentration and to orbital changes, comparing the multi-model mean response and the inter-model scatter to responses in the CMIP5 ensemble, paying special attention to the way in which land responds differently than ocean and even, with its presence, modifies the response of the oceanic ITCZ to external forcings.

A Simple Approach to Account for Climate Model Interdependence in Multi-Model Ensembles

NASA Astrophysics Data System (ADS)

Herger, N.; Abramowitz, G.; Angelil, O. M.; Knutti, R.; Sanderson, B.

2016-12-01

Multi-model ensembles are an indispensable tool for future climate projection and its uncertainty quantification. Ensembles containing multiple climate models generally have increased skill, consistency and reliability. Due to the lack of agreed-on alternatives, most scientists use the equally-weighted multi-model mean as they subscribe to model democracy ("one model, one vote").Different research groups are known to share sections of code, parameterizations in their model, literature, or even whole model components. Therefore, individual model runs do not represent truly independent estimates. Ignoring this dependence structure might lead to a false model consensus, wrong estimation of uncertainty and effective number of independent models.Here, we present a way to partially address this problem by selecting a subset of CMIP5 model runs so that its climatological mean minimizes the RMSE compared to a given observation product. Due to the cancelling out of errors, regional biases in the ensemble mean are reduced significantly.Using a model-as-truth experiment we demonstrate that those regional biases persist into the future and we are not fitting noise, thus providing improved observationally-constrained projections of the 21st century. The optimally selected ensemble shows significantly higher global mean surface temperature projections than the original ensemble, where all the model runs are considered. Moreover, the spread is decreased well beyond that expected from the decreased ensemble size.Several previous studies have recommended an ensemble selection approach based on performance ranking of the model runs. Here, we show that this approach can perform even worse than randomly selecting ensemble members and can thus be harmful. We suggest that accounting for interdependence in the ensemble selection process is a necessary step for robust projections for use in impact assessments, adaptation and mitigation of climate change.

Simulation of tropical cyclone activity over the western North Pacific based on CMIP5 models

NASA Astrophysics Data System (ADS)

Shen, Haibo; Zhou, Weican; Zhao, Haikun

2017-09-01

Based on the Coupled Model Inter-comparison Project 5 (CMIP5) models, the tropical cyclone (TC) activity in the summers of 1965-2005 over the western North Pacific (WNP) is simulated by a TC dynamically downscaling system. In consideration of diversity among climate models, Bayesian model averaging (BMA) and equal-weighed model averaging (EMA) methods are applied to produce the ensemble large-scale environmental factors of the CMIP5 model outputs. The environmental factors generated by BMA and EMA methods are compared, as well as the corresponding TC simulations by the downscaling system. Results indicate that BMA method shows a significant advantage over the EMA. In addition, impacts of model selections on BMA method are examined. To each factor, ten models with better performance are selected from 30 CMIP5 models and then conduct BMA, respectively. As a consequence, the ensemble environmental factors and simulated TC activity are similar with the results from the 30 models' BMA, which verifies the BMA method can afford corresponding weight for each model in the ensemble based on the model's predictive skill. Thereby, the existence of poor performance models will not particularly affect the BMA effectiveness and the ensemble outcomes are improved. Finally, based upon the BMA method and downscaling system, we analyze the sensitivity of TC activity to three important environmental factors, i.e., sea surface temperature (SST), large-scale steering flow, and vertical wind shear. Among three factors, SST and large-scale steering flow greatly affect TC tracks, while average intensity distribution is sensitive to all three environmental factors. Moreover, SST and vertical wind shear jointly play a critical role in the inter-annual variability of TC lifetime maximum intensity and frequency of intense TCs.

21st century drought outlook for major climate divisions of Texas based on CMIP5 multimodel ensemble: Implications for water resource management

NASA Astrophysics Data System (ADS)

Venkataraman, Kartik; Tummuri, Spandana; Medina, Aldo; Perry, Jordan

2016-03-01

Management of water resources in Texas (United States) is a challenging endeavor due to rapid population growth in the recent past coupled with significant spatiotemporal variations in climate. While climate conditions impact the availability of water, over-usage and lack of efficient management further complicate the dynamics of supply availability. In this paper, we provide the first look at the impact of climate change projections from an ensemble of Coupled Model Intercomparison Project Phase 5 (CMIP5) on 21st century drought characteristics under three future emission trajectories: Representative Concentration Pathway (RCP) 2.6, RCP 4.5 and RCP 8.5, using the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI). In addition, we evaluate the performance of the ensemble in simulating historical (1950-1999) observations from multiple climate divisions in Texas. Overall, the ensemble performs better in simulating historical temperature than precipitation. In semi-arid locations such as El Paso and Laredo, decreasing precipitation trends are projected even under the influence of climate policies represented by the RCP 4.5. There is little variability in the SPI across climate divisions and across RCPs. The SPEI, on the other hand, generally shows a decreasing trend toward the latter half of the 21st century, with multi-year droughts becoming the norm under the RCP 8.5, particularly in regions that are already dry, such as El Paso. Less severe droughts are projected for the sub-humid eastern edge of the state. Considering that state water planning agencies are already forecasting increased water shortages over the next 50 years, we recommend proactive approaches to risk management such as adjusting the planning tools for potential recurrence of multi-year droughts in regions that are already water-stressed.

Quasi-decadal Oscillation in the CMIP5 and CMIP3 Climate Model Simulations: California Case

NASA Astrophysics Data System (ADS)

Wang, J.; Yin, H.; Reyes, E.; Chung, F. I.

2014-12-01

The ongoing three drought years in California are reminding us of two other historical long drought periods: 1987-1992 and 1928-1934. This kind of interannual variability is corresponding to the dominating 7-15 yr quasi-decadal oscillation in precipitation and streamflow in California. When using global climate model projections to assess the climate change impact on water resources planning in California, it is natural to ask if global climate models are able to reproduce the observed interannual variability like 7-15 yr quasi-decadal oscillation. Further spectral analysis to tree ring retrieved precipitation and historical precipitation record proves the existence of 7-15 yr quasi-decadal oscillation in California. But while implementing spectral analysis to all the CMIP5 and CMIP3 global climate model historical simulations using wavelet analysis approach, it was found that only two models in CMIP3 , CGCM 2.3.2a of MRI and NCAP PCM1.0, and only two models in CMIP5, MIROC5 and CESM1-WACCM, have statistically significant 7-15 yr quasi-decadal oscillations in California. More interesting, the existence of 7-15 yr quasi-decadal oscillation in the global climate model simulation is also sensitive to initial conditions. 12-13 yr quasi-decadal oscillation occurs in one ensemble run of CGCM 2.3.2a of MRI but does not exist in the other four ensemble runs.

Comparative evaluation of the IPCC AR5 CMIP5 versus the AR4 CMIP3 model ensembles for regional precipitation and their extremes over South America

NASA Astrophysics Data System (ADS)

Tolen, J.; Kodra, E. A.; Ganguly, A. R.

2011-12-01

The assertion that higher-resolution experiments or more sophisticated process models within the IPCC AR5 CMIP5 suite of global climate model ensembles improves precipitation projections over the IPCC AR4 CMIP3 suite remains a hypothesis that needs to be rigorously tested. The questions are particularly important for local to regional assessments at scales relevant for the management of critical infrastructures and key resources, particularly for the attributes of sever precipitation events, for example, the intensity, frequency and duration of extreme precipitation. Our case study is South America, where precipitation and their extremes play a central role in sustaining natural, built and human systems. To test the hypothesis that CMIP5 improves over CMIP3 in this regard, spatial and temporal measures of prediction skill are constructed and computed by comparing climate model hindcasts with the NCEP-II reanalysis data, considered here as surrogate observations, for the entire globe and for South America. In addition, gridded precipitation observations over South America based on rain gage measurements are considered. The results suggest that the utility of the next-generation of global climate models over the current generation needs to be carefully evaluated on a case-by-case basis before communicating to resource managers and policy makers.

Creating "Intelligent" Climate Model Ensemble Averages Using a Process-Based Framework

NASA Astrophysics Data System (ADS)

Baker, N. C.; Taylor, P. C.

2014-12-01

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is often used to add value to model projections: consensus projections have been shown to consistently outperform individual models. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, certain models reproduce climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables—e.g., outgoing longwave radiation and surface temperature. Metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument and surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing weighted and unweighted model ensembles. For example, one tested metric weights the ensemble by how well models reproduce the time-series probability distribution of the cloud forcing component of reflected shortwave radiation. The weighted ensemble for this metric indicates lower simulated precipitation (up to .7 mm/day) in tropical regions than the unweighted ensemble: since CMIP5 models have been shown to overproduce precipitation, this result could indicate that the metric is effective in identifying models which simulate more realistic precipitation. Ultimately, the goal of the framework is to identify performance metrics for advising better methods for ensemble averaging models and create better climate predictions.

Multi-criterion model ensemble of CMIP5 surface air temperature over China

NASA Astrophysics Data System (ADS)

Yang, Tiantian; Tao, Yumeng; Li, Jingjing; Zhu, Qian; Su, Lu; He, Xiaojia; Zhang, Xiaoming

2018-05-01

The global circulation models (GCMs) are useful tools for simulating climate change, projecting future temperature changes, and therefore, supporting the preparation of national climate adaptation plans. However, different GCMs are not always in agreement with each other over various regions. The reason is that GCMs' configurations, module characteristics, and dynamic forcings vary from one to another. Model ensemble techniques are extensively used to post-process the outputs from GCMs and improve the variability of model outputs. Root-mean-square error (RMSE), correlation coefficient (CC, or R) and uncertainty are commonly used statistics for evaluating the performances of GCMs. However, the simultaneous achievements of all satisfactory statistics cannot be guaranteed in using many model ensemble techniques. In this paper, we propose a multi-model ensemble framework, using a state-of-art evolutionary multi-objective optimization algorithm (termed MOSPD), to evaluate different characteristics of ensemble candidates and to provide comprehensive trade-off information for different model ensemble solutions. A case study of optimizing the surface air temperature (SAT) ensemble solutions over different geographical regions of China is carried out. The data covers from the period of 1900 to 2100, and the projections of SAT are analyzed with regard to three different statistical indices (i.e., RMSE, CC, and uncertainty). Among the derived ensemble solutions, the trade-off information is further analyzed with a robust Pareto front with respect to different statistics. The comparison results over historical period (1900-2005) show that the optimized solutions are superior over that obtained simple model average, as well as any single GCM output. The improvements of statistics are varying for different climatic regions over China. Future projection (2006-2100) with the proposed ensemble method identifies that the largest (smallest) temperature changes will happen in the South Central China (the Inner Mongolia), the North Eastern China (the South Central China), and the North Western China (the South Central China), under RCP 2.6, RCP 4.5, and RCP 8.5 scenarios, respectively.

Ice Sheet Model Intercomparison Project (ISMIP6) contribution to CMIP6

PubMed Central

Nowicki, Sophie M.J.; Payne, Tony; Larour, Eric; Seroussi, Helene; Goelzer, Heiko; Lipscomb, William; Gregory, Jonathan; Abe-Ouchi, Ayako; Shepherd, Andrew

2018-01-01

Reducing the uncertainty in the past, present and future contribution of ice sheets to sea-level change requires a coordinated effort between the climate and glaciology communities. The Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) is the primary activity within the Coupled Model Intercomparison Project – phase 6 (CMIP6) focusing on the Greenland and Antarctic Ice Sheets. In this paper, we describe the framework for ISMIP6 and its relationship to other activities within CMIP6. The ISMIP6 experimental design relies on CMIP6 climate models and includes, for the first time within CMIP, coupled ice sheet – climate models as well as standalone ice sheet models. To facilitate analysis of the multi-model ensemble and to generate a set of standard climate inputs for standalone ice sheet models, ISMIP6 defines a protocol for all variables related to ice sheets. ISMIP6 will provide a basis for investigating the feedbacks, impacts, and sea-level changes associated with dynamic ice sheets and for quantifying the uncertainty in ice-sheet-sourced global sea-level change. PMID:29697697

Ice Sheet Model Intercomparison Project (ISMIP6) Contribution to CMIP6

NASA Technical Reports Server (NTRS)

Nowicki, Sophie M. J.; Payne, Tony; Larour, Eric; Seroussi, Helene; Goelzer, Heiko; Lipscomb, William; Gregory, Jonathan; Abe-Ouchi, Ayako; Shepherd, Andrew

2016-01-01

Reducing the uncertainty in the past, present, and future contribution of ice sheets to sea-level change requires a coordinated effort between the climate and glaciology communities. The Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6) is the primary activity within the Coupled Model Intercomparison Project phase 6 (CMIP6) focusing on the Greenland and Antarctic ice sheets. In this paper, we describe the framework for ISMIP6 and its relationship with other activities within CMIP6. The ISMIP6 experimental design relies on CMIP6 climate models and includes, for the first time within CMIP, coupled ice-sheetclimate models as well as standalone ice-sheet models. To facilitate analysis of the multi-model ensemble and to generate a set of standard climate inputs for standalone ice-sheet models, ISMIP6 defines a protocol for all variables related to ice sheets. ISMIP6 will provide a basis for investigating the feedbacks, impacts, and sea-level changes associated with dynamic ice sheets and for quantifying the uncertainty in ice-sheet-sourced global sea-level change.

Multi-model ensemble projections of European river floods and high flows at 1.5, 2, and 3 degrees global warming

NASA Astrophysics Data System (ADS)

Thober, Stephan; Kumar, Rohini; Wanders, Niko; Marx, Andreas; Pan, Ming; Rakovec, Oldrich; Samaniego, Luis; Sheffield, Justin; Wood, Eric F.; Zink, Matthias

2018-01-01

Severe river floods often result in huge economic losses and fatalities. Since 1980, almost 1500 such events have been reported in Europe. This study investigates climate change impacts on European floods under 1.5, 2, and 3 K global warming. The impacts are assessed employing a multi-model ensemble containing three hydrologic models (HMs: mHM, Noah-MP, PCR-GLOBWB) forced by five CMIP5 general circulation models (GCMs) under three Representative Concentration Pathways (RCPs 2.6, 6.0, and 8.5). This multi-model ensemble is unprecedented with respect to the combination of its size (45 realisations) and its spatial resolution, which is 5 km over the entirety of Europe. Climate change impacts are quantified for high flows and flood events, represented by 10% exceedance probability and annual maxima of daily streamflow, respectively. The multi-model ensemble points to the Mediterranean region as a hotspot of changes with significant decrements in high flows from -11% at 1.5 K up to -30% at 3 K global warming mainly resulting from reduced precipitation. Small changes (< ±10%) are observed for river basins in Central Europe and the British Isles under different levels of warming. Projected higher annual precipitation increases high flows in Scandinavia, but reduced snow melt equivalent decreases flood events in this region. Neglecting uncertainties originating from internal climate variability, downscaling technique, and hydrologic model parameters, the contribution by the GCMs to the overall uncertainties of the ensemble is in general higher than that by the HMs. The latter, however, have a substantial share in the Mediterranean and Scandinavia. Adaptation measures for limiting the impacts of global warming could be similar under 1.5 K and 2 K global warming, but have to account for significantly higher changes under 3 K global warming.

A dynamical characterization of the uncertainty in projections of regional precipitation change in the semi-arid tropics

NASA Astrophysics Data System (ADS)

Giannini, A.

2016-12-01

The uncertainty in CMIP multi-model ensembles of regional precipitation change in tropical regions is well known: taken at face value, models do not agree on the direction of precipitation change. Consequently, in adaptation discourse, either projections are discounted, e.g., by giving more relevance to temperature projections, or outcomes are grossly misrepresented, e.g., in extrapolating recent drought into the long-term future. That this is an unsatisfactory state of affairs, given the dominant role of precipitation in shaping climate-sensitive human endeavors in the tropics, is an understatement.Here I will provide a dynamical characterization of the uncertainty in regional precipitation projections that exploits the CMIP multi-model ensembles. This characterization is based on decomposing the moisture budget and relating its terms to the influence of the oceans, specifically to the roles of moisture supply and stabilization of the vertical profile. I will discuss some preliminary findings highlighting the relevance of lessons learned from seasonal-to-interannual prediction. One such lesson is to go beyond the projection taken at face value, and understand physical processes, specifically, the role of the oceans, in order to be able to make qualitative arguments, in addition to quantitative predictions. One other lesson is to abandon the search for the "best model" and exploit the multi-model ensemble to characterize "emergent constraints".

An Observationally-Based Evaluation of Cloud Ice and Liquid Water in CMIP3 and CMIP5 GCMs and Contemporary Reanalyses Using Contemporary Satellite Data (Invited)

NASA Astrophysics Data System (ADS)

Li, J. F.; Waliser, D. E.; Chen, W.; Deng, M.; Lebsock, M. D.; Stephens, G. L.; Guan, B.; Christensen, M.; Teixeira, J.

2013-12-01

Representing clouds and cloud climate feedbacks in global climate models (GCMs) remains a pressing challenge to reduce and quantify uncertainties associated with climate change projection. Vertical structures of clouds simulated by present-day models have not been extensively examined using vertically-resolved cloud hydrometers such as cloud ice water (CIW) content and cloud liquid water (CLW) content. The gap in available observations for cloud mass was clearly evident from the wide disparity in the CIW path [Waliser et al., 2009] and CLW path [Li et al., 2008;2011] values exhibited in the CMIP3 GCMs. We present an observationally-based evaluation of the CIW and CLW of present-day GCMs, notably 20th century CMIP5 simulations, and compare these results to the CMIP3 and two recent reanalyses (ECMWF and MERRA). We use three different CloudSat+CALIPSO CIW products as well as three different observation CLW products, CloudSat, MODIS and AMSRE and their combined product for CLW with methods to remove the contribution from the convective core ice mass and/or precipitating cloud hydrometeors with variable sizes and falling speeds so that a robust observational estimate with uncertainty can be obtained for model evaluations. Note, considering the CloudSat's limitations of CLW retrievals due to contamination from the precipitation and from radar clutter near the surface, an alternative CLW is synergistically constructed using MODIS CLW and CloudSat CLW. The results show that for annual mean CIW path, there are factors of 2-10 in the differences between observations and models for a majority of the GCMs and for a number of regions. Based on a number of metrics, the ensemble behavior of CMIP5 has improved considerably relative to CMIP3 (~ 50%), although neither the CMIP5 ensemble mean nor any individual model performs particularly well, and there are still a number of models that exhibit very large biases despite the availability of relevant observations. For CLW, most of the CMIP3/CMIP5 annual mean CLW path values are overestimated by factors of 2-10 compared to observations globally. For the vertical structure of CIW/CLW content, significant systematic biases are found with many models biased significantly. Based on the Taylor diagram, the ensemble performance of CMIP5 CLW path simulation shows little or no improvement relative to CMIP3. The implications of these results on model representations of the earth radiation balance are discussed, along with caveats and uncertainties associated with the observational estimates, model and observation representations of the precipitating and cloudy ice components, relevant physical processes and parameterizations.

On the generation of climate model ensembles

NASA Astrophysics Data System (ADS)

Haughton, Ned; Abramowitz, Gab; Pitman, Andy; Phipps, Steven J.

2014-10-01

Climate model ensembles are used to estimate uncertainty in future projections, typically by interpreting the ensemble distribution for a particular variable probabilistically. There are, however, different ways to produce climate model ensembles that yield different results, and therefore different probabilities for a future change in a variable. Perhaps equally importantly, there are different approaches to interpreting the ensemble distribution that lead to different conclusions. Here we use a reduced-resolution climate system model to compare three common ways to generate ensembles: initial conditions perturbation, physical parameter perturbation, and structural changes. Despite these three approaches conceptually representing very different categories of uncertainty within a modelling system, when comparing simulations to observations of surface air temperature they can be very difficult to separate. Using the twentieth century CMIP5 ensemble for comparison, we show that initial conditions ensembles, in theory representing internal variability, significantly underestimate observed variance. Structural ensembles, perhaps less surprisingly, exhibit over-dispersion in simulated variance. We argue that future climate model ensembles may need to include parameter or structural perturbation members in addition to perturbed initial conditions members to ensure that they sample uncertainty due to internal variability more completely. We note that where ensembles are over- or under-dispersive, such as for the CMIP5 ensemble, estimates of uncertainty need to be treated with care.

Future Simulated Intensification of Precipitation Extremes, CMIP5 Model Uncertainties and Dependencies

NASA Astrophysics Data System (ADS)

Bador, M.; Donat, M.; Geoffroy, O.; Alexander, L. V.

2017-12-01

Precipitation intensity during extreme events is expected to increase with climate change. Throughout the 21st century, CMIP5 climate models project a general increase in annual extreme precipitation in most regions. We investigate how robust this future increase is across different models, regions and seasons. We find that there is strong similarity in extreme precipitation changes between models that share atmospheric physics, reducing the ensemble of 27 models to 14 independent projections. We find that future simulated extreme precipitation increases in most models in the majority of land grid cells located in the dry, intermediate and wet regions according to each model's precipitation climatology. These increases significantly exceed the range of natural variability estimated from long equilibrium control runs. The intensification of extreme precipitation across the entire spectrum of dry to wet regions is particularly robust in the extra-tropics in both wet and dry season, whereas uncertainties are larger in the tropics. The CMIP5 ensemble therefore indicates robust future intensification of annual extreme rainfall in particular in extra-tropical regions. Generally, the CMIP5 robustness is higher during the dry season compared to the wet season and the annual scale, but inter-model uncertainties in the tropics remain important.

Projected Changes in Temperature and Precipitation Extremes over China as Measured by 50-yr Return Values and Periods Based on a CMIP5 Ensemble

NASA Astrophysics Data System (ADS)

Xu, Ying; Gao, Xuejie; Giorgi, Filippo; Zhou, Botao; Shi, Ying; Wu, Jie; Zhang, Yongxiang

2018-04-01

Future changes in the 50-yr return level for temperature and precipitation extremes over mainland China are investigated based on a CMIP5 multi-model ensemble for RCP2.6, RCP4.5 and RCP8.5 scenarios. The following indices are analyzed: TXx and TNn (the annual maximum and minimum of daily maximum and minimum surface temperature), RX5day (the annual maximum consecutive 5-day precipitation) and CDD (maximum annual number of consecutive dry days). After first validating the model performance, future changes in the 50-yr return values and return periods for these indices are investigated along with the inter-model spread. Multi-model median changes show an increase in the 50-yr return values of TXx and a decrease for TNn, more specifically, by the end of the 21st century under RCP8.5, the present day 50-yr return period of warm events is reduced to 1.2 yr, while extreme cold events over the country are projected to essentially disappear. A general increase in RX5day 50-yr return values is found in the future. By the end of the 21st century under RCP8.5, events of the present RX5day 50-yr return period are projected to reduce to < 10 yr over most of China. Changes in CDD-50 show a dipole pattern over China, with a decrease in the values and longer return periods in the north, and vice versa in the south. Our study also highlights the need for further improvements in the representation of extreme events in climate models to assess the future risks and engineering design related to large-scale infrastructure in China.

Annular mode changes in the CMIP5 simulations

NASA Astrophysics Data System (ADS)

Gillett, N. P.; Fyfe, J. C.

2013-03-01

We investigate simulated changes in the annular modes in historical and RCP 4.5 scenario simulations of 37 models from the fifth Coupled Model Intercomparison Project (CMIP5), a much larger ensemble of models than has previously been used to investigate annular mode trends, with improved resolution and forcings. The CMIP5 models on average simulate increases in the Northern Annular Mode (NAM) and Southern Annular Mode (SAM) in every season by 2100, and no CMIP5 model simulates a significant decrease in either the NAM or SAM in any season. No significant increase in the NAM or North Atlantic Oscillation (NAO) is simulated in response to volcanic aerosol, and no significant NAM or NAO response to solar irradiance variations is simulated. The CMIP5 models simulate a significant negative SAM response to volcanic aerosol in MAM and JJA, and a significant positive SAM response to solar irradiance variations in MAM, JJA and DJF.

Assessment of hi-resolution multi-ensemble statistical downscaling regional climate scenarios over Japan

NASA Astrophysics Data System (ADS)

Dairaku, K.

2017-12-01

The Asia-Pacific regions are increasingly threatened by large scale natural disasters. Growing concerns that loss and damages of natural disasters are projected to further exacerbate by climate change and socio-economic change. Climate information and services for risk assessments are of great concern. Fundamental regional climate information is indispensable for understanding changing climate and making decisions on when and how to act. To meet with the needs of stakeholders such as National/local governments, spatio-temporal comprehensive and consistent information is necessary and useful for decision making. Multi-model ensemble regional climate scenarios with 1km horizontal grid-spacing over Japan are developed by using CMIP5 37 GCMs (RCP8.5) and a statistical downscaling (Bias Corrected Spatial Disaggregation (BCSD)) to investigate uncertainty of projected change associated with structural differences of the GCMs for the periods of historical climate (1950-2005) and near future climate (2026-2050). Statistical downscaling regional climate scenarios show good performance for annual and seasonal averages for precipitation and temperature. The regional climate scenarios show systematic underestimate of extreme events such as hot days of over 35 Celsius and annual maximum daily precipitation because of the interpolation processes in the BCSD method. Each model projected different responses in near future climate because of structural differences. The most of CMIP5 37 models show qualitatively consistent increase of average and extreme temperature and precipitation. The added values of statistical/dynamical downscaling methods are also investigated for locally forced nonlinear phenomena, extreme events.

East Asia winter climate changes under RCP scenarios in terms of East Asian winter monsoon indices

NASA Astrophysics Data System (ADS)

Ahn, J. B.; Hong, J. Y.

2016-12-01

The changes in the winter climatology and variability of the East Asian winter monsoon (EAWM) for the late 21st century (2070-2099) under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios are projected in terms of EAWM indices (EAWMIs). Firstly, the capability of the climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulating the boreal winter climatology and the interannual variability of the EAWM for the late 20th century (1971-2000) is examined. Nine of twenty-three climate models are selected based on the pattern correlations with observation and a multi-model ensemble is applied to the nine model data. Three of twelve EAWMIs that show the most significant temporal correlations between the observation and CMIP5 surface air temperatures are utilized. The ensemble CMIP5 is capable of reproducing the overall features of the EAWM in spite of some biases in the region. The negative correlations between the EAWMIs and boreal winter temperature are well reproduced and 3-5 years of the major interannual variation observed in this region are also well simulated according to power spectral analyses of the simulated indices. The regressed fields of sea level pressure, surface air temperature, 500-hPa geopotential height, and 300-hPa zonal wind are well established with pattern correlations above 0.83 between CMIP5 and observation data. The differences between RCPs and Historical indicate strong warming, which increases with latitude, ranging from 1°C to 5°C under RCP4.5 and from 3°C to 7°C under RCP8.5 in the East Asian region. The anomalous southerly winds generally become stronger, implying weaker EAWMs in both scenarios. These features are also identified with fields regressed onto the indices in RCPs. The future projections reveal that the interannual variability of the indices will be maintained with intensity similar to that of the present. AcknowledgmentsThis work was carried out with the support of "Cooperative Research Program for Agriculture Science & Technology Development (Project No. PJ012293)" Rural Development Administration, Republic of Korea.

Changes in record-breaking temperature events in China and projections for the future

NASA Astrophysics Data System (ADS)

Deng, Hanqing; Liu, Chun; Lu, Yanyu; He, Dongyan; Tian, Hong

2017-06-01

As global warming intensifies, more record-breaking (RB) temperature events are reported in many places around the world where temperatures are higher than ever before http://cn.bing.com/dict/search?q=.&FORM=BDVSP6&mkt=zh-cn. The RB temperatures have caused severe impacts on ecosystems and human society. Here, we address changes in RB temperature events occurring over China in the past (1961-2014) as well as future projections (2006-2100) using observational data and the newly available simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The number of RB events has a significant multi-decadal variability in China, and the intensity expresses a strong decrease from 1961 to 2014. However, more frequent RB events occurred in mid-eastern and northeastern China over last 30 years (1981-2010). Comparisons with observational data indicate multi-model ensemble (MME) simulations from the CMIP5 model perform well in simulating RB events for the historical run period (1961-2005). CMIP5 MME shows a relatively larger uncertainty for the change in intensity. From 2051 to 2100, fewer RB events are projected to occur in most parts of China according to RCP 2.6 scenarios. Over the longer period from 2006 to 2100, a remarkable increase is expected for the entire country according to RCP 8.5 scenarios and the maximum numbers of RB events increase by approximately 600 per year at end of twenty-first century.

Evaluation of Historical and Projected Surface Air Temperature Simulations over China in CMIP5

NASA Astrophysics Data System (ADS)

Chen, L.; Frauenfeld, O. W.

2013-12-01

Projections of future temperature in China are crucial for assessments of climate change and implementation of appropriate adaptation and mitigation strategies. With the upcoming Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (AR5), the fifth phase of the Coupled Model Intercomparison Project (CMIP5) was developed for assessing the latest state-of-the-art climate models and their projections. In this study, monthly surface air temperature from 20 CMIP5 models and four experiments (historical, RCP 2.6, RCP 4.5, and RCP 8.5) were used to investigate the temperature variability over China during the 20th century, and future changes for the 21st century. Two observational datasets (CRU TS 3.1 and the global terrestrial air temperature dataset from the University of Delaware) were adopted to evaluate the performance of the CMIP5 multimodel ensemble average, the performance of individual models, as well as the possible improvements in CMIP5 relative to CMIP3. Results show that both CMIP3 and CMIP5 have cold biases over most parts of China. CMIP5 displays a slightly better agreement with the observations than CMIP3, but substantial cold biases still exist over the Tibetan Plateau, especially in the cold season. These biases are also characterized by the greatest discrepancies among the individual models, indicating the models' limitations over this mountainous region. Both CMIP3 and CMIP5 show poor agreement with observed 20th-century temperature trends such that the spatial and seasonal patterns of the trends are not captured in the multimodel ensemble averages. Comparing individual models we find that MPI-ESM-LR, CanESM2, MIROC-ESM, and CCSM4 exhibit better skill than the other models in this part of the world. Projections of future temperature suggest that there will be a gradual increase in annual surface air temperature in China during the 21st century at a rate of 0.60°C/decade and 0.27°C/decade under the RCP 8.5 and RCP 4.5 scenarios, respectively. RCP 2.6 shows the slowest warming at a rate of 0.10°C/decade for the whole 21st century, but temperature will increase until 2040, and then remain stable or even decrease slightly. Based on the three emission scenarios, annual temperatures are projected to rise by 1.7-5.7°C by the end of the 21st century, and the greatest warming will occur over northern China and the Tibetan Plateau.

Coupled ocean-atmosphere models feature systematic delay in Indian monsoon onset compared to their atmosphere-only component

NASA Astrophysics Data System (ADS)

Turner, Andrew

2014-05-01

In this study we examine monsoon onset characteristics in 20th century historical and AMIP integrations of the CMIP5 multi-model database. We use a period of 1979-2005, common to both the AMIP and historical integrations. While all available observed boundary conditions, including sea-surface temperature (SST), are prescribed in the AMIP integrations, the historical integrations feature ocean-atmosphere models that generate SSTs via air-sea coupled processes. The onset of Indian monsoon rainfall is shown to be systematically earlier in the AMIP integrations when comparing groups of models that provide both experiments, and in the multi-model ensemble means for each experiment in turn. We also test some common circulation indices of the monsoon onset including the horizontal shear in the lower troposphere and wind kinetic energy. Since AMIP integrations are forced by observed SSTs and CMIP5 models are known to have large cold SST biases in the northern Arabian Sea during winter and spring that limits their monsoon rainfall, we relate the delayed onset in the coupled historical integrations to cold Arabian Sea SST biases. This study provides further motivation for solving cold SST biases in the Arabian Sea in coupled models.

Interannual and low-frequency variability of Upper Indus Basin winter/spring precipitation in observations and CMIP5 models

NASA Astrophysics Data System (ADS)

Greene, Arthur M.; Robertson, Andrew W.

2017-12-01

An assessment is made of the ability of general circulation models in the CMIP5 ensemble to reproduce observed modes of low-frequency winter/spring precipitation variability in the region of the Upper Indus basin (UIB) in south-central Asia. This season accounts for about two thirds of annual precipitation totals in the UIB and is characterized by "western disturbances" propagating along the eastward extension of the Mediterranean storm track. Observational data are utilized for for spatiotemporal characterization of the precipitation seasonal cycle, to compute seasonalized spectra and finally, to examine teleconnections, in terms of large-scale patterns in sea-surface temperature (SST) and atmospheric circulation. Annual and lowpassed variations are found to be associated primarily with SST modes in the tropical and extratropical Pacific. A more obscure link to North Atlantic SST, possibly related to the North Atlantic Oscillation, is also noted. An ensemble of 31 CMIP5 models is then similarly assessed, using unforced preindustrial multi-century control runs. Of these models, eight are found to reproduce well the two leading modes of the observed seasonal cycle. This model subset is then assessed in the spectral domain and with respect to teleconnection patterns, where a range of behaviors is noted. Two model families each account for three members of this subset. The degree of within-family similarity in behavior is shown to reflect underlying model differences. The results provide estimates of unforced regional hydroclimate variability over the UIB on interannual and decadal scales and the corresponding far-field influences, and are of potential relevance for the estimation of uncertainties in future water availability.

Creation of Synthetic Surface Temperature and Precipitation Ensembles Through A Computationally Efficient, Mixed Method Approach

NASA Astrophysics Data System (ADS)

Hartin, C.; Lynch, C.; Kravitz, B.; Link, R. P.; Bond-Lamberty, B. P.

2017-12-01

Typically, uncertainty quantification of internal variability relies on large ensembles of climate model runs under multiple forcing scenarios or perturbations in a parameter space. Computationally efficient, standard pattern scaling techniques only generate one realization and do not capture the complicated dynamics of the climate system (i.e., stochastic variations with a frequency-domain structure). In this study, we generate large ensembles of climate data with spatially and temporally coherent variability across a subselection of Coupled Model Intercomparison Project Phase 5 (CMIP5) models. First, for each CMIP5 model we apply a pattern emulation approach to derive the model response to external forcing. We take all the spatial and temporal variability that isn't explained by the emulator and decompose it into non-physically based structures through use of empirical orthogonal functions (EOFs). Then, we perform a Fourier decomposition of the EOF projection coefficients to capture the input fields' temporal autocorrelation so that our new emulated patterns reproduce the proper timescales of climate response and "memory" in the climate system. Through this 3-step process, we derive computationally efficient climate projections consistent with CMIP5 model trends and modes of variability, which address a number of deficiencies inherent in the ability of pattern scaling to reproduce complex climate model behavior.

Significance of model credibility in estimating climate projection distributions for regional hydroclimatological risk assessments

USGS Publications Warehouse

Brekke, L.D.; Dettinger, M.D.; Maurer, E.P.; Anderson, M.

2008-01-01

Ensembles of historical climate simulations and climate projections from the World Climate Research Programme's (WCRP's) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset were investigated to determine how model credibility affects apparent relative scenario likelihoods in regional risk assessments. Methods were developed and applied in a Northern California case study. An ensemble of 59 twentieth century climate simulations from 17 WCRP CMIP3 models was analyzed to evaluate relative model credibility associated with a 75-member projection ensemble from the same 17 models. Credibility was assessed based on how models realistically reproduced selected statistics of historical climate relevant to California climatology. Metrics of this credibility were used to derive relative model weights leading to weight-threshold culling of models contributing to the projection ensemble. Density functions were then estimated for two projected quantities (temperature and precipitation), with and without considering credibility-based ensemble reductions. An analysis for Northern California showed that, while some models seem more capable at recreating limited aspects twentieth century climate, the overall tendency is for comparable model performance when several credibility measures are combined. Use of these metrics to decide which models to include in density function development led to local adjustments to function shapes, but led to limited affect on breadth and central tendency, which were found to be more influenced by 'completeness' of the original ensemble in terms of models and emissions pathways. ?? 2007 Springer Science+Business Media B.V.

Time of emergence in regional precipitation changes: an updated assessment using the CMIP5 multi-model ensemble

NASA Astrophysics Data System (ADS)

Nguyen, Thuy-Huong; Min, Seung-Ki; Paik, Seungmok; Lee, Donghyun

2018-01-01

This study conducted an updated time of emergence (ToE) analysis of regional precipitation changes over land regions across the globe using multiple climate models from the Coupled Model Intercomparison Project phase 5 (CMIP5). ToEs were estimated for 14 selected hotspots over two seasons of April to September (AS) and October to March (OM) from three RCP scenarios representing low (RCP2.6), medium (RCP4.5), and high (RCP8.5) emissions. Results from the RCP8.5 scenario indicate that ToEs would occur before 2040 over seven hotspots including three northern high-latitude regions (OM wettening), East Africa (OM wettening), South Asia (AS wettening), East Asia (AS wettening) and South Africa (AS drying). The Mediterranean (both OM and AS drying) is expected to experience ToEs in the mid-twenty-first century (2040-2080). In order to measure possible benefits from taking low-emission scenarios, ToE differences were examined between the RCP2.6 scenario and the RCP4.5 and RCP8.5 scenarios. Significant ToE delays from 26 years to longer than 67 years were identified over East Africa (OM wettening), the Mediterranean (both AS and OM drying), South Asia (AS wettening), and South Africa (AS drying). Further, we investigated ToE differences between CMIP3-based and CMIP5-based models using the same number of models for the comparable scenario pairs (SRESA2 vs. RCP8.5, and SRESB1 vs. RCP4.5). Results were largely consistent between two model groups, indicating the robustness of ToE results. Considerable differences in ToEs (larger than 20 years) between two model groups appeared over East Asia and South Asia (AS wettening) and South Africa (AS drying), which were found due to stronger signals in CMIP5 models. Our results provide useful information on the timing of emerging signals in regional and seasonal hydrological changes, having important implications for associated adaptation and mitigation plans.

Assessment of CMIP5 historical simulations of rainfall over Southeast Asia

NASA Astrophysics Data System (ADS)

Raghavan, Srivatsan V.; Liu, Jiandong; Nguyen, Ngoc Son; Vu, Minh Tue; Liong, Shie-Yui

2018-05-01

We present preliminary analyses of the historical (1986-2005) climate simulations of a ten-member subset of the Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models over Southeast Asia. The objective of this study was to evaluate the general circulation models' performance in simulating the mean state of climate over this less-studied climate vulnerable region, with a focus on precipitation. Results indicate that most of the models are unable to reproduce the observed state of climate over Southeast Asia. Though the multi-model ensemble mean is a better representation of the observations, the uncertainties in the individual models are far high. There is no particular model that performed well in simulating the historical climate of Southeast Asia. There seems to be no significant influence of the spatial resolutions of the models on the quality of simulation, despite the view that higher resolution models fare better. The study results emphasize on careful consideration of models for impact studies and the need to improve the next generation of models in their ability to simulate regional climates better.

Making decisions based on an imperfect ensemble of climate simulators: strategies and future directions

NASA Astrophysics Data System (ADS)

Sanderson, B. M.

2017-12-01

The CMIP ensembles represent the most comprehensive source of information available to decision-makers for climate adaptation, yet it is clear that there are fundamental limitations in our ability to treat the ensemble as an unbiased sample of possible future climate trajectories. There is considerable evidence that models are not independent, and increasing complexity and resolution combined with computational constraints prevent a thorough exploration of parametric uncertainty or internal variability. Although more data than ever is available for calibration, the optimization of each model is influenced by institutional priorities, historical precedent and available resources. The resulting ensemble thus represents a miscellany of climate simulators which defy traditional statistical interpretation. Models are in some cases interdependent, but are sufficiently complex that the degree of interdependency is conditional on the application. Configurations have been updated using available observations to some degree, but not in a consistent or easily identifiable fashion. This means that the ensemble cannot be viewed as a true posterior distribution updated by available data, but nor can observational data alone be used to assess individual model likelihood. We assess recent literature for combining projections from an imperfect ensemble of climate simulators. Beginning with our published methodology for addressing model interdependency and skill in the weighting scheme for the 4th US National Climate Assessment, we consider strategies for incorporating process-based constraints on future response, perturbed parameter experiments and multi-model output into an integrated framework. We focus on a number of guiding questions: Is the traditional framework of confidence in projections inferred from model agreement leading to biased or misleading conclusions? Can the benefits of upweighting skillful models be reconciled with the increased risk of truth lying outside the weighted ensemble distribution? If CMIP is an ensemble of partially informed best-guesses, can we infer anything about the parent distribution of all possible models of the climate system (and if not, are we implicitly under-representing the risk of a climate catastrophe outside of the envelope of CMIP simulations)?

Upgrades to the REA method for producing probabilistic climate change projections

NASA Astrophysics Data System (ADS)

Xu, Ying; Gao, Xuejie; Giorgi, Filippo

2010-05-01

We present an augmented version of the Reliability Ensemble Averaging (REA) method designed to generate probabilistic climate change information from ensembles of climate model simulations. Compared to the original version, the augmented one includes consideration of multiple variables and statistics in the calculation of the performance-based weights. In addition, the model convergence criterion previously employed is removed. The method is applied to the calculation of changes in mean and variability for temperature and precipitation over different sub-regions of East Asia based on the recently completed CMIP3 multi-model ensemble. Comparison of the new and old REA methods, along with the simple averaging procedure, and the use of different combinations of performance metrics shows that at fine sub-regional scales the choice of weighting is relevant. This is mostly because the models show a substantial spread in performance for the simulation of precipitation statistics, a result that supports the use of model weighting as a useful option to account for wide ranges of quality of models. The REA method, and in particular the upgraded one, provides a simple and flexible framework for assessing the uncertainty related to the aggregation of results from ensembles of models in order to produce climate change information at the regional scale. KEY WORDS: REA method, Climate change, CMIP3

CMIP5 ensemble-based spatial rainfall projection over homogeneous zones of India

NASA Astrophysics Data System (ADS)

Akhter, Javed; Das, Lalu; Deb, Argha

2017-09-01

Performances of the state-of-the-art CMIP5 models in reproducing the spatial rainfall patterns over seven homogeneous rainfall zones of India viz. North Mountainous India (NMI), Northwest India (NWI), North Central India (NCI), Northeast India (NEI), West Peninsular India (WPI), East Peninsular India (EPI) and South Peninsular India (SPI) have been assessed using different conventional performance metrics namely spatial correlation (R), index of agreement (d-index), Nash-Sutcliffe efficiency (NSE), Ratio of RMSE to the standard deviation of the observations (RSR) and mean bias (MB). The results based on these indices revealed that majority of the models are unable to reproduce finer-scaled spatial patterns over most of the zones. Thereafter, four bias correction methods i.e. Scaling, Standardized Reconstruction, Empirical Quantile Mapping and Gamma Quantile Mapping have been applied on GCM simulations to enhance the skills of the GCM projections. It has been found that scaling method compared to other three methods shown its better skill in capturing mean spatial patterns. Multi-model ensemble (MME) comprising 25 numbers of better performing bias corrected (Scaled) GCMs, have been considered for developing future rainfall patterns over seven zones. Models' spread from ensemble mean (uncertainty) has been found to be larger in RCP 8.5 than RCP4.5 ensemble. In general, future rainfall projections from RCP 4.5 and RCP 8.5 revealed an increasing rainfall over seven zones during 2020s, 2050s, and 2080s. The maximum increase has been found over southwestern part of NWI (12-30%), northwestern part of WPI (3-30%), southeastern part of NEI (5-18%) and northern and eastern part of SPI (6-24%). However, the contiguous region comprising by the southeastern part of NCI and northeastern part of EPI, may experience slight decreasing rainfall (about 3%) during 2020s whereas the western part of NMI may also receive around 3% reduction in rainfall during both 2050s and 2080s.

Assessment of mid-latitude atmospheric variability in CMIP5 models using a process oriented-metric

NASA Astrophysics Data System (ADS)

Di Biagio, Valeria; Calmanti, Sandro; Dell'Aquila, Alessandro; Ruti, Paolo

2013-04-01

We compare, for the period 1962-2000, an estimate of the northern hemisphere mid-latitude winter atmospheric variability according several global climate models included in the fifth phase of the Climate Model Intercomparison Project (CMIP5) with the results of the models belonging to the previous CMIP3 and with the NCEP-NCAR reanalysis. We use the space-time Hayashi spectra of the 500hPa geopotential height fields to characterize the variability of atmospheric circulation regimes and we introduce an ad hoc integral measure of the variability observed in the Northern Hemisphere on different spectral sub-domains. The overall performance of each model is evaluated by considering the total wave variability as a global scalar measure of the statistical properties of different types of atmospheric disturbances. The variability associated to eastward propagating baroclinic waves and to planetary waves is instead used to describe the performance of each model in terms of specific physical processes. We find that the two model ensembles (CMIP3 and CMIP5) do not show substantial differences in the description of northern hemisphere winter mid-latitude atmospheric variability, although some CMIP5 models display performances superior to their previous versions implemented in CMIP3. Preliminary results for the 21th century RCP 4.5 scenario will be also discussed for the CMIP5 models.

Earth's energy imbalance since 1960 in observations and CMIP5 models: Earth's energy imbalance since 1960

DOE PAGES

Smith, Doug M.; Allan, Richard P.; Coward, Andrew C.; ...

2015-02-19

Observational analyses of running 5 year ocean heat content trends (Ht) and net downward top of atmosphere radiation (N) are significantly correlated (r ~ 0.6) from 1960 to 1999, but a spike in Ht in the early 2000s is likely spurious since it is inconsistent with estimates of N from both satellite observations and climate model simulations. Variations in N between 1960 and 2000 were dominated by volcanic eruptions and are well simulated by the ensemble mean of coupled models from the Fifth Coupled Model Intercomparison Project (CMIP5). Here, we find an observation-based reduction in N of -0.31 ± 0.21more » W m -2 between 1999 and 2005 that potentially contributed to the recent warming slowdown, but the relative roles of external forcing and internal variability remain unclear. Finally, while present-day anomalies of N in the CMIP5 ensemble mean and observations agree, this may be due to a cancelation of errors in outgoing longwave and absorbed solar radiation.« less

The two types of ENSO in CMIP5 models

NASA Astrophysics Data System (ADS)

Kim, Seon Tae; Yu, Jin-Yi

2012-06-01

In this study, we evaluate the intensity of the Central-Pacific (CP) and Eastern-Pacific (EP) types of El Niño-Southern Oscillation (ENSO) simulated in the pre-industrial, historical, and the Representative Concentration Pathways (RCP) 4.5 experiments of the Coupled Model Intercomparison Project Phase 5 (CMIP5). Compared to the CMIP3 models, the pre-industrial simulations of the CMIP5 models are found to (1) better simulate the observed spatial patterns of the two types of ENSO and (2) have a significantly smaller inter-model diversity in ENSO intensities. The decrease in the CMIP5 model discrepancies is particularly obvious in the simulation of the EP ENSO intensity, although it is still more difficult for the models to reproduce the observed EP ENSO intensity than the observed CP ENSO intensity. Ensemble means of the CMIP5 models indicate that the intensity of the CP ENSO increases steadily from the pre-industrial to the historical and the RCP4.5 simulations, but the intensity of the EP ENSO increases from the pre-industrial to the historical simulations and then decreases in the RCP4.5 projections. The CP-to-EP ENSO intensity ratio, as a result, is almost the same in the pre-industrial and historical simulations but increases in the RCP4.5 simulation.

Model dependence and its effect on ensemble projections in CMIP5

NASA Astrophysics Data System (ADS)

Abramowitz, G.; Bishop, C.

2013-12-01

Conceptually, the notion of model dependence within climate model ensembles is relatively simple - modelling groups share a literature base, parametrisations, data sets and even model code - the potential for dependence in sampling different climate futures is clear. How though can this conceptual problem inform a practical solution that demonstrably improves the ensemble mean and ensemble variance as an estimate of system uncertainty? While some research has already focused on error correlation or error covariance as a candidate to improve ensemble mean estimates, a complete definition of independence must at least implicitly subscribe to an ensemble interpretation paradigm, such as the 'truth-plus-error', 'indistinguishable', or more recently 'replicate Earth' paradigm. Using a definition of model dependence based on error covariance within the replicate Earth paradigm, this presentation will show that accounting for dependence in surface air temperature gives cooler projections in CMIP5 - by as much as 20% globally in some RCPs - although results differ significantly for each RCP, especially regionally. The fact that the change afforded by accounting for dependence across different RCPs is different is not an inconsistent result. Different numbers of submissions to each RCP by different modelling groups mean that differences in projections from different RCPs are not entirely about RCP forcing conditions - they also reflect different sampling strategies.

Analysis of the variability of the North Atlantic eddy-driven jet stream in CMIP5

NASA Astrophysics Data System (ADS)

Iqbal, Waheed; Leung, Wai-Nang; Hannachi, Abdel

2017-09-01

The North Atlantic eddy-driven jet is a dominant feature of extratropical climate and its variability is associated with the large-scale changes in the surface climate of midlatitudes. Variability of this jet is analysed in a set of General Circulation Models (GCMs) from the Coupled Model Inter-comparison Project phase-5 (CMIP5) over the North Atlantic region. The CMIP5 simulations for the 20th century climate (Historical) are compared with the ERA40 reanalysis data. The jet latitude index, wind speed and jet persistence are analysed in order to evaluate 11 CMIP5 GCMs and to compare them with those from CMIP3 integrations. The phase of mean seasonal cycle of jet latitude and wind speed from historical runs of CMIP5 GCMs are comparable to ERA40. The wind speed mean seasonal cycle by CMIP5 GCMs is overestimated in winter months. A positive (negative) jet latitude anomaly in historical simulations relative to ERA40 is observed in summer (winter). The ensemble mean of jet latitude biases in historical simulations of CMIP3 and CMIP5 with respect to ERA40 are -2.43° and -1.79° respectively. Thus indicating improvements in CMIP5 in comparison to the CMIP3 GCMs. The comparison of historical and future simulations of CMIP5 under RCP4.5 and RCP8.5 for the period 2076-2099, shows positive anomalies in the jet latitude implying a poleward shifted jet. The results from the analysed models offer no specific improvements in simulating the trimodality of the eddy-driven jet.

Understanding the joint behavior of temperature and precipitation for climate change impact studies

NASA Astrophysics Data System (ADS)

Rana, Arun; Moradkhani, Hamid; Qin, Yueyue

2017-07-01

The multiple downscaled scenario products allow us to assess the uncertainty of the variations of precipitation and temperature in the current and future periods. Probabilistic assessments of both climatic variables help better understand the interdependence of the two and thus, in turn, help in assessing the future with confidence. In the present study, we use ensemble of statistically downscaled precipitation and temperature from various models. The dataset used is multi-model ensemble of 10 global climate models (GCMs) downscaled product from CMIP5 daily dataset using the Bias Correction and Spatial Downscaling (BCSD) technique, generated at Portland State University. The multi-model ensemble of both precipitation and temperature is evaluated for dry and wet periods for 10 sub-basins across Columbia River Basin (CRB). Thereafter, copula is applied to establish the joint distribution of two variables on multi-model ensemble data. The joint distribution is then used to estimate the change in trends of said variables in future, along with estimation of the probabilities of the given change. The joint distribution trends vary, but certainly positive, for dry and wet periods in sub-basins of CRB. Dry season, generally, is indicating a higher positive change in precipitation than temperature (as compared to historical) across sub-basins with wet season inferring otherwise. Probabilities of changes in future, as estimated from the joint distribution, indicate varied degrees and forms during dry season whereas the wet season is rather constant across all the sub-basins.

Downscaled rainfall projections in south Florida using self-organizing maps.

PubMed

Sinha, Palash; Mann, Michael E; Fuentes, Jose D; Mejia, Alfonso; Ning, Liang; Sun, Weiyi; He, Tao; Obeysekera, Jayantha

2018-04-20

We make future projections of seasonal precipitation characteristics in southern Florida using a statistical downscaling approach based on Self Organized Maps. Our approach is applied separately to each three-month season: September-November; December-February; March-May; and June-August. We make use of 19 different simulations from the Coupled Model Inter-comparison Project, phase 5 (CMIP5) and generate an ensemble of 1500 independent daily precipitation surrogates for each model simulation, yielding a grand ensemble of 28,500 total realizations for each season. The center and moments (25%ile and 75%ile) of this distribution are used to characterize most likely scenarios and their associated uncertainties. This approach is applied to 30-year windows of daily mean precipitation for both the CMIP5 historical simulations (1976-2005) and the CMIP5 future (RCP 4.5) projections. For the latter case, we examine both the "near future" (2021-2050) and "far future" (2071-2100) periods for three scenarios (RCP2.6, RCP4.5, and RCP8.5). Copyright © 2018 Elsevier B.V. All rights reserved.

Using Paleo-climate Comparisons to Constrain Future Projections in CMIP5

NASA Technical Reports Server (NTRS)

Schmidt, G. A.; Annan, J D.; Bartlein, P. J.; Cook, B. I.; Guilyardi, E.; Hargreaves, J. C.; Harrison, S. P.; Kageyama, M.; LeGrande, A. N..; Konecky, B.;

Systematic land climate and evapotranspiration biases in CMIP5 simulations.

PubMed

Mueller, B; Seneviratne, S I

2014-01-16

[1] Land climate is important for human population since it affects inhabited areas. Here we evaluate the realism of simulated evapotranspiration (ET), precipitation, and temperature in the CMIP5 multimodel ensemble on continental areas. For ET, a newly compiled synthesis data set prepared within the Global Energy and Water Cycle Experiment-sponsored LandFlux-EVAL project is used. The results reveal systematic ET biases in the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulations, with an overestimation in most regions, especially in Europe, Africa, China, Australia, Western North America, and part of the Amazon region. The global average overestimation amounts to 0.17 mm/d. This bias is more pronounced than in the previous CMIP3 ensemble (overestimation of 0.09 mm/d). Consistent with the ET overestimation, precipitation is also overestimated relative to existing reference data sets. We suggest that the identified biases in ET can explain respective systematic biases in temperature in many of the considered regions. The biases additionally display a seasonal dependence and are generally of opposite sign (ET underestimation and temperature overestimation) in boreal summer (June-August).

Role of Internal Variability in Surface Temperature and Precipitation Change Uncertainties over India.

NASA Astrophysics Data System (ADS)

Achutarao, K. M.; Singh, R.

2017-12-01

There are various sources of uncertainty in model projections of future climate change. These include differences in the formulation of climate models, internal variability, and differences in scenarios. Internal variability in a climate system represents the unforced change due to the chaotic nature of the climate system and is considered irreducible (Deser et al., 2012). Internal variability becomes important at regional scales where it can dominate forced changes. Therefore it needs to be carefully assessed in future projections. In this study we segregate the role of internal variability in the future temperature and precipitation projections over the Indian region. We make use of the Coupled Model Inter-comparison Project - phase 5 (CMIP5; Taylor et al., 2012) database containing climate model simulations carried out by various modeling centers around the world. While the CMIP5 experimental protocol recommended producing numerous ensemble members, only a handful of the modeling groups provided multiple realizations. Having a small number of realizations is a limitation in producing a quantification of internal variability. We therefore exploit the Community Earth System Model Large Ensemble (CESM-LE; Kay et al., 2014) dataset which contains a 40 member ensemble of a single model- CESM1 (CAM5) to explore the role of internal variability in Future Projections. Surface air temperature and precipitation change projections over regional and sub-regional scale are analyzed under the IPCC emission scenario (RCP8.5) for different seasons and homogeneous climatic zones over India. We analyze the spread in projections due to internal variability in the CESM-LE and CMIP5 datasets over these regions.

Development of probabilistic regional climate scenario in East Asia

NASA Astrophysics Data System (ADS)

Dairaku, K.; Ueno, G.; Ishizaki, N. N.

2015-12-01

Climate information and services for Impacts, Adaptation and Vulnerability (IAV) Assessments are of great concern. In order to develop probabilistic regional climate information that represents the uncertainty in climate scenario experiments in East Asia (CORDEX-EA and Japan), the probability distribution of 2m air temperature was estimated by using developed regression model. The method can be easily applicable to other regions and other physical quantities, and also to downscale to finer-scale dependent on availability of observation dataset. Probabilistic climate information in present (1969-1998) and future (2069-2098) climate was developed using CMIP3 SRES A1b scenarios 21 models and the observation data (CRU_TS3.22 & University of Delaware in CORDEX-EA, NIAES AMeDAS mesh data in Japan). The prototype of probabilistic information in CORDEX-EA and Japan represent the quantified structural uncertainties of multi-model ensemble experiments of climate change scenarios. Appropriate combination of statistical methods and optimization of climate ensemble experiments using multi-General Circulation Models (GCMs) and multi-regional climate models (RCMs) ensemble downscaling experiments are investigated.

Response of ENSO amplitude to global warming in CESM large ensemble: uncertainty due to internal variability

NASA Astrophysics Data System (ADS)

Zheng, Xiao-Tong; Hui, Chang; Yeh, Sang-Wook

2018-06-01

El Niño-Southern Oscillation (ENSO) is the dominant mode of variability in the coupled ocean-atmospheric system. Future projections of ENSO change under global warming are highly uncertain among models. In this study, the effect of internal variability on ENSO amplitude change in future climate projections is investigated based on a 40-member ensemble from the Community Earth System Model Large Ensemble (CESM-LE) project. A large uncertainty is identified among ensemble members due to internal variability. The inter-member diversity is associated with a zonal dipole pattern of sea surface temperature (SST) change in the mean along the equator, which is similar to the second empirical orthogonal function (EOF) mode of tropical Pacific decadal variability (TPDV) in the unforced control simulation. The uncertainty in CESM-LE is comparable in magnitude to that among models of the Coupled Model Intercomparison Project phase 5 (CMIP5), suggesting the contribution of internal variability to the intermodel uncertainty in ENSO amplitude change. However, the causations between changes in ENSO amplitude and the mean state are distinct between CESM-LE and CMIP5 ensemble. The CESM-LE results indicate that a large ensemble of 15 members is needed to separate the relative contributions to ENSO amplitude change over the twenty-first century between forced response and internal variability.

Reducing the Uncertainty in Atlantic Meridional Overturning Circulation Projections Using Bayesian Model Averaging

NASA Astrophysics Data System (ADS)

Olson, R.; An, S. I.

2016-12-01

Atlantic Meridional Overturning Circulation (AMOC) in the ocean might slow down in the future, which can lead to a host of climatic effects in North Atlantic and throughout the world. Despite improvements in climate models and availability of new observations, AMOC projections remain uncertain. Here we constrain CMIP5 multi-model ensemble output with observations of a recently developed AMOC index to provide improved Bayesian predictions of future AMOC. Specifically, we first calculate yearly AMOC index loosely based on Rahmstorf et al. (2015) for years 1880—2004 for both observations, and the CMIP5 models for which relevant output is available. We then assign a weight to each model based on a Bayesian Model Averaging method that accounts for differential model skill in terms of both mean state and variability. We include the temporal autocorrelation in climate model errors, and account for the uncertainty in the parameters of our statistical model. We use the weights to provide future weighted projections of AMOC, and compare them to un-weighted ones. Our projections use bootstrapping to account for uncertainty in internal AMOC variability. We also perform spectral and other statistical analyses to show that AMOC index variability, both in models and in observations, is consistent with red noise. Our results improve on and complement previous work by using a new ensemble of climate models, a different observational metric, and an improved Bayesian weighting method that accounts for differential model skill at reproducing internal variability. Reference: Rahmstorf, S., Box, J. E., Feulner, G., Mann, M. E., Robinson, A., Rutherford, S., & Schaffernicht, E. J. (2015). Exceptional twentieth-century slowdown in atlantic ocean overturning circulation. Nature Climate Change, 5(5), 475-480. doi:10.1038/nclimate2554

"Intelligent Ensemble" Projections of Precipitation and Surface Radiation in Support of Agricultural Climate Change Adaptation

NASA Technical Reports Server (NTRS)

Taylor, Patrick C.; Baker, Noel C.

2015-01-01

Earth's climate is changing and will continue to change into the foreseeable future. Expected changes in the climatological distribution of precipitation, surface temperature, and surface solar radiation will significantly impact agriculture. Adaptation strategies are, therefore, required to reduce the agricultural impacts of climate change. Climate change projections of precipitation, surface temperature, and surface solar radiation distributions are necessary input for adaption planning studies. These projections are conventionally constructed from an ensemble of climate model simulations (e.g., the Coupled Model Intercomparison Project 5 (CMIP5)) as an equal weighted average, one model one vote. Each climate model, however, represents the array of climate-relevant physical processes with varying degrees of fidelity influencing the projection of individual climate variables differently. Presented here is a new approach, termed the "Intelligent Ensemble, that constructs climate variable projections by weighting each model according to its ability to represent key physical processes, e.g., precipitation probability distribution. This approach provides added value over the equal weighted average method. Physical process metrics applied in the "Intelligent Ensemble" method are created using a combination of NASA and NOAA satellite and surface-based cloud, radiation, temperature, and precipitation data sets. The "Intelligent Ensemble" method is applied to the RCP4.5 and RCP8.5 anthropogenic climate forcing simulations within the CMIP5 archive to develop a set of climate change scenarios for precipitation, temperature, and surface solar radiation in each USDA Farm Resource Region for use in climate change adaptation studies.

Evaluation of CMIP5 Ability to Reproduce 20th Century Regional Trends in Surface Air Temperature and Precipitation over CONUS

NASA Astrophysics Data System (ADS)

Lee, J.; Waliser, D. E.; Lee, H.; Loikith, P. C.; Kunkel, K.

2017-12-01

Monitoring temporal changes in key climate variables, such as surface air temperature and precipitation, is an integral part of the ongoing efforts of the United States National Climate Assessment (NCA). Climate models participating in CMIP5 provide future trends for four different emissions scenarios. In order to have confidence in the future projections of surface air temperature and precipitation, it is crucial to evaluate the ability of CMIP5 models to reproduce observed trends for three different time periods (1895-1939, 1940-1979, and 1980-2005). Towards this goal, trends in surface air temperature and precipitation obtained from the NOAA nClimGrid 5 km gridded station observation-based product are compared during all three time periods to the 206 CMIP5 historical simulations from 48 unique GCMs and their multi-model ensemble (MME) for NCA-defined climate regions during summer (JJA) and winter (DJF). This evaluation quantitatively examines the biases of simulated trends of the spatially averaged temperature and precipitation in the NCA climate regions. The CMIP5 MME reproduces historical surface air temperature trends for JJA for all time period and all regions, except the Northern Great Plains from 1895-1939 and Southeast during 1980-2005. Likewise, for DJF, the MME reproduces historical surface air temperature trends across all time periods over all regions except the Southeast from 1895-1939 and the Midwest during 1940-1979. The Regional Climate Model Evaluation System (RCMES), an analysis tool which supports the NCA by providing access to data and tools for regional climate model validation, facilitates the comparisons between the models and observation. The RCMES Toolkit is designed to assist in the analysis of climate variables and the procedure of the evaluation of climate projection models to support the decision-making processes. This tool is used in conjunction with the above analysis and results will be presented to demonstrate its capability to access observation and model datasets, calculate evaluation metrics, and visualize the results. Several other examples of the RCMES capabilities can be found at https://rcmes.jpl.nasa.gov.

Comparing CMIP-3 and CMIP-5 climate projections on flooding estimation of Devils Lake of North Dakota, USA

PubMed Central

2018-01-01

Background Water level fluctuations in endorheic lakes are highly susceptible to even slight changes in climate and land use. Devils Lake (DL) in North Dakota, USA is an endorheic system that has undergone multi-decade flooding driven by changes in regional climate. Flooding mitigation strategies have centered on the release of lake water to a nearby river system through artificial outlets, resulting in legal challenges and environmental concerns related to water quality, downstream flooding, species migration, stakeholder opposition, and transboundary water conflicts between the US and Canada. Despite these drawbacks, running outlets would result in low overspill risks in the next 30 years. Methods In this study we evaluated the efficacy of this outlet-based mitigation strategy under scenarios based on the latest IPCC future climate projections. We used the Coupled Model Intercomparison Project CMIP-5 weather patterns from 17 general circulation models (GCMs) obtained under four representative concentration pathways (RCP) scenarios and downscaled to the DL region. Then, we simulated the changes in lake water levels using the soil and water assessment tool based hydrological model of the watershed. We estimated the probability of future flood risks under those scenarios and compared those with previously estimated overspill risks under the CMIP-3 climate. Results The CMIP-5 ensemble projected a mean annual temperature of 5.78 °C and mean daily precipitation of 1.42 mm/day; both are higher than the existing CMIP-3 future estimates of 4.98 °C and 1.40 mm/day, respectively. The increased precipitation and higher temperature resulted in a significant increase of DL’s overspill risks: 24.4–47.1% without release from outlets and 3.5–14.4% even if the outlets are operated at their combined full 17 m3/s capacity. Discussion The modeled increases in overspill risks indicate a greater frequency of water releases through the artificial outlets. Future risk mitigation management should include providing a flood warning signal to local resource managers, and tasking policy makers to identify additional solution measures such as land use management in the upper watershed to mitigate DL’s flooding. PMID:29736343

Comparing CMIP-3 and CMIP-5 climate projections on flooding estimation of Devils Lake of North Dakota, USA.

PubMed

Kharel, Gehendra; Kirilenko, Andrei

2018-01-01

Water level fluctuations in endorheic lakes are highly susceptible to even slight changes in climate and land use. Devils Lake (DL) in North Dakota, USA is an endorheic system that has undergone multi-decade flooding driven by changes in regional climate. Flooding mitigation strategies have centered on the release of lake water to a nearby river system through artificial outlets, resulting in legal challenges and environmental concerns related to water quality, downstream flooding, species migration, stakeholder opposition, and transboundary water conflicts between the US and Canada. Despite these drawbacks, running outlets would result in low overspill risks in the next 30 years. In this study we evaluated the efficacy of this outlet-based mitigation strategy under scenarios based on the latest IPCC future climate projections. We used the Coupled Model Intercomparison Project CMIP-5 weather patterns from 17 general circulation models (GCMs) obtained under four representative concentration pathways (RCP) scenarios and downscaled to the DL region. Then, we simulated the changes in lake water levels using the soil and water assessment tool based hydrological model of the watershed. We estimated the probability of future flood risks under those scenarios and compared those with previously estimated overspill risks under the CMIP-3 climate. The CMIP-5 ensemble projected a mean annual temperature of 5.78 °C and mean daily precipitation of 1.42 mm/day; both are higher than the existing CMIP-3 future estimates of 4.98 °C and 1.40 mm/day, respectively. The increased precipitation and higher temperature resulted in a significant increase of DL's overspill risks: 24.4-47.1% without release from outlets and 3.5-14.4% even if the outlets are operated at their combined full 17 m 3 /s capacity. The modeled increases in overspill risks indicate a greater frequency of water releases through the artificial outlets. Future risk mitigation management should include providing a flood warning signal to local resource managers, and tasking policy makers to identify additional solution measures such as land use management in the upper watershed to mitigate DL's flooding.

Skill and independence weighting for multi-model assessments

DOE PAGES

Sanderson, Benjamin M.; Wehner, Michael; Knutti, Reto

2017-06-28

We present a weighting strategy for use with the CMIP5 multi-model archive in the fourth National Climate Assessment, which considers both skill in the climatological performance of models over North America as well as the inter-dependency of models arising from common parameterizations or tuning practices. The method exploits information relating to the climatological mean state of a number of projection-relevant variables as well as metrics representing long-term statistics of weather extremes. The weights, once computed can be used to simply compute weighted means and significance information from an ensemble containing multiple initial condition members from potentially co-dependent models of varyingmore » skill. Two parameters in the algorithm determine the degree to which model climatological skill and model uniqueness are rewarded; these parameters are explored and final values are defended for the assessment. The influence of model weighting on projected temperature and precipitation changes is found to be moderate, partly due to a compensating effect between model skill and uniqueness. However, more aggressive skill weighting and weighting by targeted metrics is found to have a more significant effect on inferred ensemble confidence in future patterns of change for a given projection.« less

Patterns of tropical Pacific convection anomalies and associated extratropical wave trains in AMIP5

NASA Astrophysics Data System (ADS)

Ding, Shuoyi; Chen, Wen; Graf, Hans-F.; Guo, Yuanyuan

2018-05-01

In this paper, the performance of 18 Coupled Model Intercomparison Project Phase 5 (CMIP5) models forced by observational SSTs in simulating the tropical Pacific convective variation and the atmospheric responses in the extratropics are assessed. The multi-model ensemble mean results of 18 CMIP5 models show that five major patterns of tropical Pacific convection anomaly in winter can indeed be well reproduced, however, the simulation of the corresponding extratropical responses for each pattern exists some deficiency except for the La Niña pattern compared with observations. We defined an optimized subset of well performing models (ACCESS1.0, CanAM4, CCSM4, CMCC-CM, HadGEM2-A, MPI-ESM-MR) in tropical Pacific deep convection according to the ranking of model skill score. These models exhibit approximately identical convection anomaly patterns in both amplitude and spatial structure to the observation, which potentially might improve the representation of extratropical teleconnections with the tropical Pacific, especially for the CP El Niño (CPEN), EP El Niño (EPEN) and western CP (W-CP) patterns. Both evident atmospheric anomalies of CPEN and EPEN patterns over the NA/E sector and the northeastward propagating wave trains of W-CP pattern can be quite well simulated in the high-skilled models.

The role of historical forcings in simulating the observed Atlantic multidecadal oscillation

NASA Astrophysics Data System (ADS)

Murphy, Lisa N.; Bellomo, Katinka; Cane, Mark; Clement, Amy

2017-03-01

We analyze the Atlantic multidecadal oscillation (AMO) in the preindustrial (PI) and historical (HIST) simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to assess the drivers of the observed AMO from 1865 to 2005. We draw 141 year samples from the 41 CMIP5 model's PI runs and compare the correlation and variance between the observed AMO and the simulated PI and HIST AMO. The correlation coefficients in 38 forced (HIST) models are above the 90% confidence level and explain up to 56% of the observed variance. The probability that any of the unforced (PI) models do as well is less than 3% in 31 models. Multidecadal variability is larger in 39 CMIP5 HIST simulations and in all HIST members of the Community Earth System Model Large Ensemble than their corresponding PI. We conclude that there is an essential role for external forcing in driving the observed AMO.

ENSO-related Interannual Variability of Southern Hemisphere Atmospheric Circulation: Assessment and Projected Changes in CMIP5 Models

NASA Astrophysics Data System (ADS)

Frederiksen, Carsten; Grainger, Simon; Zheng, Xiaogu; Sisson, Janice

2013-04-01

ENSO variability is an important driver of the Southern Hemisphere (SH) atmospheric circulation. Understanding the observed and projected changes in ENSO variability is therefore important to understanding changes in Australian surface climate. Using a recently developed methodology (Zheng et al., 2009), the coherent patterns, or modes, of ENSO-related variability in the SH atmospheric circulation can be separated from modes that are related to intraseasonal variability or to changes in radiative forcings. Under this methodology, the seasonal mean SH 500 hPa geopotential height is considered to consist of three components. These are: (1) an intraseasonal component related to internal dynamics on intraseasonal time scales; (2) a slow-internal component related to internal dynamics on slowly varying (interannual or longer) time scales, including ENSO; and (3) a slow-external component related to external (i.e. radiative) forcings. Empirical Orthogonal Functions (EOFs) are used to represent the modes of variability of the interannual covariance of the three components. An assessment is first made of the modes in models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) dataset for the SH summer and winter seasons in the 20th century. In reanalysis data, two EOFs of the slow component (which includes the slow-internal and slow-external components) have been found to be related to ENSO variability (Frederiksen and Zheng, 2007). In SH summer, the CMIP5 models reproduce the leading ENSO mode very well when the structures of the EOF and the associated SST, and associated variance are considered. There is substantial improvement in this mode when compared with the CMIP3 models shown in Grainger et al. (2012). However, the second ENSO mode in SH summer has a poorly reproduced EOF structure in the CMIP5 models, and the associated variance is generally underestimated. In SH winter, the performance of the CMIP5 models in reproducing the structure and variance is similar for both ENSO modes, with the associated variance being generally underestimated. Projected changes in the modes in the 21st century are then investigated using ensembles of CMIP5 models that reproduce well the 20th century slow modes. The slow-internal and slow-external components are examined separately, allowing the projected changes in the response to ENSO variability to be separated from the response to changes in greenhouse gas concentrations. By using several ensembles, the model-dependency of the projected changes in the ENSO-related slow-internal modes is examined. Frederiksen, C. S., and X. Zheng, 2007: Variability of seasonal-mean fields arising from intraseasonal variability. Part 3: Application to SH winter and summer circulations. Climate Dyn., 28, 849-866. Grainger, S., C. S. Frederiksen, and X. Zheng, 2012: Modes of interannual variability of Southern Hemisphere atmospheric circulation in CMIP3 models: Assessment and Projections. Climate Dyn., in press. Zheng, X., D. M. Straus, C. S. Frederiksen, and S. Grainger, 2009: Potentially predictable patterns of extratropical tropospheric circulation in an ensemble of climate simulations with the COLA AGCM. Quart. J. Roy. Meteor. Soc., 135, 1816-1829.

How well do CMIP5 climate simulations replicate historical trends and patterns of droughts?

DOE PAGES

Nasrollahi, Nasrin; AghaKouchak, Amir; Cheng, Linyin; ...

2015-04-26

Assessing the uncertainties and understanding the deficiencies of climate models are fundamental to developing adaptation strategies. The objective of this study is to understand how well Coupled Model Intercomparison-Phase 5 (CMIP5) climate model simulations replicate ground-based observations of continental drought areas and their trends. The CMIP5 multimodel ensemble encompasses the Climatic Research Unit (CRU) ground-based observations of area under drought at all time steps. However, most model members overestimate the areas under extreme drought, particularly in the Southern Hemisphere (SH). Furthermore, the results show that the time series of observations and CMIP5 simulations of areas under drought exhibit more variabilitymore » in the SH than in the Northern Hemisphere (NH). The trend analysis of areas under drought reveals that the observational data exhibit a significant positive trend at the significance level of 0.05 over all land areas. The observed trend is reproduced by about three-fourths of the CMIP5 models when considering total land areas in drought. While models are generally consistent with observations at a global (or hemispheric) scale, most models do not agree with observed regional drying and wetting trends. Over many regions, at most 40% of the CMIP5 models are in agreement with the trends of CRU observations. The drying/wetting trends calculated using the 3 months Standardized Precipitation Index (SPI) values show better agreement with the corresponding CRU values than with the observed annual mean precipitation rates. As a result, pixel-scale evaluation of CMIP5 models indicates that no single model demonstrates an overall superior performance relative to the other models.« less

Robust scaling with global mean temperature of future heat stress projections within CMIP5 and CESM LENS

NASA Astrophysics Data System (ADS)

Buzan, J. R.; Huber, M.

2016-12-01

Heat stress is of global concern because it threatens human and animal health and productivity. Here we use the HumanIndexMod to calculate 3 moist thermodynamic quantities and 9 commonly and operationally used heat stress metrics (Buzan et al., 2015). We drive the HumanIndexMod with output from CMIP5 and the Community Earth System Model Large Ensemble (LENS) using the greenhouse gasses forcing, representative concentration pathway 8.5 (RCP8.5). We limit our analysis to models that provide 4x daily output of surface pressure, reference height temperature and moisture, and use lowest model level winds where available, 18 CMIP5 and 40 LENS simulations. We show three novel results: Comparing time slices (2081-2100 and 2026-2045 for CMIP5, and 2071-2080 and 2026-2035 for LENS), we note that each individual heat stress metric extreme, within the multi-model mean, has spatial patterns that are highly correlated (>0.99). Moist thermodynamics and heat stress extremes are intrinsically linked to the thermodynamics of the climate, and scales simply with global mean surface temperature (GMT) changes. For example, large swaths of land surface area from 30°N to 30°S, excluding the Sahel, the Arabian Peninsula, and Himalayan Plateau, show the response of wet bulb temperature to be 0.85°C/°C GMT (standard deviation <0.25) for CMIP5 and 0.85°C/°C GMT (standard deviation <0.2) for LENS in agreement with prior work by Sherwood and Huber (2010). Many heat stress metrics, after being normalized by global mean surface temperature changes, are highly spatially correlated with each other, and may reduce the necessity of numerous metrics to properly quantify total heat stress. The three results establish that different climate models, with various underlying assumptions (CMIP5) and ranges of internal variability (LENS), show similar responses in heat stress with respect to global mean temperature changes. Thus, we find the uncertainty of heat stress extremes, even changes at the fine scale, is largely subsumed within the main uncertainties encompassed in transient climate sensitivity. These results are consistent with the hypothesis that outdoor worker productivity will drop significantly with substantial climate change.

On the Value of Climate Elasticity Indices to Assess the Impact of Climate Change on Streamflow Projection using an ensemble of bias corrected CMIP5 dataset

NASA Astrophysics Data System (ADS)

Demirel, Mehmet; Moradkhani, Hamid

2015-04-01

Changes in two climate elasticity indices, i.e. temperature and precipitation elasticity of streamflow, were investigated using an ensemble of bias corrected CMIP5 dataset as forcing to two hydrologic models. The Variable Infiltration Capacity (VIC) and the Sacramento Soil Moisture Accounting (SAC-SMA) hydrologic models, were calibrated at 1/16 degree resolution and the simulated streamflow was routed to the basin outlet of interest. We estimated precipitation and temperature elasticity of streamflow from: (1) observed streamflow; (2) simulated streamflow by VIC and SAC-SMA models using observed climate for the current climate (1963-2003); (3) simulated streamflow using simulated climate from 10 GCM - CMIP5 dataset for the future climate (2010-2099) including two concentration pathways (RCP4.5 and RCP8.5) and two downscaled climate products (BCSD and MACA). The streamflow sensitivity to long-term (e.g., 30-year) average annual changes in temperature and precipitation is estimated for three periods i.e. 2010-40, 2040-70 and 2070-99. We compared the results of the three cases to reflect on the value of precipitation and temperature indices to assess the climate change impacts on Columbia River streamflow. Moreover, these three cases for two models are used to assess the effects of different uncertainty sources (model forcing, model structure and different pathways) on the two climate elasticity indices.

Maritime Continent seasonal climate biases in AMIP experiments of the CMIP5 multimodel ensemble

NASA Astrophysics Data System (ADS)

Toh, Ying Ying; Turner, Andrew G.; Johnson, Stephanie J.; Holloway, Christopher E.

2018-02-01

The fidelity of 28 Coupled Model Intercomparison Project phase 5 (CMIP5) models in simulating mean climate over the Maritime Continent in the Atmospheric Model Intercomparison Project (AMIP) experiment is evaluated in this study. The performance of AMIP models varies greatly in reproducing seasonal mean climate and the seasonal cycle. The multi-model mean has better skill at reproducing the observed mean climate than the individual models. The spatial pattern of 850 hPa wind is better simulated than the precipitation in all four seasons. We found that model horizontal resolution is not a good indicator of model performance. Instead, a model's local Maritime Continent biases are somewhat related to its biases in the local Hadley circulation and global monsoon. The comparison with coupled models in CMIP5 shows that AMIP models generally performed better than coupled models in the simulation of the global monsoon and local Hadley circulation but less well at simulating the Maritime Continent annual cycle of precipitation. To characterize model systematic biases in the AMIP runs, we performed cluster analysis on Maritime Continent annual cycle precipitation. Our analysis resulted in two distinct clusters. Cluster I models are able to capture both the winter monsoon and summer monsoon shift, but they overestimate the precipitation; especially during the JJA and SON seasons. Cluster II models simulate weaker seasonal migration than observed, and the maximum rainfall position stays closer to the equator throughout the year. The tropics-wide properties of these clusters suggest a connection between the skill of simulating global properties of the monsoon circulation and the skill of simulating the regional scale of Maritime Continent precipitation.

The natural oscillation of two types of ENSO events based on analyses of CMIP5 model control runs

NASA Astrophysics Data System (ADS)

Xu, Kang; Su, Jingzhi; Zhu, Congwen

2014-07-01

The eastern- and central-Pacific El Niño-Southern Oscillation (EP- and CP-ENSO) have been found to be dominant in the tropical Pacific Ocean, and are characterized by interannual and decadal oscillation, respectively. In the present study, we defined the EP- and CP-ENSO modes by singular value decomposition (SVD) between SST and sea level pressure (SLP) anomalous fields. We evaluated the natural features of these two types of ENSO modes as simulated by the pre-industrial control runs of 20 models involved in phase five of the Coupled Model Intercomparison Project (CMIP5). The results suggested that all the models show good skill in simulating the SST and SLP anomaly dipolar structures for the EP-ENSO mode, but only 12 exhibit good performance in simulating the tripolar CP-ENSO modes. Wavelet analysis suggested that the ensemble principal components in these 12 models exhibit an interannual and multi-decadal oscillation related to the EP- and CP-ENSO, respectively. Since there are no changes in external forcing in the pre-industrial control runs, such a result implies that the decadal oscillation of CP-ENSO is possibly a result of natural climate variability rather than external forcing.

Multi-model ensemble projections of future extreme heat stress on rice across southern China

NASA Astrophysics Data System (ADS)

He, Liang; Cleverly, James; Wang, Bin; Jin, Ning; Mi, Chunrong; Liu, De Li; Yu, Qiang

2017-08-01

Extreme heat events have become more frequent and intense with climate warming, and these heatwaves are a threat to rice production in southern China. Projected changes in heat stress in rice provide an assessment of the potential impact on crop production and can direct measures for adaptation to climate change. In this study, we calculated heat stress indices using statistical scaling techniques, which can efficiently downscale output from general circulation models (GCMs). Data across the rice belt in southern China were obtained from 28 GCMs in the Coupled Model Intercomparison Project phase 5 (CMIP5) with two emissions scenarios (RCP4.5 for current emissions and RCP8.5 for increasing emissions). Multi-model ensemble projections over the historical period (1960-2010) reproduced the trend of observations in heat stress indices (root-mean-square error RMSE = 6.5 days) better than multi-model arithmetic mean (RMSE 8.9 days) and any individual GCM (RMSE 11.4 days). The frequency of heat stress events was projected to increase by 2061-2100 in both scenarios (up to 185 and 319% for RCP4.5 and RCP8.5, respectively), especially in the middle and lower reaches of the Yangtze River. This increasing risk of exposure to heat stress above 30 °C during flowering and grain filling is predicted to impact rice production. The results of our study suggest the importance of specific adaption or mitigation strategies, such as selection of heat-tolerant cultivars and adjustment of planting date in a warmer future world.

Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization

DOE PAGES

Eyring, Veronika; Bony, Sandrine; Meehl, Gerald A.; ...

2016-05-26

By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) andmore » CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: – How does the Earth system respond to forcing? – What are the origins and consequences of systematic model biases? – How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.« less

Efficient design based on perturbed parameter ensembles to identify plausible and diverse variants of a model for climate change projections

NASA Astrophysics Data System (ADS)

Karmalkar, A.; Sexton, D.; Murphy, J.

2017-12-01

We present exploratory work towards developing an efficient strategy to select variants of a state-of-the-art but expensive climate model suitable for climate projection studies. The strategy combines information from a set of idealized perturbed parameter ensemble (PPE) and CMIP5 multi-model ensemble (MME) experiments, and uses two criteria as basis to select model variants for a PPE suitable for future projections: a) acceptable model performance at two different timescales, and b) maintaining diversity in model response to climate change. We demonstrate that there is a strong relationship between model errors at weather and climate timescales for a variety of key variables. This relationship is used to filter out parts of parameter space that do not give credible simulations of historical climate, while minimizing the impact on ranges in forcings and feedbacks that drive model responses to climate change. We use statistical emulation to explore the parameter space thoroughly, and demonstrate that about 90% can be filtered out without affecting diversity in global-scale climate change responses. This leads to identification of plausible parts of parameter space from which model variants can be selected for projection studies.

Projected changes, climate change signal, and uncertainties in the CMIP5-based projections of ocean surface wave heights

NASA Astrophysics Data System (ADS)

Wang, Xiaolan; Feng, Yang; Swail, Val R.

2016-04-01

Ocean surface waves can be major hazards in coastal and offshore activities. However, wave observations are available only at limited locations and cover only the recent few decades. Also, there exists very limited information on ocean wave behavior in response to climate change, because such information is not simulated in current global climate models. In a recent study, we used a multivariate regression model with lagged dependent variable to make statistical global projections of changes in significant wave heights (Hs) using mean sea level pressure (SLP) information from 20 CMIP5 climate models for the twenty-first century. The statistical model was calibrated and validated using the ERA-Interim reanalysis of Hs and SLP for the period 1981-2010. The results show Hs increases in the tropics (especially in the eastern tropical Pacific) and in southern hemisphere high-latitudes. Under the projected 2070-2099 climate condition of the RCP8.5 scenario, the occurrence frequency of the present-day one-in-10-year extreme wave heights is likely to double or triple in several coastal regions around the world (e.g., the Chilean coast, Gulf of Oman, Gulf of Bengal, Gulf of Mexico). More recently, we used the analysis of variance approaches to quantify the climate change signal and uncertainty in multi-model ensembles of statistical Hs simulations globally, which are based on the CMIP5 historical, RCP4.5 and RCP8.5 forcing scenario simulations of SLP. In a 4-model 3-run ensemble, the 4-model common signal of climate change is found to strengthen over time, as would be expected. For the historical followed by RCP8.5 scenario, the common signal in annual mean Hs is found to be significant over 16.6%, 55.0% and 82.2% of the area by year 2005, 2050 and 2099, respectively. For the annual maximum, the signal is much weaker. The signal is strongest in the eastern tropical Pacific, featuring significant increases in both the annual mean and maximum of Hs in this region. The climate model uncertainty (i.e., inter-model variability) is significant over 99.9% of the area; its magnitude is comparable to or greater than the climate change signal by 2099 over most areas, except in the eastern tropical Pacific where the signal is much larger. In a 20-model 2-scenario single-run ensemble of statistical Hs simulations for the period 2006-2099, the model uncertainty is found to be significant globally; it is about 10 times as large as the scenario uncertainty between RCP4.5 and RCP8.5 scenarios.

Maximum warming occurs about one decade after carbon dioxide emission

NASA Astrophysics Data System (ADS)

Ricke, K.; Caldeira, K.

2014-12-01

There has been a long tradition of estimating the amount of climate change that would result from various carbon dioxide emission or concentration scenarios but there has been relatively little quantitative analysis of how long it takes to feel the consequences of an individual carbon dioxide emission. Using conjoined results of recent carbon-cycle and physical-climate model intercomparison projects, we find the median time between an emission and maximum warming is 10.1 years, with a 90% probability range of 6.6 to 30.7 years. We evaluate uncertainties in timing and amount of warming, partitioning them into three contributing factors: carbon cycle, climate sensitivity and ocean thermal inertia. To characterize the carbon cycle uncertainty associated with the global temperature response to a carbon dioxide emission today, we use fits to the time series of carbon dioxide concentrations from a CO2-impulse response function model intercomparison project's 15 ensemble members (1). To characterize both the uncertainty in climate sensitivity and in the thermal inertia of the climate system, we use fits to the time series of global temperature change from the Coupled Model Intercomparison Project phase 5 (CMIP5; 2) abrupt4xco2 experiment's 20 ensemble's members separating the effects of each uncertainty factors using one of two simple physical models for each CMIP5 climate model. This yields 6,000 possible combinations of these three factors using a standard convolution integral approach. Our results indicate that benefits of avoided climate damage from avoided CO2 emissions will be manifested within the lifetimes of people who acted to avoid that emission. While the relevant time lags imposed by the climate system are substantially shorter than a human lifetime, they are substantially longer than the typical political election cycle, making the delay and its associated uncertainties both economically and politically significant. References: 1. Joos F et al. (2013) Carbon dioxide and climate impulse response functions for the computation of greenhouse gas metrics: a multi-model analysis. Atmos Chem Phys 13:2793-2825. 2. Taylor KE, Stouffer RJ, Meehl GA (2011) An Overview of CMIP5 and the Experiment Design. Bull Am Meteorol Soc 93:485-498.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nasrollahi, Nasrin; AghaKouchak, Amir; Cheng, Linyin

Assessing the uncertainties and understanding the deficiencies of climate models are fundamental to developing adaptation strategies. The objective of this study is to understand how well Coupled Model Intercomparison-Phase 5 (CMIP5) climate model simulations replicate ground-based observations of continental drought areas and their trends. The CMIP5 multimodel ensemble encompasses the Climatic Research Unit (CRU) ground-based observations of area under drought at all time steps. However, most model members overestimate the areas under extreme drought, particularly in the Southern Hemisphere (SH). Furthermore, the results show that the time series of observations and CMIP5 simulations of areas under drought exhibit more variabilitymore » in the SH than in the Northern Hemisphere (NH). The trend analysis of areas under drought reveals that the observational data exhibit a significant positive trend at the significance level of 0.05 over all land areas. The observed trend is reproduced by about three-fourths of the CMIP5 models when considering total land areas in drought. While models are generally consistent with observations at a global (or hemispheric) scale, most models do not agree with observed regional drying and wetting trends. Over many regions, at most 40% of the CMIP5 models are in agreement with the trends of CRU observations. The drying/wetting trends calculated using the 3 months Standardized Precipitation Index (SPI) values show better agreement with the corresponding CRU values than with the observed annual mean precipitation rates. As a result, pixel-scale evaluation of CMIP5 models indicates that no single model demonstrates an overall superior performance relative to the other models.« less

Uncertainty of global summer precipitation in the CMIP5 models: a comparison between high-resolution and low-resolution models

NASA Astrophysics Data System (ADS)

Huang, Danqing; Yan, Peiwen; Zhu, Jian; Zhang, Yaocun; Kuang, Xueyuan; Cheng, Jing

2018-04-01

The uncertainty of global summer precipitation simulated by the 23 CMIP5 CGCMs and the possible impacts of model resolutions are investigated in this study. Large uncertainties exist over the tropical and subtropical regions, which can be mainly attributed to convective precipitation simulation. High-resolution models (HRMs) and low-resolution models (LRMs) are further investigated to demonstrate their different contributions to the uncertainties of the ensemble mean. It shows that the high-resolution model ensemble means (HMME) and low-resolution model ensemble mean (LMME) mitigate the biases between the MME and observation over most continents and oceans, respectively. The HMME simulates more precipitation than the LMME over most oceans, but less precipitation over some continents. The dominant precipitation category in the HRMs (LRMs) is the heavy precipitation (moderate precipitation) over the tropic regions. The combinations of convective and stratiform precipitation are also quite different: the HMME has much higher ratio of stratiform precipitation while the LMME has more convective precipitation. Finally, differences in precipitation between the HMME and LMME can be traced to their differences in the SST simulations via the local and remote air-sea interaction.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Eyring, Veronika; Bony, Sandrine; Meehl, Gerald A.

By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) andmore » CMIP historical simulations (1850–near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: – How does the Earth system respond to forcing? – What are the origins and consequences of systematic model biases? – How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.« less

Climate change hotspots in the CMIP5 global climate model ensemble.

PubMed

Diffenbaugh, Noah S; Giorgi, Filippo

2012-01-10

We use a statistical metric of multi-dimensional climate change to quantify the emergence of global climate change hotspots in the CMIP5 climate model ensemble. Our hotspot metric extends previous work through the inclusion of extreme seasonal temperature and precipitation, which exert critical influence on climate change impacts. The results identify areas of the Amazon, the Sahel and tropical West Africa, Indonesia, and the Tibetan Plateau as persistent regional climate change hotspots throughout the 21 st century of the RCP8.5 and RCP4.5 forcing pathways. In addition, areas of southern Africa, the Mediterranean, the Arctic, and Central America/western North America also emerge as prominent regional climate change hotspots in response to intermediate and high levels of forcing. Comparisons of different periods of the two forcing pathways suggest that the pattern of aggregate change is fairly robust to the level of global warming below approximately 2°C of global warming (relative to the late-20 th -century baseline), but not at the higher levels of global warming that occur in the late-21 st -century period of the RCP8.5 pathway, with areas of southern Africa, the Mediterranean, and the Arctic exhibiting particular intensification of relative aggregate climate change in response to high levels of forcing. Although specific impacts will clearly be shaped by the interaction of climate change with human and biological vulnerabilities, our identification of climate change hotspots can help to inform mitigation and adaptation decisions by quantifying the rate, magnitude and causes of the aggregate climate response in different parts of the world.

Assessment of the aerosol distribution over Indian subcontinent in CMIP5 models

NASA Astrophysics Data System (ADS)

Sanap, S. D.; Ayantika, D. C.; Pandithurai, G.; Niranjan, K.

2014-04-01

This paper examines the aerosol distribution over Indian subcontinent as represented in 21 models from Coupled Model Inter-comparison Project Phase 5 (CMIP5) simulations, wherein model simulated aerosol optical depth (AOD) is compared with Moderate Resolution Imaging Spectro-radiometer (MODIS) satellite observations. The objective of the study is to provide an assessment of the capability of various global models, participating in CMIP5 project, in capturing the realistic spatial and temporal distribution of aerosol species over the Indian subcontinent. Results from our analysis show that majority of the CMIP5 models (excepting HADGEM2-ES, HADGEM2-CC) seriously underestimates the spatio-temporal variability of aerosol species over the Indian subcontinent, in particular over Indo-Gangetic Plains (IGP). Since IGP region is dominated by anthropogenic activities, high population density, and wind driven transport of dust and other aerosol species, MODIS observations reveal high AOD values over this region. Though the representation of black carbon (BC) loading in many models is fairly good, the dust loading is observed to be significantly low in majority of the models. The presence of pronounced dust activity over northern India and dust being one of the major constituent of aerosol species, the biases in dust loading has a great impact on the AOD of that region. We found that considerable biases in simulating the 850 hPa wind field (which plays important role in transport of dust from adjacent deserts) would be the possible reason for poor representation of dust AOD and in turn total AOD over Indian region in CMIP5 models. In addition, aerosol radiative forcing (ARF) underestimated/overestimated in most of the models. However, spatial distribution of ARF in multi-model ensemble mean is comparable reasonably well with observations with bias in magnitudes. This analysis emphasizes the fundamental need to improve the representation of aerosol species in current state of the art climate models. As reported in Intergovernmental Panel on Climate Change (IPCC) fourth assessment report (AR4), the level of scientific understanding (LOSU) of climatic impact of aerosols is medium-low. For better understanding of short and long term implications of changing concentrations of aerosol species on climate, it is imperative to have a realistic representation of aerosol distribution over regions with high aerosol loading.

May common model biases reduce CMIP5's ability to simulate the recent Pacific La Niña-like cooling?

NASA Astrophysics Data System (ADS)

Luo, Jing-Jia; Wang, Gang; Dommenget, Dietmar

2018-02-01

Over the recent three decades sea surface temperate (SST) in the eastern equatorial Pacific has decreased, which helps reduce the rate of global warming. However, most CMIP5 model simulations with historical radiative forcing do not reproduce this Pacific La Niña-like cooling. Based on the assumption of "perfect" models, previous studies have suggested that errors in simulated internal climate variations and/or external radiative forcing may cause the discrepancy between the multi-model simulations and the observation. But the exact causes remain unclear. Recent studies have suggested that observed SST warming in the other two ocean basins in past decades and the thermostat mechanism in the Pacific in response to increased radiative forcing may also play an important role in driving this La Niña-like cooling. Here, we investigate an alternative hypothesis that common biases of current state-of-the-art climate models may deteriorate the models' ability and can also contribute to this multi-model simulations-observation discrepancy. Our results suggest that underestimated inter-basin warming contrast across the three tropical oceans, overestimated surface net heat flux and underestimated local SST-cloud negative feedback in the equatorial Pacific may favor an El Niño-like warming bias in the models. Effects of the three common model biases do not cancel one another and jointly explain 50% of the total variance of the discrepancies between the observation and individual models' ensemble mean simulations of the Pacific SST trend. Further efforts on reducing common model biases could help improve simulations of the externally forced climate trends and the multi-decadal climate fluctuations.

Evaluation of CMIP5 continental precipitation simulations relative to satellite-based gauge-adjusted observations

NASA Astrophysics Data System (ADS)

Mehran, A.; AghaKouchak, A.; Phillips, T. J.

2014-02-01

The objective of this study is to cross-validate 34 Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations of precipitation against the Global Precipitation Climatology Project (GPCP) data, quantifying model pattern discrepancies, and biases for both entire distributions and their upper tails. The results of the volumetric hit index (VHI) analysis of the total monthly precipitation amounts show that most CMIP5 simulations are in good agreement with GPCP patterns in many areas but that their replication of observed precipitation over arid regions and certain subcontinental regions (e.g., northern Eurasia, eastern Russia, and central Australia) is problematical. Overall, the VHI of the multimodel ensemble mean and median also are superior to that of the individual CMIP5 models. However, at high quantiles of reference data (75th and 90th percentiles), all climate models display low skill in simulating precipitation, except over North America, the Amazon, and Central Africa. Analyses of total bias (B) in CMIP5 simulations reveal that most models overestimate precipitation over regions of complex topography (e.g., western North and South America and southern Africa and Asia), while underestimating it over arid regions. Also, while most climate model simulations show low biases over Europe, intermodel variations in bias over Australia and Amazonia are considerable. The quantile bias analyses indicate that CMIP5 simulations are even more biased at high quantiles of precipitation. It is found that a simple mean field bias removal improves the overall B and VHI values but does not make a significant improvement at high quantiles of precipitation.

Inflated Uncertainty in Multimodel-Based Regional Climate Projections.

PubMed

Madsen, Marianne Sloth; Langen, Peter L; Boberg, Fredrik; Christensen, Jens Hesselbjerg

2017-11-28

Multimodel ensembles are widely analyzed to estimate the range of future regional climate change projections. For an ensemble of climate models, the result is often portrayed by showing maps of the geographical distribution of the multimodel mean results and associated uncertainties represented by model spread at the grid point scale. Here we use a set of CMIP5 models to show that presenting statistics this way results in an overestimation of the projected range leading to physically implausible patterns of change on global but also on regional scales. We point out that similar inconsistencies occur in impact analyses relying on multimodel information extracted using statistics at the regional scale, for example, when a subset of CMIP models is selected to represent regional model spread. Consequently, the risk of unwanted impacts may be overestimated at larger scales as climate change impacts will never be realized as the worst (or best) case everywhere.

A new framework for quantifying uncertainties in modelling studies for future climates - how more certain are CMIP5 precipitation and temperature simulations compared to CMIP3?

NASA Astrophysics Data System (ADS)

Sharma, A.; Woldemeskel, F. M.; Sivakumar, B.; Mehrotra, R.

2014-12-01

We outline a new framework for assessing uncertainties in model simulations, be they hydro-ecological simulations for known scenarios, or climate simulations for assumed scenarios representing the future. This framework is illustrated here using GCM projections for future climates for hydrologically relevant variables (precipitation and temperature), with the uncertainty segregated into three dominant components - model uncertainty, scenario uncertainty (representing greenhouse gas emission scenarios), and ensemble uncertainty (representing uncertain initial conditions and states). A novel uncertainty metric, the Square Root Error Variance (SREV), is used to quantify the uncertainties involved. The SREV requires: (1) Interpolating raw and corrected GCM outputs to a common grid; (2) Converting these to percentiles; (3) Estimating SREV for model, scenario, initial condition and total uncertainty at each percentile; and (4) Transforming SREV to a time series. The outcome is a spatially varying series of SREVs associated with each model that can be used to assess how uncertain the system is at each simulated point or time. This framework, while illustrated in a climate change context, is completely applicable for assessment of uncertainties any modelling framework may be subject to. The proposed method is applied to monthly precipitation and temperature from 6 CMIP3 and 13 CMIP5 GCMs across the world. For CMIP3, B1, A1B and A2 scenarios whereas for CMIP5, RCP2.6, RCP4.5 and RCP8.5 representing low, medium and high emissions are considered. For both CMIP3 and CMIP5, model structure is the largest source of uncertainty, which reduces significantly after correcting for biases. Scenario uncertainly increases, especially for temperature, in future due to divergence of the three emission scenarios analysed. While CMIP5 precipitation simulations exhibit a small reduction in total uncertainty over CMIP3, there is almost no reduction observed for temperature projections. Estimation of uncertainty in both space and time sheds lights on the spatial and temporal patterns of uncertainties in GCM outputs, providing an effective platform for risk-based assessments of any alternate plans or decisions that may be formulated using GCM simulations.

The Effects of Climate Model Similarity on Local, Risk-Based Adaptation Planning

NASA Astrophysics Data System (ADS)

Steinschneider, S.; Brown, C. M.

2014-12-01

The climate science community has recently proposed techniques to develop probabilistic projections of climate change from ensemble climate model output. These methods provide a means to incorporate the formal concept of risk, i.e., the product of impact and probability, into long-term planning assessments for local systems under climate change. However, approaches for pdf development often assume that different climate models provide independent information for the estimation of probabilities, despite model similarities that stem from a common genealogy. Here we utilize an ensemble of projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to develop probabilistic climate information, with and without an accounting of inter-model correlations, and use it to estimate climate-related risks to a local water utility in Colorado, U.S. We show that the tail risk of extreme climate changes in both mean precipitation and temperature is underestimated if model correlations are ignored. When coupled with impact models of the hydrology and infrastructure of the water utility, the underestimation of extreme climate changes substantially alters the quantification of risk for water supply shortages by mid-century. We argue that progress in climate change adaptation for local systems requires the recognition that there is less information in multi-model climate ensembles than previously thought. Importantly, adaptation decisions cannot be limited to the spread in one generation of climate models.

How Continuous Observations of Shortwave Reflectance Spectra Can Narrow the Range of Shortwave Climate Feedbacks

NASA Astrophysics Data System (ADS)

Feldman, D.; Collins, W. D.; Wielicki, B. A.; Shea, Y.; Mlynczak, M. G.; Kuo, C.; Nguyen, N.

2017-12-01

Shortwave feedbacks are a persistent source of uncertainty for climate models and a large contributor to the diagnosed range of equilibrium climate sensitivity (ECS) for the international multi-model ensemble. The processes that contribute to these feedbacks affect top-of-atmosphere energetics and produce spectral signatures that may be time-evolving. We explore the value of such spectral signatures for providing an observational constraint on model ECS by simulating top-of-atmosphere shortwave reflectance spectra across much of the energetically-relevant shortwave bandpass (300 to 2500 nm). We present centennial-length shortwave hyperspectral simulations from low, medium and high ECS models that reported to the CMIP5 archive as part of an Observing System Simulation Experiment (OSSE) in support of the CLimate Absolute Radiance and Refractivity Observatory (CLARREO). Our framework interfaces with CMIP5 archive results and is agnostic to the choice of model. We simulated spectra from the INM-CM4 model (ECS of 2.08 °K/2xCO2), the MIROC5 model (ECS of 2.70 °K/2xCO2), and the CSIRO Mk3-6-0 (ECS of 4.08 °K/2xCO2) based on those models' integrations of the RCP8.5 scenario for the 21st Century. This approach allows us to explore how perfect data records can exclude models of lower or higher climate sensitivity. We find that spectral channels covering visible and near-infrared water-vapor overtone bands can potentially exclude a low or high sensitivity model with under 15 years' of absolutely-calibrated data. These different spectral channels are sensitive to model cloud radiative effect and cloud height changes, respectively. These unprecedented calculations lay the groundwork for spectral simulations of perturbed-physics ensembles in order to identify those shortwave observations that can help narrow the range in shortwave model feedbacks and ultimately help reduce the stubbornly-large range in model ECS.

Historical and projected changes in the Southern Hemisphere Sub-tropical Jet during winter from the CMIP5 models

NASA Astrophysics Data System (ADS)

Chenoli, Sheeba Nettukandy; Ahmad Mazuki, Muhammad Yunus; Turner, John; Samah, Azizan Abu

2017-01-01

We present projected changes in the speed and meridional location of the Subtropical Jet (STJ) during winter using output of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. We use the ERA-Interim reanalysis dataset to evaluate the historical simulations of the STJ by 18 of the CMIP5 models for the period 1979-2012. Based on the climatology of the STJ from ERA-Interim, we selected the area of study as 70°E-290°E and 20°S-40°S, which is over the Indian and Southern Pacific Oceans, and 300-100 hPa to reduce altitude-related bias. An assessment of the ability of the CMIP5 models in simulating ENSO effects on the jet stream were carried out using standardized zonal wind anomalies at 300-100 hPa. Results show that 47 % of the CMIP5 models used in this study were able to simulate ENSO impacts realistically. In addition, it is more difficult for the models to reproduce the observed intensity of ENSO impacts than the patterns. The historical simulations of the CMIP5 models show a wide range of trends in meridional movement and jet strength, with a multi-model mean of 0.04° decade-1 equatorward and 0.42 ms-1 decade-1 respectively. In contrast to the ERA-Interim analysis, 94 % of the CMIP5 models show a strengthening of the jet in the historical runs. Variability of the jet strength is significantly (5 %) linked to the sea surface temperature changes over the eastern tropical Pacific. The CMIP5 model projections with Representative Concentration Pathways (RCPs) 4.5 and 8.5 were used for analysis of changes of the STJ for the period 2011-2099. Based on the RCP 4.5 (RCP 8.5) scenario the multi-model mean trend of the 18 CMIP5 models project a statistically significant (5 % level) increase in jet strength by the end of the century of 0.29 ms-1 decade-1 (0.60 ms-1 decade-1). Also, the mean meridional location of the jet is projected to shift poleward by 0.006° decade-1 (0.042° decade-1) in 2099 during winter, with the only significant (5 %) trend being with RCP 8.5.

Inability of CMIP5 Climate Models to Simulate Recent Multi-decadal Climate Change in the Tropical Pacific.

NASA Astrophysics Data System (ADS)

Power, S.; Delage, F.; Kociuba, G.; Wang, G.; Smith, I.

2017-12-01

Observed 15-year surface temperature trends beginning 1998 or later have attracted a great deal of interest because of an apparent slowdown in the rate of global warming, and contrasts between climate model simulations and observations of such trends. Many studies have addressed the statistical significance of these relatively short trends, whether they indicate a possible bias in models and the implications for global warming generally. Here we analyse historical and projected changes in 38 CMIP5 climate models. All of the models simulate multi-decadal warming in the Pacific over the past half-century that exceeds observed values. This stark difference cannot be fully explained by observed, internal multi-decadal climate variability, even if allowance is made for an apparent tendency for models to underestimate internal multi-decadal variability in the Pacific. We also show that CMIP5 models are not able to simulate the magnitude of the strengthening of the Walker Circulation over the past thirty years. Some of the reasons for these major shortcomings in the ability of models to simulate multi-decadal variability in the Pacific, and the impact these findings have on our confidence in global 21st century projections, will be discussed.

Exceptional Arctic warmth of early winter 2016 and attribution to global warming

NASA Astrophysics Data System (ADS)

van Oldenborgh, Geert Jan; Macias-Fauria, Marc; King, Andrew; Uhe, Peter; Philip, Sjoukje; Kew, Sarah; Karoly, David; Otto, Friederike; Allen, Myles; Cullen, Heidi

2017-04-01

The dark polar winters usually sport the coldest extremes on Earth, however this winter, the North Pole and the surrounding Arctic region have experienced record high temperatures in November and December, with daily means reaching 15 °C (27 °F) above normal and a November monthly mean that was 13 °C (23 °F) above normal on the pole. November also saw a brief retreat of sea-ice that was virtually unprecedented in nearly 40 years of satellite records, followed by a record low in November sea ice area since 1850. Unlike the Antarctic, Arctic lands are inhabited and their socio-economic systems are greatly affected by the impacts of extreme and unprecedented sea ice dynamics and temperatures, such as for example, the timing of marine mammal migrations, and refreezing rain on snow that prevents reindeer from feeding. Here we report on our multi-method rapid attribution analysis of North Pole November-December temperatures. To quantify the rarity of the event, we computed the November-December averaged temperature around the North Pole (80-90 °N) in the (short but North-pole covering) ERA-interim reanalysis. To put the event in context of natural variability, we use a longer and closely related time series based on the northern most meteorological observations on land (70-80 °N). This allows for a reconstruction of Arctic temperatures back to about 1900. We also perform a multi-method analysis of North Pole temperatures with two sets of climate models: the CMIP5 multi-model ensemble, and a large ensemble of model runs in the so-called Weather@Home project. Physical mechanisms that are responsible for temperature and sea ice variability in the North Pole region are also discussed. The observations and the bias-corrected CMIP5 ensemble point to a return period of about 50 to 200 years in the present climate, i.e., the probability of such an extreme is about 0.5% to 2% every year, with a large uncertainty. The observations show that November-December temperatures have risen on the North Pole, modulated by decadal North Atlantic variability. For all phases of this variability, a warm event like the one of this winter would have been extremely unlikely in the climate of a century ago. Both sets of models also give very comparable results and show that the bulk of the arctic temperature increase is due to anthropogenic emissions. This also holds for the warm extremes caused by the type of circulation present in the early winter of 2016.

Projections of annual rainfall and surface temperature from CMIP5 models over the BIMSTEC countries

NASA Astrophysics Data System (ADS)

Pattnayak, K. C.; Kar, S. C.; Dalal, Mamta; Pattnayak, R. K.

2017-05-01

Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) comprising Bangladesh, Bhutan, India, Myanmar, Nepal, Sri Lanka and Thailand brings together 21% of the world population. Thus the impact of climate change in this region is a major concern for all. To study the climate change, fifth phase of Climate Model Inter-comparison Project (CMIP5) models have been used to project the climate for the 21st century under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 over the BIMSTEC countries for the period 1901 to 2100 (initial 105 years are historical period and the later 95 years are projected period). Climate change in the projected period has been examined with respect to the historical period. In order to validate the models, the mean annual rainfall has been compared with observations from multiple sources and temperature has been compared with the data from Climatic Research Unit (CRU) during the historical period. Comparison reveals that ensemble mean of the models is able to represent the observed spatial distribution of rainfall and temperature over the BIMSTEC countries. Therefore, data from these models may be used to study the future changes in the 21st century. Four out of six models show that the rainfall over India, Thailand and Myanmar has decreasing trend and Bangladesh, Bhutan, Nepal and Sri Lanka show an increasing trend in both the RCP scenarios. In case of temperature, all the models show an increasing trend over all the BIMSTEC countries in both the scenarios, however, the rate of increase is relatively less over Sri Lanka than the other countries. The rate of increase/decrease in rainfall and temperature are relatively more in RCP8.5 than RCP4.5 over all these countries. Inter-model comparison show that there are uncertainties within the CMIP5 model projections. More similar studies are required to be done for better understanding the model uncertainties in climate projections over this region.

Eurasian Winter Storm Activity at the End of the Century: A CMIP5 Multi-model Ensemble Projection

NASA Astrophysics Data System (ADS)

Basu, Soumik; Zhang, Xiangdong; Wang, Zhaomin

2018-01-01

Extratropical cyclone activity over Eurasia has exhibited a weakening trend in the recent decade. Extratropical cyclones bring precipitation and hence supply fresh water for winter crops in the mid- and high-latitude regions of Eurasia. Any changes in extratropical cyclone activity over Eurasia in the future may have a critical impact on winter agriculture and the economies of affected communities. However, potential future changes in regional storm activity over Eurasia have not been studied in detail. Therefore, in this study, we investigate anticipated changes in extratropical storm activity by the end of the century through a detailed examination of the historical and future emission scenarios from six different models from CMIP5. A statistical analysis of different parameters of storm activity using a storm identification and tracking algorithm reveals a decrease in the number of storms over mid-latitude regions. However, intense storms with longer duration are projected over high latitude Eurasia. A further examination of the physical mechanism for these changes reveals that a decrease in the meridional temperature gradient and a weakening of the vertical wind shear over the mid-latitudes are responsible for these changes in storm activity.

Uncertainty in Twenty-First-Century CMIP5 Sea Level Projections

NASA Technical Reports Server (NTRS)

Little, Christopher M.; Horton, Radley M.; Kopp, Robert E.; Oppenheimer, Michael; Yip, Stan

2015-01-01

The representative concentration pathway (RCP) simulations included in phase 5 of the Coupled Model Intercomparison Project (CMIP5) quantify the response of the climate system to different natural and anthropogenic forcing scenarios. These simulations differ because of 1) forcing, 2) the representation of the climate system in atmosphere-ocean general circulation models (AOGCMs), and 3) the presence of unforced (internal) variability. Global and local sea level rise projections derived from these simulations, and the emergence of distinct responses to the four RCPs depend on the relative magnitude of these sources of uncertainty at different lead times. Here, the uncertainty in CMIP5 projections of sea level is partitioned at global and local scales, using a 164-member ensemble of twenty-first-century simulations. Local projections at New York City (NYSL) are highlighted. The partition between model uncertainty, scenario uncertainty, and internal variability in global mean sea level (GMSL) is qualitatively consistent with that of surface air temperature, with model uncertainty dominant for most of the twenty-first century. Locally, model uncertainty is dominant through 2100, with maxima in the North Atlantic and the Arctic Ocean. The model spread is driven largely by 4 of the 16 AOGCMs in the ensemble; these models exhibit outlying behavior in all RCPs and in both GMSL and NYSL. The magnitude of internal variability varies widely by location and across models, leading to differences of several decades in the local emergence of RCPs. The AOGCM spread, and its sensitivity to model exclusion and/or weighting, has important implications for sea level assessments, especially if a local risk management approach is utilized.

Improved Decadal Climate Prediction in the North Atlantic using EnOI-Assimilated Initial Condition

NASA Astrophysics Data System (ADS)

Li, Q.; Xin, X.; Wei, M.; Zhou, W.

2017-12-01

Decadal prediction experiments of Beijing Climate Center climate system model version 1.1(BCC-CSM1.1) participated in Coupled Model Intercomparison Project Phase 5 (CMIP5) had poor skill in extratropics of the North Atlantic, the initialization of which was done by relaxing modeled ocean temperature to the Simple Ocean Data Assimilation (SODA) reanalysis data. This study aims to improve the prediction skill of this model by using the assimilation technique in the initialization. New ocean data are firstly generated by assimilating the sea surface temperature (SST) of the Hadley Centre Sea Ice and Sea Surface Temperature (HadISST) dataset to the ocean model of BCC-CSM1.1 via Ensemble Optimum Interpolation (EnOI). Then a suite of decadal re-forecasts launched annually over the period 1961-2005 is carried out with simulated ocean temperature restored to the assimilated ocean data. Comparisons between the re-forecasts and previous CMIP5 forecasts show that the re-forecasts are more skillful in mid-to-high latitude SST of the North Atlantic. Improved prediction skill is also found for the Atlantic multi-decadal Oscillation (AMO), which is consistent with the better skill of Atlantic meridional overturning circulation (AMOC) predicted by the re-forecasts. We conclude that the EnOI assimilation generates better ocean data than the SODA reanalysis for initializing decadal climate prediction of BCC-CSM1.1 model.

Model Independence in Downscaled Climate Projections: a Case Study in the Southeast United States

NASA Astrophysics Data System (ADS)

Gray, G. M. E.; Boyles, R.

2016-12-01

Downscaled climate projections are used to deduce how the climate will change in future decades at local and regional scales. It is important to use multiple models to characterize part of the future uncertainty given the impact on adaptation decision making. This is traditionally employed through an equally-weighted ensemble of multiple GCMs downscaled using one technique. Newer practices include several downscaling techniques in an effort to increase the ensemble's representation of future uncertainty. However, this practice may be adding statistically dependent models to the ensemble. Previous research has shown a dependence problem in the GCM ensemble in multiple generations, but has not been shown in the downscaled ensemble. In this case study, seven downscaled climate projections on the daily time scale are considered: CLAREnCE10, SERAP, BCCA (CMIP5 and CMIP3 versions), Hostetler, CCR, and MACA-LIVNEH. These data represent 83 ensemble members, 44 GCMs, and two generations of GCMs. Baseline periods are compared against the University of Idaho's METDATA gridded observation dataset. Hierarchical agglomerative clustering is applied to the correlated errors to determine dependent clusters. Redundant GCMs across different downscaling techniques show the most dependence, while smaller dependence signals are detected within downscaling datasets and across generations of GCMs. These results indicate that using additional downscaled projections to increase the ensemble size must be done with care to avoid redundant GCMs and the process of downscaling may increase the dependence of those downscaled GCMs. Climate model generation does not appear dissimilar enough to be treated as two separate statistical populations for ensemble building at the local and regional scales.

High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6

NASA Astrophysics Data System (ADS)

Haarsma, Reindert J.; Roberts, Malcolm J.; Vidale, Pier Luigi; Senior, Catherine A.; Bellucci, Alessio; Bao, Qing; Chang, Ping; Corti, Susanna; Fučkar, Neven S.; Guemas, Virginie; von Hardenberg, Jost; Hazeleger, Wilco; Kodama, Chihiro; Koenigk, Torben; Leung, L. Ruby; Lu, Jian; Luo, Jing-Jia; Mao, Jiafu; Mizielinski, Matthew S.; Mizuta, Ryo; Nobre, Paulo; Satoh, Masaki; Scoccimarro, Enrico; Semmler, Tido; Small, Justin; von Storch, Jin-Song

2016-11-01

Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest both the possibility of significant changes in large-scale aspects of circulation as well as improvements in small-scale processes and extremes. However, such high-resolution global simulations at climate timescales, with resolutions of at least 50 km in the atmosphere and 0.25° in the ocean, have been performed at relatively few research centres and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project 6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other model intercomparison projects (MIPs). Increases in high-performance computing (HPC) resources, as well as the revised experimental design for CMIP6, now enable a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability. The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal-resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950-2050, with the possibility of extending to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulations. HighResMIP thereby focuses on one of the CMIP6 broad questions, "what are the origins and consequences of systematic model biases?", but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges.

On the Lack of Stratospheric Dynamical Variability in Low-top Versions of the CMIP5 Models

NASA Technical Reports Server (NTRS)

Charlton-Perez, Andrew J.; Baldwin, Mark P.; Birner, Thomas; Black, Robert X.; Butler, Amy H.; Calvo, Natalia; Davis, Nicholas A.; Gerber, Edwin P.; Gillett, Nathan; Hardiman, Steven;

Multi-model ensemble simulations of low flows in Europe under a 1.5, 2, and 3 degree global warming

NASA Astrophysics Data System (ADS)

Marx, A.; Kumar, R.; Thober, S.; Zink, M.; Wanders, N.; Wood, E. F.; Pan, M.; Sheffield, J.; Samaniego, L. E.

2017-12-01

There is growing evidence that climate change will alter water availability in Europe. Here, we investigate how hydrological low flows are affected under different levels of future global warming (i.e., 1.5, 2 and 3 K). The analysis is based on a multi-model ensemble of 45 hydrological simulations based on three RCPs (rcp2p6, rcp6p0, rcp8p5), five CMIP5 GCMs (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M) and three state-of-the-art hydrological models (HMs: mHM, Noah-MP, and PCR-GLOBWB). High resolution model results are available at the unprecedented spatial resolution of 5 km across the pan-European domain at daily temporal resolution. Low river flow is described as the percentile of daily streamflow that is exceeded 90% of the time. It is determined separately for each GCM/HM combinations and the warming scenarios. The results show that the change signal amplifies with increasing warming levels. Low flows decrease in the Mediterranean, while they increase in the Alpine and Northern regions. In the Mediterranean, the level of warming amplifies the signal from -12% under 1.5 K to -35% under 3 K global warming largely due to the projected decreases in annual precipitation. In contrast, the signal is amplified from +22% (1.5 K) to +45% (3 K) because of the reduced snow melt contribution. The changes in low flows are significant for regions with relatively large change signals and under higher levels of warming. Nevertheless, it is not possible to distinguish climate induced differences in low flows between 1.5 and 2 K warming because of the large variability inherent in the multi-model ensemble. The contribution by the GCMs to the uncertainty in the Alpine and Northern region as well as the Mediterranean, the uncertainty contribution by the HMs is partly higher than those by the GCMs due to different representations of processes such as snow, soil moisture and evapotranspiration.

A common fallacy in climate model evaluation

NASA Astrophysics Data System (ADS)

Annan, J. D.; Hargreaves, J. C.; Tachiiri, K.

2012-04-01

We discuss the assessment of model ensembles such as that arising from the CMIP3 coordinated multi-model experiments. An important aspect of this is not merely the closeness of the models to observations in absolute terms but also the reliability of the ensemble spread as an indication of uncertainty. In this context, it has been widely argued that the multi-model ensemble of opportunity is insufficiently broad to adequately represent uncertainties regarding future climate change. For example, the IPCC AR4 summarises the consensus with the sentence: "Those studies also suggest that the current AOGCMs may not cover the full range of uncertainty for climate sensitivity." Similar claims have been made in the literature for other properties of the climate system, including the transient climate response and efficiency of ocean heat uptake. Comparison of model outputs with observations of the climate system forms an essential component of model assessment and is crucial for building our confidence in model predictions. However, methods for undertaking this comparison are not always clearly justified and understood. Here we show that the popular approach which forms the basis for the above claims, of comparing the ensemble spread to a so-called "observationally-constrained pdf", can be highly misleading. Such a comparison will almost certainly result in disagreement, but in reality tells us little about the performance of the ensemble. We present an alternative approach based on an assessment of the predictive performance of the ensemble, and show how it may lead to very different, and rather more encouraging, conclusions. We additionally outline some necessary conditions for an ensemble (or more generally, a probabilistic prediction) to be challenged by an observation.

Climate change indices for Greenland applied directly for other arctic regions - Enhanced and utilized climate information from one high resolution RCM downscaling for Greenland evaluated through pattern scaling and CMIP5

NASA Astrophysics Data System (ADS)

Olesen, M.; Christensen, J. H.; Boberg, F.

2016-12-01

Climate change indices for Greenland applied directly for other arctic regions - Enhanced and utilized climate information from one high resolution RCM downscaling for Greenland evaluated through pattern scaling and CMIP5Climate change affects the Greenlandic society both advantageously and disadvantageously. Changes in temperature and precipitation patterns may result in changes in a number of derived society related climate indices, such as the length of growing season or the number of annual dry days or a combination of the two - indices of substantial importance to society in a climate adaptation context.Detailed climate indices require high resolution downscaling. We have carried out a very high resolution (5 km) simulation with the regional climate model HIRHAM5, forced by the global model EC-Earth. Evaluation of RCM output is usually done with an ensemble of downscaled output with multiple RCM's and GCM's. Here we have introduced and tested a new technique; a translation of the robustness of an ensemble of GCM models from CMIP5 into the specific index from the HIRHAM5 downscaling through a correlation between absolute temperatures and its corresponding index values from the HIRHAM5 output.The procedure is basically conducted in two steps: First, the correlation between temperature and a given index for the HIRHAM5 simulation by a best fit to a second order polynomial is identified. Second, the standard deviation from the CMIP5 simulations is introduced to show the corresponding standard deviation of the index from the HIRHAM5 run. The change of specific climate indices due to global warming will then be possible to evaluate elsewhere corresponding to the change in absolute temperature.Results based on selected indices with focus on the future climate in Greenland calculated for the rcp4.5 and rcp8.5 scenarios will be presented.

Transit navigation through Northern Sea Route from satellite data and CMIP5 simulations

NASA Astrophysics Data System (ADS)

Khon, Vyacheslav C.; Mokhov, Igor I.; Semenov, Vladimir A.

2017-02-01

Rapid Arctic sea ice decline over the last few decades opens new perspectives for Arctic marine navigation. Further warming in the Arctic will promote the Northern Sea Route (NSR) as an alternative to the conventional Suez or Panama Canal routes for intercontinental shipping. Here we use both satellite data and CMIP5 ensemble of climate models to estimate the NSR transit window allowing intercontinental navigation between Atlantic and Pacific regions. To this end, we introduce a novel approach to calculate start and end dates of the navigation season along the NSR. We show that modern climate models are able to reproduce the mean time of the NSR transit window and its trend over the last few decades. The selected models demonstrate that the rate of increase of the NSR navigation season will slow down over the next few decades with the RCP4.5 scenario. By the end of the 21st century ensemble-mean estimates show an increase of the NSR transit window by about 4 and 6.5 months according to RCP4.5 and 8.5, respectively. Estimated trends for the end date of the navigation season are found to be stronger compared to those for the start date.

Representation of the Great Lakes in the Coupled Model Intercomparison Project Version 5

NASA Astrophysics Data System (ADS)

Briley, L.; Rood, R. B.

2017-12-01

The U.S. Great Lakes play a significant role in modifying regional temperatures and precipitation, and as the lakes change in response to a warming climate (i.e., warmer surface water temperatures, decreased ice cover, etc) lake-land-atmosphere dynamics are affected. Because the lakes modify regional weather and are a driver of regional climate change, understanding how they are represented in climate models is important to the reliability of model based information for the region. As part of the Great Lakes Integrated Sciences + Assessments (GLISA) Ensemble project, a major effort is underway to evaluate the Coupled Model Intercomparison Project version (CMIP) 5 global climate models for how well they physically represent the Great Lakes and lake-effects. The CMIP models were chosen because they are a primary source of information in many products developed for decision making (i.e., National Climate Assessment, downscaled future climate projections, etc.), yet there is very little description of how well they represent the lakes. This presentation will describe the results of our investigation of if and how the Great Lakes are represented in the CMIP5 models.

Understanding the Asian summer monsoon response to greenhouse warming: the relative roles of direct radiative forcing and sea surface temperature change

NASA Astrophysics Data System (ADS)

Li, Xiaoqiong; Ting, Mingfang

2017-10-01

Future hydroclimate projections from state-of-the-art climate models show large uncertainty and model spread, particularly in the tropics and over the monsoon regions. The precipitation and circulation responses to rising greenhouse gases involve a fast component associated with direct radiative forcing and a slow component associated with sea surface temperature (SST) warming; the relative importance of the two may contribute to model discrepancies. In this study, regional hydroclimate responses to greenhouse warming are assessed using output from coupled general circulation models in the Coupled Model Intercomparison Project-Phase 5 (CMIP5) and idealized atmospheric general circulation model experiments from the Atmosphere Model Intercomparison Project. The thermodynamic and dynamic mechanisms causing the rainfall changes are examined using moisture budget analysis. Results show that direct radiative forcing and SST change exert significantly different responses both over land and ocean. For most part of the Asian monsoon region, the summertime rainfall changes are dominated by the direct CO2 radiative effect through enhanced monsoon circulation. The response to SST warming shows a larger model spread compared to direct radiative forcing, possibly due to the cancellation between the thermodynamical and dynamical components. While the thermodynamical response of the Asian monsoon is robust across the models, there is a lack of consensus for the dynamical response among the models and weak multi-model mean responses in the CMIP5 ensemble, which may be related to the multiple physical processes evolving on different time scales.

How well the Reliable Ensemble Averaging Method (REA) for 15 CMIP5 GCMs simulations works for Mexico?

NASA Astrophysics Data System (ADS)

Colorado, G.; Salinas, J. A.; Cavazos, T.; de Grau, P.

2013-05-01

15 CMIP5 GCMs precipitation simulations were combined in a weighted ensemble using the Reliable Ensemble Averaging (REA) method, obtaining the weight of each model. This was done for a historical period (1961-2000) and for the future emissions based on low (RCP4.5) and high (RCP8.5) radiating forcing for the period 2075-2099. The annual cycle of simple ensemble of the historical GCMs simulations, the historical REA average and the Climate Research Unit (CRU TS3.1) database was compared in four zones of México. In the case of precipitation we can see the improvements by using the REA method, especially in the two northern zones of México where the REA average is more close to the observations (CRU) that the simple average. However in the southern zones although there is an improvement it is not as good as it is in the north, particularly in the southeast where instead of the REA average is able to reproduce qualitatively good the annual cycle with the mid-summer drought it was greatly underestimated. The main reason is because the precipitation is underestimated for all the models and the mid-summer drought do not even exists in some models. In the REA average of the future scenarios, as we can expected, the most drastic decrease in precipitation was simulated using the RCP8.5 especially in the monsoon area and in the south of Mexico in summer and in winter. In the center and southern of Mexico however, the same scenario in autumn simulates an increase of precipitation.

Creating "Intelligent" Ensemble Averages Using a Process-Based Framework

NASA Astrophysics Data System (ADS)

Baker, Noel; Taylor, Patrick

2014-05-01

The CMIP5 archive contains future climate projections from over 50 models provided by dozens of modeling centers from around the world. Individual model projections, however, are subject to biases created by structural model uncertainties. As a result, ensemble averaging of multiple models is used to add value to individual model projections and construct a consensus projection. Previous reports for the IPCC establish climate change projections based on an equal-weighted average of all model projections. However, individual models reproduce certain climate processes better than other models. Should models be weighted based on performance? Unequal ensemble averages have previously been constructed using a variety of mean state metrics. What metrics are most relevant for constraining future climate projections? This project develops a framework for systematically testing metrics in models to identify optimal metrics for unequal weighting multi-model ensembles. The intention is to produce improved ("intelligent") unequal-weight ensemble averages. A unique aspect of this project is the construction and testing of climate process-based model evaluation metrics. A climate process-based metric is defined as a metric based on the relationship between two physically related climate variables—e.g., outgoing longwave radiation and surface temperature. Several climate process metrics are constructed using high-quality Earth radiation budget data from NASA's Clouds and Earth's Radiant Energy System (CERES) instrument in combination with surface temperature data sets. It is found that regional values of tested quantities can vary significantly when comparing the equal-weighted ensemble average and an ensemble weighted using the process-based metric. Additionally, this study investigates the dependence of the metric weighting scheme on the climate state using a combination of model simulations including a non-forced preindustrial control experiment, historical simulations, and several radiative forcing Representative Concentration Pathway (RCP) scenarios. Ultimately, the goal of the framework is to advise better methods for ensemble averaging models and create better climate predictions.

Projected Changes in the Annual Cycle of Precipitation over Central Asia by CMIP5 Models

NASA Astrophysics Data System (ADS)

Yu, X.; Zhao, Y.

2017-12-01

Future changes in the annual cycle of the precipitation in central Asia (CA) were estimated based on the historical and Representative Concentration Pathway 8.5 (RCP8.5) experiments from 25 models of the Coupled Model Intercomparison Project phase 5 (CMIP5). Compared with the Global Precipitation Climatology Project (GPCP) observations, the historical (1979-1999) experiments showed that most models can capture the migration of rainfall centers, but remarkable discrepancies exist in the location and intensity of rainfall centers between simulations and observations. Considering the skill scores of precipitation and pattern correlations of circulations, which are closely related to the precipitation for each month, for the 25 models, the four best models (e.g., CanESM2, CMCC-CMS, MIROC5 and MPI-ESM-LR) with relatively good performance were selected. The four models' ensemble mean indicated that the migration and location of the precipitation centers were better reproduced, except the intensity of the centers was overestimated, compared with the result that only considered precipitation. Based on the four best models' ensemble mean under RCP8.5 scenarios, precipitation was projected to increase dramatically over most of the CA region in the boreal cold seasons (November, December, January, February, March, April and May) with the maximum in December in the end of twenty-first century (2079-2099), and several positive centers were located in the Pamirs Plateau and the Tianshan Mountains. By contrast, the precipitation changes were weak in the boreal warm seasons (June, July, August, September and October), with a wet center located in the northern Himalayas. Furthermore, there remain some uncertainties in the projected precipitation regions and periods obtained by comparing models' ensemble results of this paper and the results of previous studies. These uncertainties should be investigated in future work.

Forecasting European cold waves based on subsampling strategies of CMIP5 and Euro-CORDEX ensembles

NASA Astrophysics Data System (ADS)

Cordero-Llana, Laura; Braconnot, Pascale; Vautard, Robert; Vrac, Mathieu; Jezequel, Aglae

2016-04-01

Forecasting future extreme events under the present changing climate represents a difficult task. Currently there are a large number of ensembles of simulations for climate projections that take in account different models and scenarios. However, there is a need for reducing the size of the ensemble to make the interpretation of these simulations more manageable for impact studies or climate risk assessment. This can be achieved by developing subsampling strategies to identify a limited number of simulations that best represent the ensemble. In this study, cold waves are chosen to test different approaches for subsampling available simulations. The definition of cold waves depends on the criteria used, but they are generally defined using a minimum temperature threshold, the duration of the cold spell as well as their geographical extend. These climate indicators are not universal, highlighting the difficulty of directly comparing different studies. As part of the of the CLIPC European project, we use daily surface temperature data obtained from CMIP5 outputs as well as Euro-CORDEX simulations to predict future cold waves events in Europe. From these simulations a clustering method is applied to minimise the number of ensembles required. Furthermore, we analyse the different uncertainties that arise from the different model characteristics and definitions of climate indicators. Finally, we will test if the same subsampling strategy can be used for different climate indicators. This will facilitate the use of the subsampling results for a wide number of impact assessment studies.

Show me the data: advances in multi-model benchmarking, assimilation, and forecasting

NASA Astrophysics Data System (ADS)

Dietze, M.; Raiho, A.; Fer, I.; Cowdery, E.; Kooper, R.; Kelly, R.; Shiklomanov, A. N.; Desai, A. R.; Simkins, J.; Gardella, A.; Serbin, S.

2016-12-01

Researchers want their data to inform carbon cycle predictions, but there are considerable bottlenecks between data collection and the use of data to calibrate and validate earth system models and inform predictions. This talk highlights recent advancements in the PEcAn project aimed at it making it easier for individual researchers to confront models with their own data: (1) The development of an easily extensible site-scale benchmarking system aimed at ensuring that models capture process rather than just reproducing pattern; (2) Efficient emulator-based Bayesian parameter data assimilation to constrain model parameters; (3) A novel, generalized approach to ensemble data assimilation to estimate carbon pools and fluxes and quantify process error; (4) automated processing and downscaling of CMIP climate scenarios to support forecasts that include driver uncertainty; (5) a large expansion in the number of models supported, with new tools for conducting multi-model and multi-site analyses; and (6) a network-based architecture that allows analyses to be shared with model developers and other collaborators. Application of these methods is illustrated with data across a wide range of time scales, from eddy-covariance to forest inventories to tree rings to paleoecological pollen proxies.

Assessing the potential impact and uncertainty of climate, land use change and demographic trends on malaria transmission in Africa by 2050.

NASA Astrophysics Data System (ADS)

Tompkins, Adrian; Caporaso, Luca; Colon-Gonzalez, Felipe

2014-05-01

Previous analyses of data has shown that in addition to variability and longer term trends in climate variables, both land use change (LUC) and population mobility and urbanisation trends can impact malaria transmission intensities and socio-economic burden. With the new regional VECTRI dynamical malaria model it is now possible to examine these in an integrated modelling framework. Using 5 global climate models which were bias corrected using the WATCH data for the recent ISIMIP project, the four Representative Concentration Pathways (RCP), population projections disaggregated from the Shared Socioeconomic Pathways (SSP) and Land use change from the HYDE model output used in the CMIP5 process, we construct a multi-member ensemble of malaria transmission intensity projections for 2050. The ensemble integrations indicate that climate has the leading impact on malaria changes, but that population growth and urbanisation can offset the effect of climate locally. LUC impacts can also be significant on the local scale but their assessment is highly uncertain and only indicative in this study. It is argued that the study should be repeated with a range of malaria models or VECTRI configurations in order to assess the additional uncertainty due to the malaria model assumptions.

Scenario and modelling uncertainty in global mean temperature change derived from emission-driven global climate models

NASA Astrophysics Data System (ADS)

Booth, B. B. B.; Bernie, D.; McNeall, D.; Hawkins, E.; Caesar, J.; Boulton, C.; Friedlingstein, P.; Sexton, D. M. H.

2013-04-01

We compare future changes in global mean temperature in response to different future scenarios which, for the first time, arise from emission-driven rather than concentration-driven perturbed parameter ensemble of a global climate model (GCM). These new GCM simulations sample uncertainties in atmospheric feedbacks, land carbon cycle, ocean physics and aerosol sulphur cycle processes. We find broader ranges of projected temperature responses arising when considering emission rather than concentration-driven simulations (with 10-90th percentile ranges of 1.7 K for the aggressive mitigation scenario, up to 3.9 K for the high-end, business as usual scenario). A small minority of simulations resulting from combinations of strong atmospheric feedbacks and carbon cycle responses show temperature increases in excess of 9 K (RCP8.5) and even under aggressive mitigation (RCP2.6) temperatures in excess of 4 K. While the simulations point to much larger temperature ranges for emission-driven experiments, they do not change existing expectations (based on previous concentration-driven experiments) on the timescales over which different sources of uncertainty are important. The new simulations sample a range of future atmospheric concentrations for each emission scenario. Both in the case of SRES A1B and the Representative Concentration Pathways (RCPs), the concentration scenarios used to drive GCM ensembles, lies towards the lower end of our simulated distribution. This design decision (a legacy of previous assessments) is likely to lead concentration-driven experiments to under-sample strong feedback responses in future projections. Our ensemble of emission-driven simulations span the global temperature response of the CMIP5 emission-driven simulations, except at the low end. Combinations of low climate sensitivity and low carbon cycle feedbacks lead to a number of CMIP5 responses to lie below our ensemble range. The ensemble simulates a number of high-end responses which lie above the CMIP5 carbon cycle range. These high-end simulations can be linked to sampling a number of stronger carbon cycle feedbacks and to sampling climate sensitivities above 4.5 K. This latter aspect highlights the priority in identifying real-world climate-sensitivity constraints which, if achieved, would lead to reductions on the upper bound of projected global mean temperature change. The ensembles of simulations presented here provides a framework to explore relationships between present-day observables and future changes, while the large spread of future-projected changes highlights the ongoing need for such work.

High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6

DOE PAGES

Haarsma, Reindert J.; Roberts, Malcolm J.; Vidale, Pier Luigi; ...

2016-11-22

Robust projections and predictions of climate variability and change, particularly at regional scales, rely on the driving processes being represented with fidelity in model simulations. The role of enhanced horizontal resolution in improved process representation in all components of the climate system is of growing interest, particularly as some recent simulations suggest both the possibility of significant changes in large-scale aspects of circulation as well as improvements in small-scale processes and extremes. However, such high-resolution global simulations at climate timescales, with resolutions of at least 50 km in the atmosphere and 0.25° in the ocean, have been performed at relativelymore » few research centres and generally without overall coordination, primarily due to their computational cost. Assessing the robustness of the response of simulated climate to model resolution requires a large multi-model ensemble using a coordinated set of experiments. The Coupled Model Intercomparison Project 6 (CMIP6) is the ideal framework within which to conduct such a study, due to the strong link to models being developed for the CMIP DECK experiments and other model intercomparison projects (MIPs). Increases in high-performance computing (HPC) resources, as well as the revised experimental design for CMIP6, now enable a detailed investigation of the impact of increased resolution up to synoptic weather scales on the simulated mean climate and its variability. The High Resolution Model Intercomparison Project (HighResMIP) presented in this paper applies, for the first time, a multi-model approach to the systematic investigation of the impact of horizontal resolution. A coordinated set of experiments has been designed to assess both a standard and an enhanced horizontal-resolution simulation in the atmosphere and ocean. The set of HighResMIP experiments is divided into three tiers consisting of atmosphere-only and coupled runs and spanning the period 1950–2050, with the possibility of extending to 2100, together with some additional targeted experiments. This paper describes the experimental set-up of HighResMIP, the analysis plan, the connection with the other CMIP6 endorsed MIPs, as well as the DECK and CMIP6 historical simulations. Lastly, HighResMIP thereby focuses on one of the CMIP6 broad questions, “what are the origins and consequences of systematic model biases?”, but we also discuss how it addresses the World Climate Research Program (WCRP) grand challenges.« less

Clustering of European winter storms: A multi-model perspective

NASA Astrophysics Data System (ADS)

Renggli, Dominik; Buettner, Annemarie; Scherb, Anke; Straub, Daniel; Zimmerli, Peter

2016-04-01

The storm series over Europe in 1990 (Daria, Vivian, Wiebke, Herta) and 1999 (Anatol, Lothar, Martin) are very well known. Such clusters of severe events strongly affect the seasonally accumulated damage statistics. The (re)insurance industry has quantified clustering by using distribution assumptions deduced from the historical storm activity of the last 30 to 40 years. The use of storm series simulated by climate models has only started recently. Climate model runs can potentially represent 100s to 1000s of years, allowing a more detailed quantification of clustering than the history of the last few decades. However, it is unknown how sensitive the representation of clustering is to systematic biases. Using a multi-model ensemble allows quantifying that uncertainty. This work uses CMIP5 decadal ensemble hindcasts to study clustering of European winter storms from a multi-model perspective. An objective identification algorithm extracts winter storms (September to April) in the gridded 6-hourly wind data. Since the skill of European storm predictions is very limited on the decadal scale, the different hindcast runs are interpreted as independent realizations. As a consequence, the available hindcast ensemble represents several 1000 simulated storm seasons. The seasonal clustering of winter storms is quantified using the dispersion coefficient. The benchmark for the decadal prediction models is the 20th Century Reanalysis. The decadal prediction models are able to reproduce typical features of the clustering characteristics observed in the reanalysis data. Clustering occurs in all analyzed models over the North Atlantic and European region, in particular over Great Britain and Scandinavia as well as over Iberia (i.e. the exit regions of the North Atlantic storm track). Clustering is generally weaker in the models compared to reanalysis, although the differences between different models are substantial. In contrast to existing studies, clustering is driven by weak and moderate events, and not by extreme storms. Thus, the decision which climate model to use to quantify clustering can have a substantial impact on the risk assessment in the (re)insurance business.

Multi-centennial upper-ocean heat content reconstruction using online data assimilation

NASA Astrophysics Data System (ADS)

Perkins, W. A.; Hakim, G. J.

2017-12-01

The Last Millennium Reanalysis (LMR) provides an advanced paleoclimate ensemble data assimilation framework for multi-variate climate field reconstructions over the Common Era. Although reconstructions in this framework with full Earth system models remain prohibitively expensive, recent work has shown improved ensemble reconstruction validation using computationally inexpensive linear inverse models (LIMs). Here we leverage these techniques in pursuit of a new multi-centennial field reconstruction of upper-ocean heat content (OHC), synthesizing model dynamics with observational constraints from proxy records. OHC is an important indicator of internal climate variability and responds to planetary energy imbalances. Therefore, a consistent extension of the OHC record in time will help inform aspects of low-frequency climate variability. We use the Community Climate System Model version 4 (CCSM4) and Max Planck Institute (MPI) last millennium simulations to derive the LIMs, and the PAGES2K v.2.0 proxy database to perform annually resolved reconstructions of upper-OHC, surface air temperature, and wind stress over the last 500 years. Annual OHC reconstructions and uncertainties for both the global mean and regional basins are compared against observational and reanalysis data. We then investigate differences in dynamical behavior at decadal and longer time scales between the reconstruction and simulations in the last-millennium Coupled Model Intercomparison Project version 5 (CMIP5). Preliminary investigation of 1-year forecast skill for an OHC-only LIM shows largely positive spatial grid point local anomaly correlations (LAC) with a global average LAC of 0.37. Compared to 1-year OHC persistence forecast LAC (global average LAC of 0.30), the LIM outperforms the persistence forecasts in the tropical Indo-Pacific region, the equatorial Atlantic, and in certain regions near the Antarctic Circumpolar Current. In other regions, the forecast correlations are less than the persistence case but still positive overall.

The ENSO Effects on Tropical Clouds and Top-of-Atmosphere Cloud Radiative Effects in CMIP5 Models

NASA Technical Reports Server (NTRS)

Su, Wenying; Wang, Hailan

2015-01-01

The El Nino-Southern Oscillation (ENSO) effects on tropical clouds and top-of-atmosphere (TOA) cloud radiative effects (CREs) in Coupled Model Intercomparison Project Phase5 (CMIP5) models are evaluated using satellite-based observations and International Satellite Cloud Climatology Project satellite simulator output. Climatologically, most CMIP5 models produce considerably less total cloud amount with higher cloud top and notably larger reflectivity than observations in tropical Indo-Pacific (60 degrees East - 200 degrees East; 10 degrees South - 10 degrees North). During ENSO, most CMIP5 models considerably underestimate TOA CRE and cloud changes over western tropical Pacific. Over central tropical Pacific, while the multi-model mean resembles observations in TOA CRE and cloud amount anomalies, it notably overestimates cloud top pressure (CTP) decreases; there are also substantial inter-model variations. The relative effects of changes in cloud properties, temperature and humidity on TOA CRE anomalies during ENSO in the CMIP5 models are assessed using cloud radiative kernels. The CMIP5 models agree with observations in that their TOA shortwave CRE anomalies are primarily contributed by total cloud amount changes, and their TOA longwave CRE anomalies are mostly contributed by changes in both total cloud amount and CTP. The model biases in TOA CRE anomalies particularly the strong underestimations over western tropical Pacific are, however, mainly explained by model biases in CTP and cloud optical thickness (tau) changes. Despite the distinct model cloud biases particularly in tau regime, the TOA CRE anomalies from cloud amount changes are comparable between the CMIP5 models and observations, because of the strong compensations between model underestimation of TOA CRE anomalies from thin clouds and overestimation from medium and thick clouds.

21st Century Changes in Precipitation Extremes Over the United States: Can Climate Analogues Help or Hinder?

NASA Astrophysics Data System (ADS)

Gao, X.; Schlosser, C. A.

2013-12-01

Global warming is expected to alter the frequency and/or magnitude of extreme precipitation events. Such changes could have substantial ecological, economic, and sociological consequences. However, climate models in general do not correctly reproduce the frequency and intensity distribution of precipitation, especially at the regional scale. In this study, gridded data from a dense network of surface precipitation gauges and a global atmospheric analysis at a coarser scale are combined to develop a diagnostic framework for the large-scale meteorological conditions (i.e. flow features, moisture supply) that dominate during extreme precipitation. Such diagnostic framework is first evaluated with the late 20th century simulations from an ensemble of climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), and is found to produce more consistent (and less uncertain) total and interannaul number of extreme days with the observations than the model-based precipitation over the south-central United States and the Western United States examined in this study. The framework is then applied to the CMIP5 multi-model projections for two radiative forcing scenarios (Representative Concentration Pathways 4.5 and 8.5) to assess the potential future changes in the probability of precipitation extremes over the same study regions. We further analyze the accompanying circulation features and their changes that may be responsible for shifts in extreme precipitation in response to changed climate. The results from this study may guide hazardous weather watches and help society develop adaptive strategies for preventing catastrophic losses.

Is Polar Amplification Deeper and Stronger than Dynamicists Assume?

NASA Astrophysics Data System (ADS)

Scheff, J.; Maroon, E.

2017-12-01

In the CMIP multi-model mean under strong future warming, Arctic amplification is confined to the lower troposphere, so that the meridional gradient of warming reverses around 500 mb and the upper troposphere is characterized by strong "tropical amplification" in which warming weakens with increasing latitude. This model-derived pattern of warming maxima in the upper-level tropics and lower-level Arctic has become a canonical assumption driving theories of the large-scale circulation response to climate change. Yet, several lines of evidence and reasoning suggest that Arctic amplification may in fact extend through the entire depth of the troposphere, and/or may be stronger than commonly modeled. These include satellite Microwave Sounding Unit (MSU) temperature trends as a function of latitude and vertical level, the recent discovery that the extratropical negative cloud phase feedback in models is largely spurious, and the very strong polar amplification observed in past warm and lukewarm climates. Such a warming pattern, with deep, dominant Arctic amplification, would have very different implications for the circulation than a canonical CMIP-like warming: instead of slightly shifting poleward and strengthening, eddies, jets and cells might shift equatorward and considerably weaken. Indeed, surface winds have been mysteriously weakening ("stilling") at almost all stations over the last half-century or so, there has been no poleward shift in northern hemisphere circulation metrics, and past warm climates' subtropics were apparently quite wet (and their global ocean circulations were weak.) To explore these possibilities more deeply, we examine the y-z structure of warming and circulation changes across a much broader range of models, scenarios and time periods than the CMIP future mean, and use an MSU simulator to compare them to the satellite warming record. Specifically, we examine whether the use of historical (rather than future) forcing, AMIP (rather than CMIP) configuration, individual GCMs, and/or individual ensemble members can better reproduce the structure of the MSU and surface-wind observations. Figure 1 already shows that tropical amplification is absent in the CESM1 historical ensemble (1979-2012). The results of these analyses will guide our future modeling work on these topics.

The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

DOE PAGES

Boer, George J.; Smith, Douglas M.; Cassou, Christophe; ...

2016-01-01

The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Predictionmore » (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Furthermore, groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.« less

The analyses of extreme climate events over China based on CMIP5 historical and future simulations

NASA Astrophysics Data System (ADS)

Yang, S.; Dong, W.; Feng, J.; Chou, J.

2013-12-01

The extreme climate events have a serious influence on human society. Based on observations and 12 simulations from Coupled Model Intercomparison Project Phase 5 (CMIP5), Climatic extremes and their changes over china in history and future scenarios of three Representative Concentration Pathways (RCPs) are analyzed. Because of the background of global warming, in observations, the frost days (FD) and low-temperature threshold days (TN10P) have decreasing trend, and summer days (SU), high-temperature threshold days (TX90P), the heavy precipitation days (R20) and contribution of heavy precipitation days (P95T) show an increasing trend. Most coupled models can basically simulate main characteristics of most extreme indexes. The models reproduce the mean FD and TX90P value best and can give basic trends of the FD, TN10P, SU and TX90P. High correlation coefficients between simulated results and observation are found in FD, SU and P95T. For FD and SU index, most of the models have good ability to capture the spatial differences between the mean state of the 1986-2005 and 1961-1980 periods, but for other indexes, most of models' simulation ability for spatial disparity are not so satisfactory and have to be promoted. Under the high emission scenario of RCP8.5, the century-scale linear changes of Multi-Model Ensembles (MME) for FD, SU, TN10P, TX90P, R20 and P95T are -46.9, 46.0, -27.1, 175.4, 2.9 days and 9.9%, respectively. Due to the complexities of physical process parameterizations and the limitation of forcing data, a large uncertainty still exists in the simulations of climatic extremes. Fig.1 Observed and modeled multi-year average for each index (Dotted line: observation) Table1. Extreme index definition

Intercomparison of terrestrial carbon fluxes and carbon use efficiency simulated by CMIP5 Earth System Models

NASA Astrophysics Data System (ADS)

Kim, Dongmin; Lee, Myong-In; Jeong, Su-Jong; Im, Jungho; Cha, Dong Hyun; Lee, Sanggyun

2017-12-01

This study compares historical simulations of the terrestrial carbon cycle produced by 10 Earth System Models (ESMs) that participated in the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Using MODIS satellite estimates, this study validates the simulation of gross primary production (GPP), net primary production (NPP), and carbon use efficiency (CUE), which depend on plant function types (PFTs). The models show noticeable deficiencies compared to the MODIS data in the simulation of the spatial patterns of GPP and NPP and large differences among the simulations, although the multi-model ensemble (MME) mean provides a realistic global mean value and spatial distributions. The larger model spreads in GPP and NPP compared to those of surface temperature and precipitation suggest that the differences among simulations in terms of the terrestrial carbon cycle are largely due to uncertainties in the parameterization of terrestrial carbon fluxes by vegetation. The models also exhibit large spatial differences in their simulated CUE values and at locations where the dominant PFT changes, primarily due to differences in the parameterizations. While the MME-simulated CUE values show a strong dependence on surface temperatures, the observed CUE values from MODIS show greater complexity, as well as non-linear sensitivity. This leads to the overall underestimation of CUE using most of the PFTs incorporated into current ESMs. The results of this comparison suggest that more careful and extensive validation is needed to improve the terrestrial carbon cycle in terms of ecosystem-level processes.

Uncertainties in Projecting Future Changes in Atmospheric Rivers and Their Impacts on Heavy Precipitation over Europe

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Yang; Lu, Jian; Leung, L. Ruby

This study investigates the North Atlantic atmospheric rivers (ARs) making landfall over western Europe in the present and future climate from the multi-model ensemble of the Coupled Model Intercomparison Project Phase 5 (CMIP5). Overall, CMIP5 captures the seasonal and spatial variations of historical landfalling AR days, with the large inter-model variability strongly correlated with the inter-model spread of historical jet position. Under RCP 8.5, AR frequency is projected to increase a few times by the end of this century. While thermodynamics plays a dominate role in the future increase of ARs, wind changes associated with the midlatitude jet shifts alsomore » significantly contribute to AR changes, resulting in dipole change patterns in all seasons. In the North Atlantic, the model projected jet shifts are strongly correlated with the simulated historical jet position. As models exhibit predominantly equatorward biases in the historical jet position, the large poleward jet shifts reduce AR days south of the historical mean jet position through the dynamical connections between the jet positions and AR days. Using the observed historical jet position as an emergent constraint, dynamical effects further increase AR days in the future above the large increases due to thermodynamical effects. In the future, both total and extreme precipitation induced by AR contribute more to the seasonal mean and extreme precipitation compared to present primarily because of the increase in AR frequency. While AR precipitation intensity generally increases more relative to the increase in integrated vapor transport, AR extreme precipitation intensity increases much less.« less

Costa Rica Rainfall in Future Climate Change Scenarios

NASA Astrophysics Data System (ADS)

Castillo Rodriguez, R. A., Sr.; Amador, J. A.; Duran-Quesada, A. M.

2017-12-01

Studies of intraseasonal and annual cycles of meteorological variables, using projections of climate change, are nowadays extremely important to improve regional socio-economic planning for countries. This is particularly true in Costa Rica, as Central America has been identified as a climate change hot spot. Today many of the economic activities in the region, especially those related to agriculture, tourism and hydroelectric power generation are linked to the seasonal cycle of precipitation. Changes in rainfall (mm/day) and in the diurnal temperature range (°C) for the periods 1950-2005 and 2006-2100 were investigated using the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) constructed using the CMIP5 (Coupled Model Intercomparison Project version 5) data. Differences between the multi-model ensembles of the two prospective scenarios (RCP 4.5 and RCP 8.5) and the retrospective baseline scenario were computed. This study highlights Costa Rica as an inflexion point of the climate change in the region and also suggests future drying conditions.

What Controls ENSO-Amplitude Diversity in Climate Models?

NASA Astrophysics Data System (ADS)

Wengel, C.; Dommenget, D.; Latif, M.; Bayr, T.; Vijayeta, A.

2018-02-01

Climate models depict large diversity in the strength of the El Niño/Southern Oscillation (ENSO) (ENSO amplitude). Here we investigate ENSO-amplitude diversity in the Coupled Model Intercomparison Project Phase 5 (CMIP5) by means of the linear recharge oscillator model, which reduces ENSO dynamics to a two-dimensional problem in terms of eastern equatorial Pacific sea surface temperature anomalies (T) and equatorial Pacific upper ocean heat content anomalies (h). We find that a large contribution to ENSO-amplitude diversity originates from stochastic forcing. Further, significant interactions exist between the stochastic forcing and the growth rates of T and h with competing effects on ENSO amplitude. The joint consideration of stochastic forcing and growth rates explains more than 80% of the ENSO-amplitude variance within CMIP5. Our results can readily explain the lack of correlation between the Bjerknes Stability index, a measure of the growth rate of T, and ENSO amplitude in a multimodel ensemble.

Climate Projections and Drought: Verification for the Colorado River Basin

NASA Astrophysics Data System (ADS)

Santos, N. I.; Piechota, T. C.; Miller, W. P.; Ahmad, S.

2017-12-01

The Colorado River Basin has experienced the driest 17 year period (2000-2016) in over 100 years of historical record keeping. While the Colorado River reservoir system began the current drought at near 100% capacity, reservoir storage has fallen to just above 50% during the drought. Even though federal and state water agencies have worked together to mitigate the impact of the drought and have collaboratively sponsored conservation programs and drought contingency plans, the 17-years of observed data beg the question as to whether the most recent climate projections would have been able to project the current drought's severity. The objective of this study is to analyze observations and ensemble projections (e.g. temperature, precipitation, streamflow) from the CMIP3 and CMIP5 archive in the Colorado River Basin and compare metrics related to skill scores, the Palmer Drought Severity Index, and water supply sustainability index. Furthermore, a sub-ensemble of CMIP3/CMIP5 projections, developed using a teleconnection replication verification technique developed by the author, will also be compared to the observed record to assist in further validating the technique as a usable process to increase skill in climatological projections. In the end, this study will assist to better inform water resource managers about the ability of climate ensembles to project hydroclimatic variability and the appearance of decadal drought periods.

A Scaling Model for the Anthropocene Climate Variability with Projections to 2100

NASA Astrophysics Data System (ADS)

Hébert, Raphael; Lovejoy, Shaun

2017-04-01

The determination of the climate sensitivity to radiative forcing is a fundamental climate science problem with important policy implications. We use a scaling model, with a limited set of parameters, which can directly calculate the forced globally-average surface air temperature response to anthropogenic and natural forcings. At timescales larger than an inner scale τ, which we determine as the ocean-atmosphere coupling scale at around 2 years, the global system responds, approximately, linearly, so that the variability may be decomposed into additive forced and internal components. The Ruelle response theory extends the classical linear response theory for small perturbations to systems far from equilibrium. Our model thus relates radiative forcings to a forced temperature response by convolution with a suitable Green's function, or climate response function. Motivated by scaling symmetries which allow for long range dependence, we assume a general scaling form, a scaling climate response function (SCRF) which is able to produce a wide range of responses: a power-law truncated at τ. This allows us to analytically calculate the climate sensitivity at different time scales, yielding a one-to-one relation from the transient climate response to the equilibrium climate sensitivity which are estimated, respectively, as 1.6+0.3-0.2K and 2.4+1.3-0.6K at the 90 % confidence level. The model parameters are estimated within a Bayesian framework, with a fractional Gaussian noise error model as the internal variability, from forcing series, instrumental surface temperature datasets and CMIP5 GCMs Representative Concentration Pathways (RCP) scenario runs. This observation based model is robust and projections for the coming century are made following the RCP scenario 2.6, 4.5 and 8.5, yielding in the year 2100, respectively : 1.5 +0.3)_{-0.2K, 2.3 ± 0.4 K and 4.0 ± 0.6 K at the 90 % confidence level. For comparison, the associated projections from a CMIP5 multi-model ensemble(MME) (32 models) are: 1.7 ± 0.8 K, 2.6 ± 0.8 K and 4.8 ± 1.3 K. Therefore, our projection uncertainty is less than half the structural uncertainty of this CMIP5 MME.

An integrated assessment modeling framework for uncertainty studies in global and regional climate change: the MIT IGSM-CAM (version 1.0)

NASA Astrophysics Data System (ADS)

Monier, E.; Scott, J. R.; Sokolov, A. P.; Forest, C. E.; Schlosser, C. A.

2013-12-01

This paper describes a computationally efficient framework for uncertainty studies in global and regional climate change. In this framework, the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM), an integrated assessment model that couples an Earth system model of intermediate complexity to a human activity model, is linked to the National Center for Atmospheric Research (NCAR) Community Atmosphere Model (CAM). Since the MIT IGSM-CAM framework (version 1.0) incorporates a human activity model, it is possible to analyze uncertainties in emissions resulting from both uncertainties in the underlying socio-economic characteristics of the economic model and in the choice of climate-related policies. Another major feature is the flexibility to vary key climate parameters controlling the climate system response to changes in greenhouse gases and aerosols concentrations, e.g., climate sensitivity, ocean heat uptake rate, and strength of the aerosol forcing. The IGSM-CAM is not only able to realistically simulate the present-day mean climate and the observed trends at the global and continental scale, but it also simulates ENSO variability with realistic time scales, seasonality and patterns of SST anomalies, albeit with stronger magnitudes than observed. The IGSM-CAM shares the same general strengths and limitations as the Coupled Model Intercomparison Project Phase 3 (CMIP3) models in simulating present-day annual mean surface temperature and precipitation. Over land, the IGSM-CAM shows similar biases to the NCAR Community Climate System Model (CCSM) version 3, which shares the same atmospheric model. This study also presents 21st century simulations based on two emissions scenarios (unconstrained scenario and stabilization scenario at 660 ppm CO2-equivalent) similar to, respectively, the Representative Concentration Pathways RCP8.5 and RCP4.5 scenarios, and three sets of climate parameters. Results of the simulations with the chosen climate parameters provide a good approximation for the median, and the 5th and 95th percentiles of the probability distribution of 21st century changes in global mean surface air temperature from previous work with the IGSM. Because the IGSM-CAM framework only considers one particular climate model, it cannot be used to assess the structural modeling uncertainty arising from differences in the parameterization suites of climate models. However, comparison of the IGSM-CAM projections with simulations of 31 CMIP5 models under the RCP4.5 and RCP8.5 scenarios show that the range of warming at the continental scale shows very good agreement between the two ensemble simulations, except over Antarctica, where the IGSM-CAM overestimates the warming. This demonstrates that by sampling the climate system response, the IGSM-CAM, even though it relies on one single climate model, can essentially reproduce the range of future continental warming simulated by more than 30 different models. Precipitation changes projected in the IGSM-CAM simulations and the CMIP5 multi-model ensemble both display a large uncertainty at the continental scale. The two ensemble simulations show good agreement over Asia and Europe. However, the ranges of precipitation changes do not overlap - but display similar size - over Africa and South America, two continents where models generally show little agreement in the sign of precipitation changes and where CCSM3 tends to be an outlier. Overall, the IGSM-CAM provides an efficient and consistent framework to explore the large uncertainty in future projections of global and regional climate change associated with uncertainty in the climate response and projected emissions.

Non-stationary Return Levels of CMIP5 Multi-model Temperature Extremes

DOE PAGES

Cheng, L.; Phillips, T. J.; AghaKouchak, A.

2015-05-01

The objective of this study is to evaluate to what extent the CMIP5 climate model simulations of the climate of the twentieth century can represent observed warm monthly temperature extremes under a changing environment. The biases and spatial patterns of 2-, 10-, 25-, 50- and 100-year return levels of the annual maxima of monthly mean temperature (hereafter, annual temperature maxima) from CMIP5 simulations are compared with those of Climatic Research Unit (CRU) observational data considered under a non-stationary assumption. The results show that CMIP5 climate models collectively underestimate the mean annual maxima over arid and semi-arid regions that are mostmore » subject to severe heat waves and droughts. Furthermore, the results indicate that most climate models tend to underestimate the historical annual temperature maxima over the United States and Greenland, while generally disagreeing in their simulations over cold regions. Return level analysis shows that with respect to the spatial patterns of the annual temperature maxima, there are good agreements between the CRU observations and most CMIP5 simulations. However, the magnitudes of the simulated annual temperature maxima differ substantially across individual models. Discrepancies are generally larger over higher latitudes and cold regions.« less

Abrupt cooling over the North Atlantic in modern climate models

PubMed Central

Sgubin, Giovanni; Swingedouw, Didier; Drijfhout, Sybren; Mary, Yannick; Bennabi, Amine

2017-01-01

Observations over the 20th century evidence no long-term warming in the subpolar North Atlantic (SPG). This region even experienced a rapid cooling around 1970, raising a debate over its potential reoccurrence. Here we assess the risk of future abrupt SPG cooling in 40 climate models from the fifth Coupled Model Intercomparison Project (CMIP5). Contrary to the long-term SPG warming trend evidenced by most of the models, 17.5% of the models (7/40) project a rapid SPG cooling, consistent with a collapse of the local deep-ocean convection. Uncertainty in projections is associated with the models' varying capability in simulating the present-day SPG stratification, whose realistic reproduction appears a necessary condition for the onset of a convection collapse. This event occurs in 45.5% of the 11 models best able to simulate the observed SPG stratification. Thus, due to systematic model biases, the CMIP5 ensemble as a whole underestimates the chance of future abrupt SPG cooling, entailing crucial implications for observation and adaptation policy. PMID:28198383

Constraints on High Northern Photosynthesis Increase Using Earth System Models and a Set of Independent Observations

NASA Astrophysics Data System (ADS)

Winkler, A. J.; Brovkin, V.; Myneni, R.; Alexandrov, G.

2017-12-01

Plant growth in the northern high latitudes benefits from increasing temperature (radiative effect) and CO2 fertilization as a consequence of rising atmospheric CO2 concentration. This enhanced gross primary production (GPP) is evident in large scale increase in summer time greening over the 36-year record of satellite observations. In this time period also various global ecosystem models simulate a greening trend in terms of increasing leaf area index (LAI). We also found a persistent greening trend analyzing historical simulations of Earth system models (ESM) participating in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). However, these models span a large range in strength of the LAI trend, expressed as sensitivity to both key environmental factors, temperature and CO2 concentration. There is also a wide spread in magnitude of the associated increase of terrestrial GPP among the ESMs, which contributes to pronounced uncertainties in projections of future climate change. Here we demonstrate that there is a linear relationship across the CMIP5 model ensemble between projected GPP changes and historical LAI sensitivity, which allows using the observed LAI sensitivity as an "emerging constraint" on GPP estimation at future CO2 concentration. This constrained estimate of future GPP is substantially higher than the traditional multi-model mean suggesting that the majority of current ESMs may be significantly underestimating carbon fixation by vegetation in NHL. We provide three independent lines of evidence in analyzing observed and simulated CO2 amplitude as well as atmospheric CO2 inversion products to arrive at the same conclusion.

Inconsistent Strategies to Spin up Models in CMIP5: Implications for Ocean Biogeochemical Model Performance Assessment

NASA Technical Reports Server (NTRS)

Seferian, Roland; Gehlen, Marion; Bopp, Laurent; Resplandy, Laure; Orr, James C.; Marti, Olivier; Dunne, John P.; Christian, James R.; Doney, Scott C.; Ilyina, Tatiana;

Evaluation of CMIP5 models for projection of future precipitation change in Bornean tropical rainforests

NASA Astrophysics Data System (ADS)

Hussain, Mubasher; Yusof, Khamaruzaman Wan; Mustafa, Muhammad Raza Ul; Mahmood, Rashid; Jia, Shaofeng

2017-10-01

We present the climate change impact on the annual and seasonal precipitation over Rajang River Basin (RRB) in Sarawak by employing a set of models from Coupled Model Intercomparison Project Phase 5 (CMIP5). Based on the capability to simulate the historical precipitation, we selected the three most suitable GCMs (i.e. ACCESS1.0, ACCESS1.3, and GFDL-ESM2M) and their mean ensemble (B3MMM) was used to project the future precipitation over the RRB. Historical (1976-2005) and future (2011-2100) precipitation ensembles of B3MMM were used to perturb the stochastically generated future precipitation over 25 rainfall stations in the river basin. The B3MMM exhibited a significant increase in precipitation during 2080s, up to 12 and 8% increase in annual precipitation over upper and lower RRB, respectively, under RCP8.5, and up to 7% increase in annual precipitation under RCP4.5. On the seasonal scale, Mann-Kendal trend test estimated statistically significant positive trend in the future precipitation during all seasons; except September to November when we only noted significant positive trend for the lower RRB under RCP4.5. Overall, at the end of the twenty-first century, an increase in annual precipitation is noteworthy in the whole RRB, with 7 and 10% increase in annual precipitation under the RCP4.5 and the RCP8.5, respectively.

Projected wave conditions in the Eastern North Pacific under the influence of two CMIP5 climate scenarios

USGS Publications Warehouse

Erikson, Li H.; Hegermiller, Christie; Barnard, Patrick; Ruggiero, Peter; van Ormondt, Martin

2015-01-01

Hindcast and 21st century winds, simulated by General Circulation Models (GCMs), were used to drive global- and regional-scale spectral wind-wave generation models in the Pacific Ocean Basin to assess future wave conditions along the margins of the North American west coast and Hawaiian Islands. Three-hourly winds simulated by four separate GCMs were used to generate an ensemble of wave conditions for a recent historical time-period (1976–2005) and projections for the mid and latter parts of the 21st century under two radiative forcing scenarios (RCP 4.5 and RCP 8.5), as defined by the fifth phase of the Coupled Model Inter-comparison Project (CMIP5) experiments. Comparisons of results from historical simulations with wave buoy and ERA-Interim wave reanalysis data indicate acceptable model performance of wave heights, periods, and directions, giving credence to generating projections. Mean and extreme wave heights are projected to decrease along much of the North American west coast. Extreme wave heights are projected to decrease south of ∼50°N and increase to the north, whereas extreme wave periods are projected to mostly increase. Incident wave directions associated with extreme wave heights are projected to rotate clockwise at the eastern end of the Aleutian Islands and counterclockwise offshore of Southern California. Local spatial patterns of the changing wave climate are similar under the RCP 4.5 and RCP 8.5 scenarios, but stronger magnitudes of change are projected under RCP 8.5. Findings of this study are similar to previous work using CMIP3 GCMs that indicates decreasing mean and extreme wave conditions in the Eastern North Pacific, but differ from other studies with respect to magnitude and local patterns of change. This study contributes toward a larger ensemble of global and regional climate projections needed to better assess uncertainty of potential future wave climate change, and provides model boundary conditions for assessing the impacts of climate change on coastal systems.

The CESM Large Ensemble Project: Inspiring New Ideas and Understanding

NASA Astrophysics Data System (ADS)

Kay, J. E.; Deser, C.

2016-12-01

While internal climate variability is known to affect climate projections, its influence is often underappreciated and confused with model error. Why? In general, modeling centers contribute a small number of realizations to international climate model assessments [e.g., phase 5 of the Coupled Model Intercomparison Project (CMIP5)]. As a result, model error and internal climate variability are difficult, and at times impossible, to disentangle. In response, the Community Earth System Model (CESM) community designed the CESM Large Ensemble (CESM-LE) with the explicit goal of enabling assessment of climate change in the presence of internal climate variability. All CESM-LE simulations use a single CMIP5 model (CESM with the Community Atmosphere Model, version 5). The core simulations replay the twenty to twenty-first century (1920-2100) 40+ times under historical and representative concentration pathway 8.5 external forcing with small initial condition differences. Two companion 2000+-yr-long preindustrial control simulations (fully coupled, prognostic atmosphere and land only) allow assessment of internal climate variability in the absence of climate change. Comprehensive outputs, including many daily fields, are available as single-variable time series on the Earth System Grid for anyone to use. Examples of scientists and stakeholders that are using the CESM-LE outputs to help interpret the observational record, to understand projection spread and to plan for a range of possible futures influenced by both internal climate variability and forced climate change will be highlighted the presentation.

Multi-component ensembles of future meteorological and natural snow conditions for 1500 m altitude in the Chartreuse mountain range, Northern French Alps

NASA Astrophysics Data System (ADS)

Verfaillie, Deborah; Lafaysse, Matthieu; Déqué, Michel; Eckert, Nicolas; Lejeune, Yves; Morin, Samuel

2018-04-01

This article investigates the climatic response of a series of indicators for characterizing annual snow conditions and corresponding meteorological drivers at 1500 m altitude in the Chartreuse mountain range in the Northern French Alps. Past and future changes were computed based on reanalysis and observations from 1958 to 2016, and using CMIP5-EURO-CORDEX GCM-RCM pairs spanning historical (1950-2005) and RCP2.6 (4), RCP4.5 and RCP8.5 (13 each) future scenarios (2006-2100). The adjusted climate model runs were used to drive the multiphysics ensemble configuration of the detailed snowpack model Crocus. Uncertainty arising from physical modeling of snow accounts for 20 % typically, although the multiphysics is likely to have a much smaller impact on trends. Ensembles of climate projections are rather similar until the middle of the 21st century, and all show a continuation of the ongoing reduction in average snow conditions, and sustained interannual variability. The impact of the RCPs becomes significant for the second half of the 21st century, with overall stable conditions with RCP2.6, and continued degradation of snow conditions for RCP4.5 and 8.5, the latter leading to more frequent ephemeral snow conditions. Changes in local meteorological and snow conditions show significant correlation with global temperature changes. Global temperature levels 1.5 and 2 °C above preindustrial levels correspond to a 25 and 32 % reduction, respectively, of winter mean snow depth with respect to the reference period 1986-2005. Larger reduction rates are expected for global temperature levels exceeding 2 °C. The method can address other geographical areas and sectorial indicators, in the field of water resources, mountain tourism or natural hazards.

Using Ensemble Short-Term Initialized Coupled NASA GEOS5 Climate Model Integrations to Study Convective Bias Growth

NASA Technical Reports Server (NTRS)

Cohen, Charlie; Robertson, Franklin; Molod, Andrea

2014-01-01

The representation of convective processes, particularly deep convection in the tropics, remains a persistent problem in climate models. In fact structural biases in the distribution of tropical rainfall in the CMIP5 models is hardly different than that of the CMIP3 versions. Given that regional climate change at higher latitudes is sensitive to the configuration of tropical forcing, this persistent bias is a major issue for the credibility of climate change projections. In this study we use model output from integrations of the NASA Global Earth Observing System Five (GEOS5) climate modeling system to study the evolution of biases in the location and intensity of convective processes. We take advantage of a series of hindcast experiments done in support of the US North American Multi-Model Ensemble (NMME) initiative. For these experiments a nine-month forecast using a coupled model configuration is made approximately every five days over the past 30 years. Each forecast is started with an updated analysis of the ocean, atmosphere and land states. For a given calendar month we have approximately 180 forecasts with daily means of various quantities. These forecasts can be averaged to essentially remove "weather scales" and highlight systematic errors as they evolve. Our primary question is to ask how the spatial structure of daily mean precipitation over the tropics evolves from the initial state and what physical processes are involved. Errors in parameterized convection, various water and energy fluxes and the divergent circulation are found to set up on fast time scales (order five days) compared to errors in the ocean, although SST changes can be non-negligible over that time. For the month of June the difference between forecast day five versus day zero precipitation looks quite similar to the difference between the June precipitation climatology and that from the Global Precipitation Climatology Project (GPCP). We focus much of our analysis on the influence of SST gradients, associated PBL baroclinicity enabled by turbulent mixing, the ensuing PBL moisture convergence, and how changes in these processes relate to convective precipitation bias growth over this short period.

Using Ensemble Short-Term Initialized Coupled NASA GEOS5 Climate Model Integrations to Study Convective Bias Growth

NASA Astrophysics Data System (ADS)

Robertson, F. R.; Cohen, C.

2014-12-01

The representation of convective processes, particularly deep convection in the tropics, remains a persistent problem in climate models. In fact structural biases in the distribution of tropical rainfall in the CMIP5 models is hardly different than that of the CMIP3 versions. Given that regional climate change at higher latitudes is sensitive to the configuration of tropical forcing, this persistent bias is a major issue for the credibility of climate change projections. In this study we use model output from integrations of the NASA Global Earth Observing System Five (GEOS5) climate modeling system to study the evolution of biases in the location and intensity of convective processes. We take advantage of a series of hindcast experiments done in support of the US North American Multi-Model Ensemble (NMME) initiative. For these experiments a nine-month forecast using a coupled model configuration is made approximately every five days over the past 30 years. Each forecast is started with an updated analysis of the ocean, atmosphere and land states. For a given calendar month we have approximately 180 forecasts with daily means of various quantities. These forecasts can be averaged to essentially remove "weather scales" and highlight systematic errors as they evolve. Our primary question is to ask how the spatial structure of daily mean precipitation over the tropics evolves from the initial state and what physical processes are involved. Errors in parameterized convection, various water and energy fluxes and the divergent circulation are found to set up on fast time scales (order five days) compared to errors in the ocean, although SST changes can be non-negligible over that time. For the month of June the difference between forecast day five versus day zero precipitation looks quite similar to the difference between the June precipitation climatology and that from the Global Precipitation Climatology Project (GPCP). We focus much of our analysis on the influence of SST gradients, associated PBL baroclinicity enabled by turbulent mixing, the ensuing PBL moisture convergence, and how changes in these processes relate to convective precipitation bias growth over this short period.

Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble

NASA Astrophysics Data System (ADS)

Lorenz, Ruth; Argüeso, Daniel; Donat, Markus G.; Pitman, Andrew J.; van den Hurk, Bart; Berg, Alexis; Lawrence, David M.; Chéruy, Frédérique; Ducharne, Agnès.; Hagemann, Stefan; Meier, Arndt; Milly, P. C. D.; Seneviratne, Sonia I.

2016-01-01

We examine how soil moisture variability and trends affect the simulation of temperature and precipitation extremes in six global climate models using the experimental protocol of the Global Land-Atmosphere Coupling Experiment of the Coupled Model Intercomparison Project, Phase 5 (GLACE-CMIP5). This protocol enables separate examinations of the influences of soil moisture variability and trends on the intensity, frequency, and duration of climate extremes by the end of the 21st century under a business-as-usual (Representative Concentration Pathway 8.5) emission scenario. Removing soil moisture variability significantly reduces temperature extremes over most continental surfaces, while wet precipitation extremes are enhanced in the tropics. Projected drying trends in soil moisture lead to increases in intensity, frequency, and duration of temperature extremes by the end of the 21st century. Wet precipitation extremes are decreased in the tropics with soil moisture trends in the simulations, while dry extremes are enhanced in some regions, in particular the Mediterranean and Australia. However, the ensemble results mask considerable differences in the soil moisture trends simulated by the six climate models. We find that the large differences between the models in soil moisture trends, which are related to an unknown combination of differences in atmospheric forcing (precipitation, net radiation), flux partitioning at the land surface, and how soil moisture is parameterized, imply considerable uncertainty in future changes in climate extremes.

Simulation of an ensemble of future climate time series with an hourly weather generator

NASA Astrophysics Data System (ADS)

Caporali, E.; Fatichi, S.; Ivanov, V. Y.; Kim, J.

2010-12-01

There is evidence that climate change is occurring in many regions of the world. The necessity of climate change predictions at the local scale and fine temporal resolution is thus warranted for hydrological, ecological, geomorphological, and agricultural applications that can provide thematic insights into the corresponding impacts. Numerous downscaling techniques have been proposed to bridge the gap between the spatial scales adopted in General Circulation Models (GCM) and regional analyses. Nevertheless, the time and spatial resolutions obtained as well as the type of meteorological variables may not be sufficient for detailed studies of climate change effects at the local scales. In this context, this study presents a stochastic downscaling technique that makes use of an hourly weather generator to simulate time series of predicted future climate. Using a Bayesian approach, the downscaling procedure derives distributions of factors of change for several climate statistics from a multi-model ensemble of GCMs. Factors of change are sampled from their distributions using a Monte Carlo technique to entirely account for the probabilistic information obtained with the Bayesian multi-model ensemble. Factors of change are subsequently applied to the statistics derived from observations to re-evaluate the parameters of the weather generator. The weather generator can reproduce a wide set of climate variables and statistics over a range of temporal scales, from extremes, to the low-frequency inter-annual variability. The final result of such a procedure is the generation of an ensemble of hourly time series of meteorological variables that can be considered as representative of future climate, as inferred from GCMs. The generated ensemble of scenarios also accounts for the uncertainty derived from multiple GCMs used in downscaling. Applications of the procedure in reproducing present and future climates are presented for different locations world-wide: Tucson (AZ), Detroit (MI), and Firenze (Italy). The stochastic downscaling is carried out with eight GCMs from the CMIP3 multi-model dataset (IPCC 4AR, A1B scenario).

Assessment of Arctic and Antarctic Sea Ice Predictability in CMIP5 Decadal Hindcasts

NASA Technical Reports Server (NTRS)

Yang, Chao-Yuan; Liu, Jiping (Inventor); Hu, Yongyun; Horton, Radley M.; Chen, Liqi; Cheng, Xiao

2016-01-01

This paper examines the ability of coupled global climate models to predict decadal variability of Arctic and Antarctic sea ice. We analyze decadal hindcasts/predictions of 11 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Decadal hindcasts exhibit a large multimodel spread in the simulated sea ice extent, with some models deviating significantly from the observations as the predicted ice extent quickly drifts away from the initial constraint. The anomaly correlation analysis between the decadal hindcast and observed sea ice suggests that in the Arctic, for most models, the areas showing significant predictive skill become broader associated with increasing lead times. This area expansion is largely because nearly all the models are capable of predicting the observed decreasing Arctic sea ice cover. Sea ice extent in the North Pacific has better predictive skill than that in the North Atlantic (particularly at a lead time of 3-7 years), but there is a reemerging predictive skill in the North Atlantic at a lead time of 6-8 years. In contrast to the Arctic, Antarctic sea ice decadal hindcasts do not show broad predictive skill at any timescales, and there is no obvious improvement linking the areal extent of significant predictive skill to lead time increase. This might be because nearly all the models predict a retreating Antarctic sea ice cover, opposite to the observations. For the Arctic, the predictive skill of the multi-model ensemble mean outperforms most models and the persistence prediction at longer timescales, which is not the case for the Antarctic. Overall, for the Arctic, initialized decadal hindcasts show improved predictive skill compared to uninitialized simulations, although this improvement is not present in the Antarctic.

Asymmetric response of tropical cyclone activity to global warming over the North Atlantic and western North Pacific from CMIP5 model projections

NASA Astrophysics Data System (ADS)

Park, Doo-Sun R.; Ho, Chang-Hoi; Chan, Johnny C. L.; Ha, Kyung-Ja; Kim, Hyeong-Seog; Kim, Jinwon; Kim, Joo-Hong

2017-01-01

Recent improvements in the theoretical understanding of the relationship between tropical cyclones (TCs) and their large-scale environments have resulted in significant improvements in the skill for forecasting TC activity at daily and seasonal time-scales. However, future changes in TC activity under a warmer climate remain uncertain, particularly in terms of TC genesis locations and subsequent pathways. Applying a track-pattern-based statistical model to 22 Coupled Model Intercomparison Project Phase 5 (CMIP5) model runs for the historical period and the future period corresponding to the Representative Concentration Pathway 8.5 emissions scenarios, this study shows that in future climate conditions, TC passage frequency will decrease over the North Atlantic, particularly in the Gulf of Mexico, but will increase over the western North Pacific, especially that hits Korea and Japan. Unlike previous studies based on fine-resolution models, an ensemble mean of CMIP5 models projects an increase in TC activity in the western North Pacific, which is owing to enhanced subtropical deep convection and favorable dynamic conditions therein in conjunction with the expansion of the tropics and vice versa for the North Atlantic. Our results suggest that North America will experience less TC landfalls, while northeast Asia will experience more TCs than in the present-day climate.

Asymmetric response of tropical cyclone activity to global warming over the North Atlantic and western North Pacific from CMIP5 model projections.

PubMed

Park, Doo-Sun R; Ho, Chang-Hoi; Chan, Johnny C L; Ha, Kyung-Ja; Kim, Hyeong-Seog; Kim, Jinwon; Kim, Joo-Hong

2017-01-30

Recent improvements in the theoretical understanding of the relationship between tropical cyclones (TCs) and their large-scale environments have resulted in significant improvements in the skill for forecasting TC activity at daily and seasonal time-scales. However, future changes in TC activity under a warmer climate remain uncertain, particularly in terms of TC genesis locations and subsequent pathways. Applying a track-pattern-based statistical model to 22 Coupled Model Intercomparison Project Phase 5 (CMIP5) model runs for the historical period and the future period corresponding to the Representative Concentration Pathway 8.5 emissions scenarios, this study shows that in future climate conditions, TC passage frequency will decrease over the North Atlantic, particularly in the Gulf of Mexico, but will increase over the western North Pacific, especially that hits Korea and Japan. Unlike previous studies based on fine-resolution models, an ensemble mean of CMIP5 models projects an increase in TC activity in the western North Pacific, which is owing to enhanced subtropical deep convection and favorable dynamic conditions therein in conjunction with the expansion of the tropics and vice versa for the North Atlantic. Our results suggest that North America will experience less TC landfalls, while northeast Asia will experience more TCs than in the present-day climate.

Spatial patterns of Antarctic surface temperature trends in the context of natural variability: Lessons from the CMIP5 Models

NASA Astrophysics Data System (ADS)

Smith, K. L.; Polvani, L. M.

2015-12-01

The recent annually averaged warming of the Antarctic Peninsula, and of West Antarctica, stands in stark contrast to very small and weakly negative trends over East Antarctica. This asymmetry arises primarily from a highly significant warming of West Antarctica in austral spring and a strong cooling of East Antarctic in austral autumn. Here we examine whether this East-West asymmetry is a response to anthropogenic climate forcings or a manifestation of natural climate variability. We compare the observed Antarctic surface air temperature (SAT) trends from five temperature reconstructions over two distinct time periods (1979-2005 and 1960-2005), and with those simulated by 40 coupled models participating in Phase 5 of the Coupled Model Intercomparison Project. We find that the observed East-West asymmetry differs substantially over the two time periods and, furthermore, is completely absent from the CMIP5 multi-model mean (from which all natural variability is eliminated by the averaging). We compare the CMIP5 SAT trends to those of 29 historical atmosphere-only simulations with prescribed sea surface temperatures (SSTs) and sea ice and find that these simulations are in better agreement with the observations. This suggests that natural multi-decadal variability associated with SSTs and sea ice and not external forcings is the primary driver of Antarctic SAT trends. We confirm this by showing that the observed trends lie within the distribution of multi-decadal trends from the CMIP5 pre-industrial integrations. These results, therefore, offer new evidence which points to natural climate variability as the more likely cause of the recent warming of West Antarctica and of the Peninsula.

Utilizing Multi-Ensemble of Downscaled CMIP5 GCMs to Investigate Trends and Spatial and Temporal Extent of Drought in Willamette Basin

NASA Astrophysics Data System (ADS)

Ahmadalipour, A.; Beal, B.; Moradkhani, H.

2015-12-01

Changing climate and potential future increases in global temperature are likely to have impacts on drought characteristics and hydrologic cylce. In this study, we analyze changes in temporal and spatial extent of meteorological and hydrological droughts in future, and their trends. Three statistically downscaled datasets from NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), Multivariate Adaptive Constructed Analogs (MACA), and Bias Correction and Spatial Disagregation (BCSD-PSU) each consisting of 10 CMIP5 Global Climate Models (GCM) are utilized for RCP4.5 and RCP8.5 scenarios. Further, Precipitation Runoff Modeling System (PRMS) hydrologic model is used to simulate streamflow from GCM inputs and assess the hydrological drought characteristics. Standard Precipitation Index (SPI) and Streamflow Drought Index (SDI) are the two indexes used to investigate meteorological and hydrological drought, respectively. Study is done for Willamette Basin with a drainage area of 29,700 km2 accommodating more than 3 million inhabitants and 25 dams. We analyze our study for annual time scale as well as three future periods of near future (2010-2039), intermediate future (2040-2069), and far future (2070-2099). Large uncertainty is found from GCM predictions. Results reveal that meteorological drought events are expected to increase in near future. Severe to extreme drought with large areal coverage and several years of occurance is predicted around year 2030 with the likelihood of exceptional drought for both drought types. SPI is usually showing positive trends, while SDI indicates negative trends in most cases.

Merging Multi-model CMIP5/PMIP3 Past-1000 Ensemble Simulations with Tree Ring Proxy Data by Optimal Interpolation Approach

NASA Astrophysics Data System (ADS)

Chen, Xin; Luo, Yong; Xing, Pei; Nie, Suping; Tian, Qinhua

2015-04-01

Two sets of gridded annual mean surface air temperature in past millennia over the Northern Hemisphere was constructed employing optimal interpolation (OI) method so as to merge the tree ring proxy records with the simulations from CMIP5 (the fifth phase of the Climate Model Intercomparison Project). Both the uncertainties in proxy reconstruction and model simulations can be taken into account applying OI algorithm. For better preservation of physical coordinated features and spatial-temporal completeness of climate variability in 7 copies of model results, we perform the Empirical Orthogonal Functions (EOF) analysis to truncate the ensemble mean field as the first guess (background field) for OI. 681 temperature sensitive tree-ring chronologies are collected and screened from International Tree Ring Data Bank (ITRDB) and Past Global Changes (PAGES-2k) project. Firstly, two methods (variance matching and linear regression) are employed to calibrate the tree ring chronologies with instrumental data (CRUTEM4v) individually. In addition, we also remove the bias of both the background field and proxy records relative to instrumental dataset. Secondly, time-varying background error covariance matrix (B) and static "observation" error covariance matrix (R) are calculated for OI frame. In our scheme, matrix B was calculated locally, and "observation" error covariance are partially considered in R matrix (the covariance value between the pairs of tree ring sites that are very close to each other would be counted), which is different from the traditional assumption that R matrix should be diagonal. Comparing our results, it turns out that regional averaged series are not sensitive to the selection for calibration methods. The Quantile-Quantile plots indicate regional climatologies based on both methods are tend to be more agreeable with regional reconstruction of PAGES-2k in 20th century warming period than in little ice age (LIA). Lager volcanic cooling response over Asia and Europe in context of recent millennium are detected in our datasets than that revealed in regional reconstruction from PAGES-2k network. Verification experiments have showed that the merging approach really reconcile the proxy data and model ensemble simulations in an optimal way (with smaller errors than both of them). Further research is needed to improve the error estimation on them.

Projecting 21st century snowpack trends in western USA mountains using variable-resolution CESM

NASA Astrophysics Data System (ADS)

Rhoades, Alan M.; Ullrich, Paul A.; Zarzycki, Colin M.

2018-01-01

Climate change will impact western USA water supplies by shifting precipitation from snow to rain and driving snowmelt earlier in the season. However, changes at the regional-to-mountain scale is still a major topic of interest. This study addresses the impacts of climate change on mountain snowpack by assessing historical and projected variable-resolution (VR) climate simulations in the community earth system model (VR-CESM) forced by prescribed sea-surface temperatures along with widely used regional downscaling techniques, the coupled model intercomparison projects phase 5 bias corrected and statistically downscaled (CMIP5-BCSD) and the North American regional climate change assessment program (NARCCAP). The multi-model RCP8.5 scenario analysis of winter season SWE for western USA mountains indicates by 2040-2065 mean SWE could decrease -19% (NARCCAP) to -38% (VR-CESM), with an ensemble median change of -27%. Contrary to CMIP5-BCSD and NARCCAP, VR-CESM highlights a more pessimistic outcome for western USA mountain snowpack in latter-parts of the 21st century. This is related to temperature changes altering the snow-albedo feedback, snowpack storage, and precipitation phase, but may indicate that VR-CESM resolves more physically consistent elevational effects lacking in statistically downscaled datasets and teleconnections that are not captured in limited area models. Overall, VR-CESM projects by 2075-2100 that average western USA mountain snowfall decreases by -30%, snow cover by -44%, SWE by -69%, and average surface temperature increase of +5.0°C. This places pressure on western USA states to preemptively invest in climate adaptation measures such as alternative water storage, water use efficiency, and reassess reservoir storage operations.

A Canonical Repsonse of Precipitation Characteristics to Global Warming from CMIP5 Models

NASA Technical Reports Server (NTRS)

Lau, William K.-M.; Wu, H.-T.; Kim, K.-M.

2013-01-01

In this study, we find from analyses of projections of 14 CMIP5 models a robust, canonical global response in rainfall characteristics to a warming climate. Under a scenario of 1% increase per year of CO2 emission, the model ensemble projects globally more heavy precipitation (+7+/-2.4%/K1), less moderate precipitation (-2.5+/-0.6%/K), more light precipitation (+1.8+/-1.3%/K1), and increased length of dry (no-rain) periods (+4.7+/-2.1%/K). Regionally, a majority of the models project a consistent response with more heavy precipitation over climatologically wet regions of the deep tropics, especially the equatorial Pacific Ocean and the Asian monsoon regions, and more dry periods over the land areas of the subtropics and the tropical marginal convective zones. Our results suggest that increased CO2 emissions induce a global adjustment in circulation and moisture availability manifested in basic changes in global precipitation characteristics, including increasing risks of severe floods and droughts in preferred geographic locations worldwide.

Evaluation of global climate model on performances of precipitation simulation and prediction in the Huaihe River basin

NASA Astrophysics Data System (ADS)

Wu, Yenan; Zhong, Ping-an; Xu, Bin; Zhu, Feilin; Fu, Jisi

2017-06-01

Using climate models with high performance to predict the future climate changes can increase the reliability of results. In this paper, six kinds of global climate models that selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under Representative Concentration Path (RCP) 4.5 scenarios were compared to the measured data during baseline period (1960-2000) and evaluate the simulation performance on precipitation. Since the results of single climate models are often biased and highly uncertain, we examine the back propagation (BP) neural network and arithmetic mean method in assembling the precipitation of multi models. The delta method was used to calibrate the result of single model and multimodel ensembles by arithmetic mean method (MME-AM) during the validation period (2001-2010) and the predicting period (2011-2100). We then use the single models and multimodel ensembles to predict the future precipitation process and spatial distribution. The result shows that BNU-ESM model has the highest simulation effect among all the single models. The multimodel assembled by BP neural network (MME-BP) has a good simulation performance on the annual average precipitation process and the deterministic coefficient during the validation period is 0.814. The simulation capability on spatial distribution of precipitation is: calibrated MME-AM > MME-BP > calibrated BNU-ESM. The future precipitation predicted by all models tends to increase as the time period increases. The order of average increase amplitude of each season is: winter > spring > summer > autumn. These findings can provide useful information for decision makers to make climate-related disaster mitigation plans.

Projections of Southern Hemisphere atmospheric circulation interannual variability

NASA Astrophysics Data System (ADS)

Grainger, Simon; Frederiksen, Carsten S.; Zheng, Xiaogu

2017-02-01

An analysis is made of the coherent patterns, or modes, of interannual variability of Southern Hemisphere 500 hPa geopotential height field under current and projected climate change scenarios. Using three separate multi-model ensembles (MMEs) of coupled model intercomparison project phase 5 (CMIP5) models, the interannual variability of the seasonal mean is separated into components related to (1) intraseasonal processes; (2) slowly-varying internal dynamics; and (3) the slowly-varying response to external changes in radiative forcing. In the CMIP5 RCP8.5 and RCP4.5 experiments, there is very little change in the twenty-first century in the intraseasonal component modes, related to the Southern annular mode (SAM) and mid-latitude wave processes. The leading three slowly-varying internal component modes are related to SAM, the El Niño-Southern oscillation (ENSO), and the South Pacific wave (SPW). Structural changes in the slow-internal SAM and ENSO modes do not exceed a qualitative estimate of the spatial sampling error, but there is a consistent increase in the ENSO-related variance. Changes in the SPW mode exceed the sampling error threshold, but cannot be further attributed. Changes in the dominant slowly-varying external mode are related to projected changes in radiative forcing. They reflect thermal expansion of the tropical troposphere and associated changes in the Hadley Cell circulation. Changes in the externally-forced associated variance in the RCP8.5 experiment are an order of magnitude greater than for the internal components, indicating that the SH seasonal mean circulation will be even more dominated by a SAM-like annular structure. Across the three MMEs, there is convergence in the projected response in the slow-external component.

Evaluating characteristics of dry spell changes in Lake Urmia Basin using an ensemble CMIP5 GCM models

NASA Astrophysics Data System (ADS)

Fazel, Nasim; Berndtsson, Ronny; Bertacchi Uvo, Cintia; Klove, Bjorn; Madani, Kaveh

2015-04-01

Drought is a natural phenomenon that can cause significant environmental, ecological, and socio-economic losses in water scarce regions. Studies of drought under climate change are essential for water resources planning and management. Dry spells and number of consecutive days with precipitation below a certain threshold can be used to identify the severity of hydrological drought. In this study, we analyzed the projected changes of number of dry days in two future periods, 2011-2040 and 2071-2100, for both seasonal and annual time scales in the Lake Urmia Basin. The lake and its wetlands, located in northwestern Iran, have invaluable environmental, social, and economic importance for the region. The lake level has been shrinking dramatically since 1995 and now the water volume is less than 30% of its original. Moreover, frequent dry spells have struck the region and effected the region's water resources and lake ecosystem as in other parts of Iran too. Analyzing future drought and dry spells characteristics in the region is crucial for sustainable water management and lake restoration plans. We used daily projected precipitation from 20 climate models used in the CMIP5 (Coupled Model Inter-comparison Project Phase 5) driven by three representative paths, RCP2.6, RCP4.5, and, RCP8.5. The model outputs were statistically downscaled and validated based on the historical observation period 1980-2010. We defined days with precipitation less than 1 mm as dry days for both observation periods and model projections. The model validation showed that all models underestimated the number of dry days. An ensemble based on the validation results consisting of five models which were in best agreement with observations was used to assess the changes in number of future dry days in Lake Urmia Basin. The entire ensemble showed increase in number of dry days for all seasons. The projected changes in winter and spring were larger than for summer and autumn. All models projected dryer winter and spring periods in the near and far future periods. The ensemble mean for future annual dry days increased by 6.5 % to 7.3% for the different climate change related emission and concentration pathway RCP2.6, RCP4.5, and, RCP8.5.

Impacts of 1, 1.5, and 2 Degree Warming on Arctic Terrestrial Snow and Sea Ice

NASA Astrophysics Data System (ADS)

Derksen, C.; Mudryk, L.; Howell, S.; Flato, G. M.; Fyfe, J. C.; Gillett, N. P.; Sigmond, M.; Kushner, P. J.; Dawson, J.; Zwiers, F. W.; Lemmen, D.; Duguay, C. R.; Zhang, X.; Fletcher, C. G.; Dery, S. J.

2017-12-01

The 2015 Paris Agreement of the United Nations Framework Convention on Climate Change (UNFCCC) established the global temperature goal of "holding the increase in the global average temperature to below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5°C above pre-industrial levels." In this study, we utilize multiple gridded snow and sea ice products (satellite retrievals; assimilation systems; physical models driven by reanalyses) and ensembles of climate model simulations to determine the impacts of observed warming, and project the relative impacts of the UNFCC future warming targets on Arctic seasonal terrestrial snow and sea ice cover. Observed changes during the satellite era represent the response to approximately 1°C of global warming. Consistent with other studies, analysis of the observational record (1970's to present) identifies changes including a shorter snow cover duration (due to later snow onset and earlier snow melt), significant reductions in spring snow cover and summer sea ice extent, and the loss of a large proportion of multi-year sea ice. The spatial patterns of observed snow and sea ice loss are coherent across adjacent terrestrial/marine regions. There are strong pattern correlations between snow and temperature trends, with weaker association between sea ice and temperature due to the additional influence of dynamical effects such wind-driven redistribution of sea ice. Climate model simulations from the Coupled Model Inter-comparison Project Phase 5(CMIP-5) multi-model ensemble, large initial condition ensembles of the Community Earth System Model (CESM) and Canadian Earth System Model (CanESM2) , and warming stabilization simulations from CESM were used to identify changes in snow and ice under further increases to 1.5°C and 2°C warming. The model projections indicate these levels of warming will be reached over the coming 2-4 decades. Warming to 1.5°C results in an increase in the number of melting days over snow and sea ice (and resultant increases in snow-free and ice-free duration), which are similar in magnitude to the change from pre-industrial conditions to present day. Continued warming to 2°C further intensifies the cryospheric response consistent with amplified Arctic warming relative to the global average trend.

Projection of spatial and temporal changes of rainfall in Sarawak of Borneo Island using statistical downscaling of CMIP5 models

NASA Astrophysics Data System (ADS)

Sa'adi, Zulfaqar; Shahid, Shamsuddin; Chung, Eun-Sung; Ismail, Tarmizi bin

2017-11-01

This study assesses the possible changes in rainfall patterns of Sarawak in Borneo Island due to climate change through statistical downscaling of General Circulation Models (GCM) projections. Available in-situ observed rainfall data were used to downscale the future rainfall from ensembles of 20 GCMs of Coupled Model Intercomparison Project phase 5 (CMIP5) for four Representative Concentration Pathways (RCP) scenarios, namely, RCP2.6, RCP4.5, RCP6.0 and RCP8.5. Model Output Statistics (MOS) based downscaling models were developed using two data mining approaches known as Random Forest (RF) and Support Vector Machine (SVM). The SVM was found to downscale all GCMs with normalized mean square error (NMSE) of 48.2-75.2 and skill score (SS) of 0.94-0.98 during validation. The results show that the future projection of the annual rainfalls is increasing and decreasing on the region-based and catchment-based basis due to the influence of the monsoon season affecting the coast of Sarawak. The ensemble mean of GCMs projections reveals the increased and decreased mean of annual precipitations at 33 stations with the rate of 0.1% to 19.6% and one station with the rate of - 7.9% to - 3.1%, respectively under all RCP scenarios. The remaining 15 stations showed inconsistency neither increasing nor decreasing at the rate of - 5.6% to 5.2%, but mainly showing a trend of decreasing rainfall during the first period (2010-2039) followed by increasing rainfall for the period of 2070-2099.

CMIP5 Historical Simulations (1850-2012) with GISS ModelE2

NASA Technical Reports Server (NTRS)

Miller, Ronald Lindsay; Schmidt, Gavin A.; Nazarenko, Larissa S.; Tausnev, Nick; Bauer, Susanne E.; DelGenio, Anthony D.; Kelley, Max; Lo, Ken K.; Ruedy, Reto; Shindell, Drew T.;

Impact of internal variability on projections of Sahel precipitation change

NASA Astrophysics Data System (ADS)

Monerie, Paul-Arthur; Sanchez-Gomez, Emilia; Pohl, Benjamin; Robson, Jon; Dong, Buwen

2017-11-01

The impact of the increase of greenhouse gases on Sahelian precipitation is very uncertain in both its spatial pattern and magnitude. In particular, the relative importance of internal variability versus external forcings depends on the time horizon considered in the climate projection. In this study we address the respective roles of the internal climate variability versus external forcings on Sahelian precipitation by using the data from the CESM Large Ensemble Project, which consists of a 40 member ensemble performed with the CESM1-CAM5 coupled model for the period 1920-2100. We show that CESM1-CAM5 is able to simulate the mean and interannual variability of Sahel precipitation, and is representative of a CMIP5 ensemble of simulations (i.e. it simulates the same pattern of precipitation change along with equivalent magnitude and seasonal cycle changes as the CMIP5 ensemble mean). However, CESM1-CAM5 underestimates the long-term decadal variability in Sahel precipitation. For short-term (2010-2049) and mid-term (2030-2069) projections the simulated internal variability component is able to obscure the projected impact of the external forcing. For long-term (2060-2099) projections external forcing induced change becomes stronger than simulated internal variability. Precipitation changes are found to be more robust over the central Sahel than over the western Sahel, where climate change effects struggle to emerge. Ten (thirty) members are needed to separate the 10 year averaged forced response from climate internal variability response in the western Sahel for a long-term (short-term) horizon. Over the central Sahel two members (ten members) are needed for a long-term (short-term) horizon.

Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble

USGS Publications Warehouse

Lorenz, Ruth; Argueso, Daniel; Donat, Markus G.; Pitman, Andrew J.; van den Hurk, Bart; Berg, Alexis; Lawrence, David M.; Cheruy, Frederique; Ducharne, Agnes; Hagemann, Stefan; Meier, Arndt; Milly, Paul C.D.; Seneviratne, Sonia I

2016-01-01

We examine how soil moisture variability and trends affect the simulation of temperature and precipitation extremes in six global climate models using the experimental protocol of the Global Land-Atmosphere Coupling Experiment of the Coupled Model Intercomparison Project, Phase 5 (GLACE-CMIP5). This protocol enables separate examinations of the influences of soil moisture variability and trends on the intensity, frequency, and duration of climate extremes by the end of the 21st century under a business-as-usual (Representative Concentration Pathway 8.5) emission scenario. Removing soil moisture variability significantly reduces temperature extremes over most continental surfaces, while wet precipitation extremes are enhanced in the tropics. Projected drying trends in soil moisture lead to increases in intensity, frequency, and duration of temperature extremes by the end of the 21st century. Wet precipitation extremes are decreased in the tropics with soil moisture trends in the simulations, while dry extremes are enhanced in some regions, in particular the Mediterranean and Australia. However, the ensemble results mask considerable differences in the soil moisture trends simulated by the six climate models. We find that the large differences between the models in soil moisture trends, which are related to an unknown combination of differences in atmospheric forcing (precipitation, net radiation), flux partitioning at the land surface, and how soil moisture is parameterized, imply considerable uncertainty in future changes in climate extremes.

Understanding independence

NASA Astrophysics Data System (ADS)

Annan, James; Hargreaves, Julia

2016-04-01

In order to perform any Bayesian processing of a model ensemble, we need a prior over the ensemble members. In the case of multimodel ensembles such as CMIP, the historical approach of ``model democracy'' (i.e. equal weight for all models in the sample) is no longer credible (if it ever was) due to model duplication and inbreeding. The question of ``model independence'' is central to the question of prior weights. However, although this question has been repeatedly raised, it has not yet been satisfactorily addressed. Here I will discuss the issue of independence and present a theoretical foundation for understanding and analysing the ensemble in this context. I will also present some simple examples showing how these ideas may be applied and developed.

Impacts of climate change on peanut yield in China simulated by CMIP5 multi-model ensemble projections

NASA Astrophysics Data System (ADS)

Xu, Hanqing; Tian, Zhan; Zhong, Honglin; Fan, Dongli; Shi, Runhe; Niu, Yilong; He, Xiaogang; Chen, Maosi

2017-09-01

Peanut is one of the major edible vegetable oil crops in China, whose growth and yield are very sensitive to climate change. In addition, agriculture climate resources are expected to be redistributed under climate change, which will further influence the growth, development, cropping patterns, distribution and production of peanut. In this study, we used the DSSAT-Peanut model to examine the climate change impacts on peanut production, oil industry and oil food security in China. This model is first calibrated using site observations including 31 years' (1981-2011) climate, soil and agronomy data. This calibrated model is then employed to simulate the future peanut yield based on 20 climate scenarios from 5 Global Circulation Models (GCMs) developed by the InterSectoral Impact Model Intercomparison Project (ISIMIP) driven by 4 Representative Concentration Pathways (RCPs). Results indicate that the irrigated peanut yield will decrease 2.6% under the RCP 2.6 scenario, 9.9% under the RCP 4.5 scenario and 29% under the RCP 8.5 scenario, respectively. Similarly, the rain-fed peanut yield will also decrease, with a 2.5% reduction under the RCP 2.6 scenario, 11.5% reduction under the RCP 4.5 scenario and 30% reduction under the RCP 8.5 scenario, respectively.

Attribution of spring snow water equivalent (SWE) changes over the northern hemisphere to anthropogenic effects

NASA Astrophysics Data System (ADS)

Jeong, Dae Il; Sushama, Laxmi; Naveed Khaliq, M.

2017-06-01

Snow is an important component of the cryosphere and it has a direct and important influence on water storage and supply in snowmelt-dominated regions. This study evaluates the temporal evolution of snow water equivalent (SWE) for the February-April spring period using the GlobSnow observation dataset for the 1980-2012 period. The analysis is performed for different regions of hemispherical to sub-continental scales for the Northern Hemisphere. The detection-attribution analysis is then performed to demonstrate anthropogenic and natural effects on spring SWE changes for different regions, by comparing observations with six CMIP5 model simulations for three different external forcings: all major anthropogenic and natural (ALL) forcings, greenhouse gas (GHG) forcing only, and natural forcing only. The observed spring SWE generally displays a decreasing trend, due to increasing spring temperatures. However, it exhibits a remarkable increasing trend for the southern parts of East Eurasia. The six CMIP5 models with ALL forcings reproduce well the observed spring SWE decreases at the hemispherical scale and continental scales, whereas important differences are noted for smaller regions such as southern and northern parts of East Eurasia and northern part of North America. The effects of ALL and GHG forcings are clearly detected for the spring SWE decline at the hemispherical scale, based on multi-model ensemble signals. The effects of ALL and GHG forcings, however, are less clear for the smaller regions or with single-model signals, indicating the large uncertainty in regional SWE changes, possibly due to stronger influence of natural climate variability.

High-resolution multimodel projections of soil moisture drought in Europe under 1.5, 2 and 3 degree global warming

NASA Astrophysics Data System (ADS)

Samaniego, L. E.; Kumar, R.; Zink, M.; Pan, M.; Wanders, N.; Marx, A.; Sheffield, J.; Wood, E. F.; Thober, S.

2017-12-01

Droughts are creeping hydro-meteorological events that may bring societies and natural systems to their limits by inducing significant environmental changes and large socio-economic losses. Little is know about the effects of varios degrees of warming (i.e., 1.5 , 2 and 3 K) and their respective uncertainties on extreme characteristics such as drought duration and area under drought in general, and in Europe in particular. In this study we investigate the evolution of droughts characteristics under three levels of warming using an unprecedented high-resolution multi-model hydrologic ensemble over the Pan-EU domain at a scale of 5x5 km2 from 1950 until 2100. This multi-model ensemble comprises four hydrologic models (HMs: mHM, Noah-MP, PCR-GLOBWB, VIC) which are forced by five CMIP-5 Global Climate Models (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M) under three RCP scenarios 2.6, 6.0, and 8.5. This results in a 60-member ensemble. The contribution GCM/HM uncertainties were analyzed based on a sequential sampling algorithm proposed by Samaniego et al. 2016. This study is carried out within the EDgE project funded by the Copernicus Climate Change Service (edge.climate.copernicus.eu) and the HOKLIM project funded by the German Ministry of Education (BMBF)(www.ufz.de/hoklim). The changes under three levels of warming indicate significant increase (more than 10%) of the number of droughts and area under drought with respect to 30-year climatological means obtained with E-OBS observations. Furthermore, we found that: 1) the number of drought events exhibit significant regional changes. Largest changes are observed in the Mediterrinian where frequency of droughts increases from 25% under 1.5 K to 33% under 2 K, and to more than 50% under 3 K warming. Minor changes are seen in Central-Europe and the British Isles. 2) The GCMs/HMs uncertainties have marked regional differences too, with GCM uncertainty appear to be larger everywhere. The uncertainty of HMs are, however, similar to those of the GCMs in the Iberian peninsula due to different representation of evapotranspiration and soil moisture dynamics. And, 3) despite the large uncertainty in the full ensemble, significant positive trends have been observed in all drought characteristics that intensify with increased global warming.

Changing carbonate chemistry in ocean waters surrounding coral reefs in the CMIP5 ensemble

NASA Astrophysics Data System (ADS)

Ricke, K.; Schneider, K.; Cao, L.; Caldeira, K.

2012-12-01

Coral reefs comprise some of the most biodiverse ecosystems in the world. Today they are threatened by a number of stressors, including pollution, bleaching from global warming and ocean acidification. In this study, we focus on the implications of ocean acidification for the open ocean chemistry surrounding coral reefs. We use results from 13 Earth System Models included in the Coupled Model Intercomparison Project 5 (CMIP5) to examine the changing aragonite saturations (Ωa) of open ocean waters surrounding approximately 6,000 coral reefs. These 13 Earth System Models participating in CMIP5 each have interactive ocean biogeochemistry models that output state variables including DIC, alkalinity, SST, and salinity. Variation in these values were combined with values from the GLODAP database to calculate aragonite, the form of calcium carbonate that corals use to make their skeletons. We used reef locations from ReefBase that were within one degree (in latitude or longitude) of water masses represented both in the GLODAP database and in the climate models. Carbonate chemistry calculations were performed by Dr. James C. Orr (IPSL) as part of a separate study. We find that in preindustrial times, 99.9 % of coral reefs were located in regions of the ocean with aragonite saturations of 3.5 or more. The saturation threshold for viable reef ecosystems in uncertain, but the pre-industrial distribution of water chemistry surrounding coral reefs may nevertheless provide some indication of viability. We examine the fate of coral reefs in the context of several potential aragonite saturation thresholds, i.e., when Ωa_crit equals 3, 3.25, or 3.5. We show that under a business-as-usual scenario Representative Concentration Pathway (RCP) 8.5, the specific value of Ωa_crit does not affect the long-term fate of coral reefs -- by the end of the 21st century, no coral reef considered is surrounded by water with Ωa> 3. However, under scenarios with significant CO2 emissions abatement, the aragonite saturation threshold is critical to projecting the fate of coral reefs -- under RCP 4.5, less than 5% of reefs are surrounded by waters with Ωa < 3.5 by the end of the century, but nearly half are still surrounded by waters with saturations greater than 3. Our results indicate that only under a very aggressive emissions elimination (and CO2 air-capture) scenario (RCP 2.6) are a majority of coral reefs projected to remain in waters with Ωa > 3.5 at the end of the century. We find that, except for one model that is an outlier, the spread of aragonite saturation states across earth system models in the CMIP5 ensemble is narrow, implying that these ocean chemistry projections are fairly robust.

A Multimodel Ensemble Analysis of Global Changes in Plant Water Use Efficiency and Primary Productivity in the 21st Century

NASA Astrophysics Data System (ADS)

Bernardes, S.

2017-12-01

Outputs from coupled carbon-climate models show considerable variability in atmospheric and land fields over the 21st century, including changes in temperature and in the spatiotemporal distribution and quantity of precipitation over the planet. Reductions in water availability due to decreased precipitation and increased water demand by the atmosphere may reduce carbon uptake by critical ecosystems. Conversely, increases in atmospheric carbon dioxide have the potential to offset reductions in productivity. This work focuses on predicted responses of plants to environmental changes and on how plants will adjust their water use efficiency (WUE, plant production per water loss by evapotranspiration) in the 21st century. Predicted changes in WUE were investigated using an ensemble of Earth System Models from the Coupled Model Intercomparison Project 5 (CMIP5), flux tower data and products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Scenarios for climate futures used two representative concentration pathways, including carbon concentration peak in 2040 (RCP4.5) and rising emissions throughout the 21st century (RCP8.5). Model results included the periods 2006-2009 (predicted) and 1850-2005 (reference). IPCC SREX regions were used to compare modeled, flux and satellite data and to address the significant intermodel variability observed for the CMIP5 ensemble (larger variability for RCP8.5, higher intermodel agreement in Southeast Asia, lower intermodel agreement in arid areas). An evaluation of model skill at the regional level supported model selection and the spatiotemporal analysis of changes in WUE. Departures of projected conditions in relation to historical values are presented for both concentration pathways at global, regional levels, including latitudinal distributions. High model sensitivity to different concentration pathways and increase in GPP and WUE was observed for most of the planet (increases consistently higher for RCP8.5). Higher latitudes in the northern hemisphere (boreal region) are predicted to experience higher increases in GPP and WUE, with WUE usually following GPP in changes. Models point to decreases in productivity and WUE mostly in the tropics, affecting tropical forests in the Amazon and in Central America.

Observed and Projected Droughts Conditioned on Temperature Change

NASA Astrophysics Data System (ADS)

Chiang, F.; AghaKouchak, A.; Mazdiyasni, O.

2016-12-01

Droughts have had severe urban, agricultural and wildlife impacts in historical and recent years. In addition, during times of water scarcity, heat stress has been shown to produce compounding climatic and environmental effects. Understanding the overall conditions associated with drought intensities is important for mapping the anatomy of the climate in the changing world. For the study, we evaluated the relationship drought severity has exhibited with temperature shifts between observed periods and also between an ensemble of BCSD downscaled CMIP5 projected and historically modeled datasets. We compared temperatures during different categories of drought severity on a monthly scale, and mapped areas displaying an escalation of temperature with stricter definitions of drought. A historical shift of warmer temperatures in more severe droughts was observed most consistently in Southwestern and Eastern states between the later half of the 20th century and a reference period of the early half of the 20th century. Future projections from an ensemble of CMIP5 models also showed a shift to warmer temperatures during more intense drought events in similar states. Preliminary statistics show that in many areas future droughts will be warmer that the average projected climate. These observed and forecasted shifts in the heating intensity of severe drought events underscore the need to further research these patterns and relationships both spatially and temporally.

Multi-decadal evolution characteristics of global surface temperature anomaly data shown by observation and CMIP5 models

NASA Astrophysics Data System (ADS)

Zhu, X.

2017-12-01

Based on methods of statistical analysis, the time series of global surface air temperature(SAT) anomalies from 1860-2014 has been defined by three types of phase changes that occur through the division of temperature changes into different stages. The characteristics of the three types of phase changes simulated by CMIP5 models were evaluated. The conclusion is as follows: the SAT from 1860-2014 can be divided into six stages according to trend differences, and this subdivision is proved to be statistically significant. Based on trend analysis and the distribution of slopes between any two points (two points' slope) in every stage, the six stages can be summarized as three phase changes of warming, cooling, and hiatus. Between 1860 and 2014, the world experienced three heating phases (1860-1878, 1909-1942,1975-2004), one cooling phase (1878-1909), and two hiatus phases (1942-1975, 2004-2014).Using the definition method, whether the next year belongs to the previous phase can be estimated. Furthermore, the temperature in 2015 was used as an example to validate the feasibility of this method. The simulations of the heating period by CMIP5 models are well; however the characteristics shown by SAT during the cooling and hiatus period cannot be represented by CMIP5 models. As such, the projections of future heating phases using the CMIP5 models are credible, but for cooling and hiatus events they are unreliable.

Future Climate Change Impacts on Surface Hydrology over Texas River Basins

NASA Astrophysics Data System (ADS)

Lee, K.; Gao, H.; Huang, M.; Sheffield, J.

2014-12-01

Future freshwater availability is a pressing issue in Texas due to frequent drought events and fast population growth. Even though the science community has well investigated future temperature trends, it is still unclear whether precipitation will increase or decrease in this region. Furthermore, there is a lack of understanding on how the changing climate will affect water resources across different spatial-temporal scales. This study aims to quantify the impacts of climate change on surface hydrology at the basin scale under different future emission scenarios. The Variable Infiltration Capacity (VIC) model, forced by an ensemble of statistically downscaled climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) models, is employed to predict the future hydrology. The VIC model parameters are adopted from the North American Land Data Assimilation System (NLDAS) at a spatial resolution of 1/8°. The CMIP5 projections contain four different scenarios in terms of Representative Concentration Pathway (RCP) (i.e. 2.6, 4.5, 6.0 and 8.5 w/m2). The analysis is carried out in three steps. First, the observed streamflows are used to evaluate the performance of VIC simulations forced by CMIP5 models during historical period. Second, VIC outputs under multiple CMIP5 model scenarios from 1950 to 2099 are analyzed to identify how soil moisture, evapotranspiration, runoff, and routed streamflows change in time and space. Third, the spatial patterns of seasonal temperature, seasonal precipitation, and the Palmer Drought Severity Index (PDSI)—over four 20-year periods (1980-1999, 2010-2029, 2040-2059 and 2080-2099)—are used to pinpoint the regions that will be most affected by climate change (among the 13 Texan river basins). Furthermore, the role of groundwater in meeting the increasing needs for water supply is discussed. The results are expected to contribute to various future water resources management decisions in Texas.

Modes of interannual variability in northern hemisphere winter atmospheric circulation in CMIP5 models: evaluation, projection and role of external forcing

NASA Astrophysics Data System (ADS)

Frederiksen, Carsten S.; Ying, Kairan; Grainger, Simon; Zheng, Xiaogu

2018-04-01

Models from the coupled model intercomparison project phase 5 (CMIP5) dataset are evaluated for their ability to simulate the dominant slow modes of interannual variability in the Northern Hemisphere atmospheric circulation 500 hPa geopotential height in the twentieth century. A multi-model ensemble of the best 13 models has then been used to identify the leading modes of interannual variability in components related to (1) intraseasonal processes; (2) slowly-varying internal dynamics; and (3) the slowly-varying response to external changes in radiative forcing. Modes in the intraseasonal component are related to intraseasonal variability in the North Atlantic, North Pacific and North American, and Eurasian regions and are little affected by the larger radiative forcing of the Representative Concentration Pathways 8.5 (RCP8.5) scenario. The leading modes in the slow-internal component are related to the El Niño-Southern Oscillation, Pacific North American or Tropical Northern Hemisphere teleconnection, the North Atlantic Oscillation, and the Western Pacific teleconnection pattern. While the structure of these slow-internal modes is little affected by the larger radiative forcing of the RCP8.5 scenario, their explained variance increases in the warmer climate. The leading mode in the slow-external component has a significant trend and is shown to be related predominantly to the climate change trend in the well mixed greenhouse gas concentration during the historical period. This mode is associated with increasing height in the 500 hPa pressure level. A secondary influence on this mode is the radiative forcing due to stratospheric aerosols associated with volcanic eruptions. The second slow-external mode is shown to be also related to radiative forcing due to stratospheric aerosols. Under RCP8.5 there is only one slow-external mode related to greenhouse gas forcing with a trend over four times the historical trend.

Evaluating wind extremes in CMIP5 climate models

NASA Astrophysics Data System (ADS)

Kumar, Devashish; Mishra, Vimal; Ganguly, Auroop R.

2015-07-01

Wind extremes have consequences for renewable energy sectors, critical infrastructures, coastal ecosystems, and insurance industry. Considerable debates remain regarding the impacts of climate change on wind extremes. While climate models have occasionally shown increases in regional wind extremes, a decline in the magnitude of mean and extreme near-surface wind speeds has been recently reported over most regions of the Northern Hemisphere using observed data. Previous studies of wind extremes under climate change have focused on selected regions and employed outputs from the regional climate models (RCMs). However, RCMs ultimately rely on the outputs of global circulation models (GCMs), and the value-addition from the former over the latter has been questioned. Regional model runs rarely employ the full suite of GCM ensembles, and hence may not be able to encapsulate the most likely projections or their variability. Here we evaluate the performance of the latest generation of GCMs, the Coupled Model Intercomparison Project phase 5 (CMIP5), in simulating extreme winds. We find that the multimodel ensemble (MME) mean captures the spatial variability of annual maximum wind speeds over most regions except over the mountainous terrains. However, the historical temporal trends in annual maximum wind speeds for the reanalysis data, ERA-Interim, are not well represented in the GCMs. The historical trends in extreme winds from GCMs are statistically not significant over most regions. The MME model simulates the spatial patterns of extreme winds for 25-100 year return periods. The projected extreme winds from GCMs exhibit statistically less significant trends compared to the historical reference period.

Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere-ocean climate models

DOE PAGES

Andrews, Timothy; Gregory, Jonathan M.; Webb, Mark J.; ...

2012-05-15

We quantify forcing and feedbacks across available CMIP5 coupled atmosphere-ocean general circulation models (AOGCMs) by analysing simulations forced by an abrupt quadrupling of atmospheric carbon dioxide concentration. This is the first application of the linear forcing-feedback regression analysis of Gregory et al. (2004) to an ensemble of AOGCMs. The range of equilibrium climate sensitivity is 2.1–4.7 K. Differences in cloud feedbacks continue to be important contributors to this range. Some models show small deviations from a linear dependence of top-of-atmosphere radiative fluxes on global surface temperature change. We show that this phenomenon largely arises from shortwave cloud radiative effects overmore » the ocean and is consistent with independent estimates of forcing using fixed sea-surface temperature methods. Moreover, we suggest that future research should focus more on understanding transient climate change, including any time-scale dependence of the forcing and/or feedback, rather than on the equilibrium response to large instantaneous forcing.« less

Evaluating the cloud radiative forcing over East Asia during summer simulated by CMIP5 models

NASA Astrophysics Data System (ADS)

Lin, Z.; Wang, Y.; Liu, X.

2017-12-01

A large degree of uncertainty in global climate models (GCMs) can be attributed to the representation of clouds and its radiative forcing (CRF). In this study, the simulated CRFs, total cloud fraction (CF) and cloud properties over East Asia from 20 CMIP5 AMIP models are evaluated and compared with multiple satellite observations, and the possible causes for the CRF bias in the CMIP5 models are then investigated. Based on the satellite observation, strong Long wave CRF (LWCRF) and Short wave CRF (SWCRF) are found to be located over Southwestern China, with minimum SWCRF less than -130Wm-2 and this is associated with the large amount of cloud in the region. By contrast, weak CRFs are located over Northwest China and Western Pacific region because of less cloud amount. In Northeastern China, the strong SWCRF and week LWCRF can be found due to the dominant low-level cloud. In Eastern China, the CRFs is moderate due to the co-existence of the multi-layer cloud. CMIP5 models can basically capture the structure of CRFs in East Asia, with the spatial correlation coefficient between 0.5 and 0.9. But most models underestimate CRFs in East Asia, which is highly associated with the underestimation of cloud amount in the region. The performance of CMIP5 models varies in different part of East Asian region, with a larger deviation in Eastern China (EC). Further investigation suggests that, underestimation of the cloud amount in EC can lead to the weak bias of CRFs in EC, however, this CRF bias can be cancelled out by the overestimation effect of CRF due to excessive cloud optical depth (COD) simulated by the models. The annual cycle of simulated CRF over Eastern China is also examined, and it is found, CMIP models are unable to reproduce the northward migration of CRF in summer monsoon season, which is closely related with northward shift of East Asian summer monsoon rain belt.

Scenario and modelling uncertainty in global mean temperature change derived from emission driven Global Climate Models

NASA Astrophysics Data System (ADS)

Booth, B. B. B.; Bernie, D.; McNeall, D.; Hawkins, E.; Caesar, J.; Boulton, C.; Friedlingstein, P.; Sexton, D.

2012-09-01

We compare future changes in global mean temperature in response to different future scenarios which, for the first time, arise from emission driven rather than concentration driven perturbed parameter ensemble of a Global Climate Model (GCM). These new GCM simulations sample uncertainties in atmospheric feedbacks, land carbon cycle, ocean physics and aerosol sulphur cycle processes. We find broader ranges of projected temperature responses arising when considering emission rather than concentration driven simulations (with 10-90 percentile ranges of 1.7 K for the aggressive mitigation scenario up to 3.9 K for the high end business as usual scenario). A small minority of simulations resulting from combinations of strong atmospheric feedbacks and carbon cycle responses show temperature increases in excess of 9 degrees (RCP8.5) and even under aggressive mitigation (RCP2.6) temperatures in excess of 4 K. While the simulations point to much larger temperature ranges for emission driven experiments, they do not change existing expectations (based on previous concentration driven experiments) on the timescale that different sources of uncertainty are important. The new simulations sample a range of future atmospheric concentrations for each emission scenario. Both in case of SRES A1B and the Representative Concentration Pathways (RCPs), the concentration pathways used to drive GCM ensembles lies towards the lower end of our simulated distribution. This design decision (a legecy of previous assessments) is likely to lead concentration driven experiments to under-sample strong feedback responses in concentration driven projections. Our ensemble of emission driven simulations span the global temperature response of other multi-model frameworks except at the low end, where combinations of low climate sensitivity and low carbon cycle feedbacks lead to responses outside our ensemble range. The ensemble simulates a number of high end responses which lie above the CMIP5 carbon cycle range. These high end simulations can be linked to sampling a number of stronger carbon cycle feedbacks and to sampling climate sensitivities above 4.5 K. This latter aspect highlights the priority in identifying real world climate sensitivity constraints which, if achieved, would lead to reductions on the uppper bound of projected global mean temperature change. The ensembles of simulations presented here provides a framework to explore relationships between present day observables and future changes while the large spread of future projected changes, highlights the ongoing need for such work.

European climate change at global mean temperature increases of 1.5 and 2 °C above pre-industrial conditions as simulated by the EURO-CORDEX regional climate models

NASA Astrophysics Data System (ADS)

Kjellström, Erik; Nikulin, Grigory; Strandberg, Gustav; Bøssing Christensen, Ole; Jacob, Daniela; Keuler, Klaus; Lenderink, Geert; van Meijgaard, Erik; Schär, Christoph; Somot, Samuel; Sørland, Silje Lund; Teichmann, Claas; Vautard, Robert

2018-05-01

We investigate European regional climate change for time periods when the global mean temperature has increased by 1.5 and 2 °C compared to pre-industrial conditions. Results are based on regional downscaling of transient climate change simulations for the 21st century with global climate models (GCMs) from the fifth-phase Coupled Model Intercomparison Project (CMIP5). We use an ensemble of EURO-CORDEX high-resolution regional climate model (RCM) simulations undertaken at a computational grid of 12.5 km horizontal resolution covering Europe. The ensemble consists of a range of RCMs that have been used for downscaling different GCMs under the RCP8.5 forcing scenario. The results indicate considerable near-surface warming already at the lower 1.5 °C of warming. Regional warming exceeds that of the global mean in most parts of Europe, being the strongest in the northernmost parts of Europe in winter and in the southernmost parts of Europe together with parts of Scandinavia in summer. Changes in precipitation, which are less robust than the ones in temperature, include increases in the north and decreases in the south with a borderline that migrates from a northerly position in summer to a southerly one in winter. Some of these changes are already seen at 1.5 °C of warming but are larger and more robust at 2 °C. Changes in near-surface wind speed are associated with a large spread among individual ensemble members at both warming levels. Relatively large areas over the North Atlantic and some parts of the continent show decreasing wind speed while some ocean areas in the far north show increasing wind speed. The changes in temperature, precipitation and wind speed are shown to be modified by changes in mean sea level pressure, indicating a strong relationship with the large-scale circulation and its internal variability on decade-long timescales. By comparing to a larger ensemble of CMIP5 GCMs we find that the RCMs can alter the results, leading either to attenuation or amplification of the climate change signal in the underlying GCMs. We find that the RCMs tend to produce less warming and more precipitation (or less drying) in many areas in both winter and summer.

Persistent Cold Air Outbreaks over North America Under Climate Warming

NASA Astrophysics Data System (ADS)

Gao, Y.; Leung, L. R.; Lu, J.

2014-12-01

This study evaluates the change of cold air outbreaks (CAO) over North America using Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble of global climate simulations as well as regional high resolution climate simulations. In future, while robust decrease of CAO duration dominates in most of the North America, the decrease over northwestern U.S. was found to have much smaller magnitude than the surrounding regions. We found statistically significant increase of the sea level pressure over gulf of Alaska, leading to the advection of cold air to northwestern U.S.. By shifting the probability distribution of present temperature towards future warmer conditions, we identified the changes in large scale circulation contribute to about 50% of the enhanced sea level pressure. Using the high resolution regional climate model results, we found that increases of existing snowpack could potentially trigger the increase of CAO in the near future over the southwestern U.S. and Rocky Mountain through surface albedo effects. By the end of this century, the top 5 most extreme historical CAO events may still occur and wind chill warning will continue to have societal impacts over North America in particular over northwestern United States.

Development of hi-resolution regional climate scenarios in Japan by statistical downscaling

NASA Astrophysics Data System (ADS)

Dairaku, K.

2016-12-01

Climate information and services for Impacts, Adaptation and Vulnerability (IAV) Assessments are of great concern. To meet with the needs of stakeholders such as local governments, a Japan national project, Social Implementation Program on Climate Change Adaptation Technology (SI-CAT), launched in December 2015. It develops reliable technologies for near-term climate change predictions. Multi-model ensemble regional climate scenarios with 1km horizontal grid-spacing over Japan are developed by using CMIP5 GCMs and a statistical downscaling method to support various municipal adaptation measures appropriate for possible regional climate changes. A statistical downscaling method, Bias Correction Spatial Disaggregation (BCSD), is employed to develop regional climate scenarios based on CMIP5 RCP8.5 five GCMs (MIROC5, MRI-CGCM3, GFDL-CM3, CSIRO-Mk3-6-0, HadGEM2-ES) for the periods of historical climate (1970-2005) and near future climate (2020-2055). Downscaled variables are monthly/daily precipitation and temperature. File format is NetCDF4 (conforming to CF1.6, HDF5 compression). Developed regional climate scenarios will be expanded to meet with needs of stakeholders and interface applications to access and download the data are under developing. Statistical downscaling method is not necessary to well represent locally forced nonlinear phenomena, extreme events such as heavy rain, heavy snow, etc. To complement the statistical method, dynamical downscaling approach is also combined and applied to some specific regions which have needs of stakeholders. The added values of statistical/dynamical downscaling methods compared with parent GCMs are investigated.

One-Dimensional Contact Mode Interdigitated Center of Pressure Sensor (CMIPS)

NASA Technical Reports Server (NTRS)

Xu, Tian-Bing; Kang, Jinho; Park, Cheol; Harrison, Joycelyn S.; Guerreiro, Nelson M.; Hubbard, James E.

2009-01-01

A one dimensional contact mode interdigitated center of pressure sensor (CMIPS) has been developed. The experimental study demonstrated that the CMIPS has the capability to measure the overall pressure as well as the center of pressure in one dimension, simultaneously. A theoretical model for the CMIPS is established here based on the equivalent circuit of the configuration of the CMIPS as well as the material properties of the sensor. The experimental results match well with theoretical modeling predictions. A system mapped with two or more pieces of the CMIPS can be used to obtain information from the pressure distribution in multi-dimensions.

Decadal Prediction Skill in the GEOS-5 Forecast System

NASA Technical Reports Server (NTRS)

Ham, Yoo-Geun; Rienecker, Michael M.; Suarez, M.; Vikhliaev, Yury V.; Zhao, Bin; Marshak, Jelena; Vernieres, Guillaume; Schubert, Siegfried D.

2012-01-01

A suite of decadal predictions has been conducted with the NASA Global Modeling and Assimilation Office?s GEOS-5 Atmosphere-Ocean General Circulation Model (AOGCM). The hindcasts are initialized every December from 1959 to 2010 following the CMIP5 experimental protocol for decadal predictions. The initial conditions are from a multi-variate ensemble optimal interpolation ocean and sea-ice reanalysis, and from the atmospheric reanalysis (MERRA, the Modern-Era Retrospective Analysis for Research and Applications) generated using the GEOS-5 atmospheric model. The forecast skill of a three-member-ensemble mean is compared to that of an experiment without initialization but forced with observed CO2. The results show that initialization acts to increase the forecast skill of Northern Atlantic SST compared to the uninitialized runs, with the increase in skill maintained for almost a decade over the subtropical and mid-latitude Atlantic. The annual-mean Atlantic Meridional Overturning Circulation (AMOC) index is predictable up to a 5-year lead time, consistent with the predictable signal in upper ocean heat content over the Northern Atlantic. While the skill measured by Mean Squared Skill Score (MSSS) shows 50% improvement up to 10-year lead forecast over the subtropical and mid-latitude Atlantic, however, prediction skill is relatively low in the subpolar gyre, due in part to the fact that the spatial pattern of the dominant simulated decadal mode in upper ocean heat content over this region appears to be unrealistic. An analysis of the large-scale temperature budget shows that this is the result of a model bias, implying that realistic simulation of the climatological fields is crucial for skillful decadal forecasts.

Large-scale, high-performance and cloud-enabled multi-model analytics experiments in the context of the Earth System Grid Federation

NASA Astrophysics Data System (ADS)

Fiore, S.; Płóciennik, M.; Doutriaux, C.; Blanquer, I.; Barbera, R.; Williams, D. N.; Anantharaj, V. G.; Evans, B. J. K.; Salomoni, D.; Aloisio, G.

2017-12-01

The increased models resolution in the development of comprehensive Earth System Models is rapidly leading to very large climate simulations output that pose significant scientific data management challenges in terms of data sharing, processing, analysis, visualization, preservation, curation, and archiving.Large scale global experiments for Climate Model Intercomparison Projects (CMIP) have led to the development of the Earth System Grid Federation (ESGF), a federated data infrastructure which has been serving the CMIP5 experiment, providing access to 2PB of data for the IPCC Assessment Reports. In such a context, running a multi-model data analysis experiment is very challenging, as it requires the availability of a large amount of data related to multiple climate models simulations and scientific data management tools for large-scale data analytics. To address these challenges, a case study on climate models intercomparison data analysis has been defined and implemented in the context of the EU H2020 INDIGO-DataCloud project. The case study has been tested and validated on CMIP5 datasets, in the context of a large scale, international testbed involving several ESGF sites (LLNL, ORNL and CMCC), one orchestrator site (PSNC) and one more hosting INDIGO PaaS services (UPV). Additional ESGF sites, such as NCI (Australia) and a couple more in Europe, are also joining the testbed. The added value of the proposed solution is summarized in the following: it implements a server-side paradigm which limits data movement; it relies on a High-Performance Data Analytics (HPDA) stack to address performance; it exploits the INDIGO PaaS layer to support flexible, dynamic and automated deployment of software components; it provides user-friendly web access based on the INDIGO Future Gateway; and finally it integrates, complements and extends the support currently available through ESGF. Overall it provides a new "tool" for climate scientists to run multi-model experiments. At the time this contribution is being written, the proposed testbed represents the first implementation of a distributed large-scale, multi-model experiment in the ESGF/CMIP context, joining together server-side approaches for scientific data analysis, HPDA frameworks, end-to-end workflow management, and cloud computing.

Multimodel Evidence for an Atmospheric Circulation Response to Arctic Sea Ice Loss in the CMIP5 Future Projections

NASA Astrophysics Data System (ADS)

Zappa, G.; Pithan, F.; Shepherd, T. G.

2018-01-01

Previous single-model experiments have found that Arctic sea ice loss can influence the atmospheric circulation. To evaluate this process in a multimodel ensemble, a novel methodology is here presented and applied to infer the influence of Arctic sea ice loss in the CMIP5 future projections. Sea ice influence is estimated by comparing the circulation response in the RCP8.5 scenario against the circulation response to sea surface warming and CO2 increase inferred from the AMIPFuture and AMIP4xCO2 experiments, where sea ice is unperturbed. Multimodel evidence of the impact of sea ice loss on midlatitude atmospheric circulation is identified in late winter (January-March), when the sea ice-related surface heat flux perturbation is largest. Sea ice loss acts to suppress the projected poleward shift of the North Atlantic jet, to increase surface pressure in northern Siberia, and to lower it in North America. These features are consistent with previous single-model studies, and the present results indicate that they are robust to model formulation.

Multimodel Evidence for an Atmospheric Circulation Response to Arctic Sea Ice Loss in the CMIP5 Future Projections.

PubMed

Zappa, G; Pithan, F; Shepherd, T G

2018-01-28

Previous single-model experiments have found that Arctic sea ice loss can influence the atmospheric circulation. To evaluate this process in a multimodel ensemble, a novel methodology is here presented and applied to infer the influence of Arctic sea ice loss in the CMIP5 future projections. Sea ice influence is estimated by comparing the circulation response in the RCP8.5 scenario against the circulation response to sea surface warming and CO 2 increase inferred from the AMIPFuture and AMIP4xCO2 experiments, where sea ice is unperturbed. Multimodel evidence of the impact of sea ice loss on midlatitude atmospheric circulation is identified in late winter (January-March), when the sea ice-related surface heat flux perturbation is largest. Sea ice loss acts to suppress the projected poleward shift of the North Atlantic jet, to increase surface pressure in northern Siberia, and to lower it in North America. These features are consistent with previous single-model studies, and the present results indicate that they are robust to model formulation.

Northern Winter Climate Change: Assessment of Uncertainty in CMIP5 Projections Related to Stratosphere-Troposphere Coupling

NASA Technical Reports Server (NTRS)

Manzini, E.; Karpechko, A.Yu.; Anstey, J.; Shindell, Drew Todd; Baldwin, M.P.; Black, R.X.; Cagnazzo, C.; Calvo, N.; Charlton-Perez, A.; Christiansen, B.;

The centrality of meta-programming in the ES-DOC eco-system

NASA Astrophysics Data System (ADS)

Greenslade, Mark

2017-04-01

The Earth System Documentation (ES-DOC) project is an international effort aiming to deliver a robust earth system model inter-comparison project documentation infrastructure. Such infrastructure both simplifies & standardizes the process of documenting (in detail) projects, experiments, models, forcings & simulations. In support of CMIP6, ES-DOC has upgraded its eco-system of tools, web-services & web-sites. The upgrade consolidates the existing infrastructure (built for CMIP5) and extends it with the introduction of new capabilities. The strategic focus of the upgrade is improvements in the documentation experience and broadening the range of scientific use-cases that the archived documentation may help deliver. Whether it is highlighting dataset errors, exploring experimental protocols, comparing forcings across ensemble runs, understanding MIP objectives, reviewing citations, exploring component properties of configured models, visualising inter-model relationships, scientists involved in CMIP6 will find the ES-DOC infrastructure helpful. This presentation underlines the centrality of meta-programming within the ES-DOC eco-system. We will demonstrate how agility is greatly enhanced by taking a meta-programming approach to representing data models and controlled vocabularies. Such an approach nicely decouples representations from encodings. Meta-models will be presented along with the associated tooling chain that forward engineers artefacts as diverse as: class hierarchies, IPython notebooks, mindmaps, configuration files, OWL & SKOS documents, spreadsheets …etc.

Climate Change Impact Study with CMIP5 and Comparison with CMIP3

NASA Astrophysics Data System (ADS)

Wang, J.; Yin, H.; Reyes, E.; Chung, F. I.

2016-12-01

One of significant uncertainties in climate change impact study is the selection of climate model projection including the choosing of greenhouse gas emission scenarios. With the new generation of climate model projection, CMIP5, coming into use, CCTAG selected 11 climate models and two RCPs (rcp4.5 and rcp8.5) for California. Previous DWR climate change study was based on 6 CMIP3 climate models and two emission scenarios (SRES A2 and B1) which were selected by CAT. It is an unanswered question that how the selection of these climate model projections and emission scenarios affect the assessment of climate change impact on future water supply of California CVP/SWP project. This work will run the water planning model CalSim in DWR with 44 CMIP5 and 12 CMIP3 climate model projections to investigate the sensitivity of climate model impact study on future water supply in the CVP/SWP region to the section of climate model projection. It was found that in 2060 CMIP5 projects the wetting trend in Northern California while CMIP3 projects the drying trend in the entire California on the average. And CMIP5 projects about half-degree more warming than CMIP3. As a result, Sacramento River rim inflow increases by 8% for CMIP5 and reduces by 3% for CMIP3. In spite of this difference in rim inflow, north of Delta carryover storage will be reduced both under CMIP5 (14%) and under CMIP3 (23%) in 2060. And south Delta export will be reduced both for CMIP5 (8%) and for CMIP3 (15%). Thus, The CC impact uncertainty caused by the selection of climate model projection (CMIP3 vs CMIP5) is about 7% in terms of Delta export and about 9% in terms of north of Delta carryover storage. This uncertainty is more than the one caused by the selection of sea level rise in that the climate change impact uncertainty caused by the selection of sea level rise (Zero vs 1.5ft SLR) is about 5% in terms of Delta export and about 4-5% in terms of North of Delta carryover storage.

Climate Change Impact Assessment in Pacific North West Using Copula based Coupling of Temperature and Precipitation variables

NASA Astrophysics Data System (ADS)

Qin, Y.; Rana, A.; Moradkhani, H.

2014-12-01

The multi downscaled-scenario products allow us to better assess the uncertainty of the changes/variations of precipitation and temperature in the current and future periods. Joint Probability distribution functions (PDFs), of both the climatic variables, might help better understand the interdependence of the two, and thus in-turn help in accessing the future with confidence. Using the joint distribution of temperature and precipitation is also of significant importance in hydrological applications and climate change studies. In the present study, we have used multi-modelled statistically downscaled-scenario ensemble of precipitation and temperature variables using 2 different statistically downscaled climate dataset. The datasets used are, 10 Global Climate Models (GCMs) downscaled products from CMIP5 daily dataset, namely, those from the Bias Correction and Spatial Downscaling (BCSD) technique generated at Portland State University and from the Multivariate Adaptive Constructed Analogs (MACA) technique, generated at University of Idaho, leading to 2 ensemble time series from 20 GCM products. Thereafter the ensemble PDFs of both precipitation and temperature is evaluated for summer, winter, and yearly periods for all the 10 sub-basins across Columbia River Basin (CRB). Eventually, Copula is applied to establish the joint distribution of two variables enabling users to model the joint behavior of the variables with any level of correlation and dependency. Moreover, the probabilistic distribution helps remove the limitations on marginal distributions of variables in question. The joint distribution is then used to estimate the change trends of the joint precipitation and temperature in the current and future, along with estimation of the probabilities of the given change. Results have indicated towards varied change trends of the joint distribution of, summer, winter, and yearly time scale, respectively in all 10 sub-basins. Probabilities of changes, as estimated by the joint precipitation and temperature, will provide useful information/insights for hydrological and climate change predictions.

Impacts of weather versus climate and driver uncertainty on multi-centennial ecosystem model simulations

NASA Astrophysics Data System (ADS)

Rollinson, C.; Simkins, J.; Fer, I.; Desai, A. R.; Dietze, M.

2017-12-01

Simulations of ecosystem dynamics and comparisons with empirical data require accurate, continuous, and often sub-daily meteorology records that are spatially aligned to the scale of the empirical data. A wealth of meteorology data for the past, present, and future is available through site-specific observations, modern reanalysis products, and gridded GCM simulations. However, these products are mismatched in spatial and temporal resolution, often with both different means and seasonal patterns. We have designed and implemented a two-step meteorological downscaling and ensemble generation method that combines multiple meteorology data products through debiasing and temporal downscaling protocols. Our methodology is designed to preserve the covariance among seven meteorological variables for use as drivers in ecosystem model simulations: temperature, precipitation, short- and longwave radiation, surface pressure, humidity, and wind. Furthermore, our method propagates uncertainty through the downscaling process and results in ensembles of meteorology that can be compared to paleoclimate reconstructions and used to analyze the effects of both high- and low-frequency climate anomalies on ecosystem dynamics. Using a multiple linear regression approach, we have combined hourly, 0.125-degree gridded data from the NLDAS (1980-present) with CRUNCEP (1901-2010) and CMIP5 historical (1850-2005), past millennium (850-1849), and future (1950-2100) GCM simulations. This has resulted in an ensemble of continuous, hourly-resolved meteorology from from the paleo era into the future with variability in weather events as well as low-frequency climatic changes. We investigate the influence of extreme sub-daily weather phenomena versus long-term climatic changes in an ensemble of ecosystem models that range in atmospheric and biological complexity. Through data assimilation with paleoclimate reconstructions of past climate, we can improve data-model comparisons using observations of vegetation change from the past 1200 years. Accounting for driver uncertainty in model evaluation can help determine the relative influence of structural versus parameterization errors in ecosystem modelings.

Regime-based evaluation of cloudiness in CMIP5 models

NASA Astrophysics Data System (ADS)

Jin, Daeho; Oreopoulos, Lazaros; Lee, Dongmin

2017-01-01

The concept of cloud regimes (CRs) is used to develop a framework for evaluating the cloudiness of 12 fifth Coupled Model Intercomparison Project (CMIP5) models. Reference CRs come from existing global International Satellite Cloud Climatology Project (ISCCP) weather states. The evaluation is made possible by the implementation in several CMIP5 models of the ISCCP simulator generating in each grid cell daily joint histograms of cloud optical thickness and cloud top pressure. Model performance is assessed with several metrics such as CR global cloud fraction (CF), CR relative frequency of occurrence (RFO), their product [long-term average total cloud amount (TCA)], cross-correlations of CR RFO maps, and a metric of resemblance between model and ISCCP CRs. In terms of CR global RFO, arguably the most fundamental metric, the models perform unsatisfactorily overall, except for CRs representing thick storm clouds. Because model CR CF is internally constrained by our method, RFO discrepancies yield also substantial TCA errors. Our results support previous findings that CMIP5 models underestimate cloudiness. The multi-model mean performs well in matching observed RFO maps for many CRs, but is still not the best for this or other metrics. When overall performance across all CRs is assessed, some models, despite shortcomings, apparently outperform Moderate Resolution Imaging Spectroradiometer cloud observations evaluated against ISCCP like another model output. Lastly, contrasting cloud simulation performance against each model's equilibrium climate sensitivity in order to gain insight on whether good cloud simulation pairs with particular values of this parameter, yields no clear conclusions.

Probabilistic Near and Far-Future Climate Scenarios of Precipitation and Surface Temperature for the North American Monsoon Region Under a Weighted CMIP5-GCM Ensemble Approach.

NASA Astrophysics Data System (ADS)

Montero-Martinez, M. J.; Colorado, G.; Diaz-Gutierrez, D. E.; Salinas-Prieto, J. A.

2017-12-01

It is well known the North American Monsoon (NAM) region is already a very dry region which is under a lot of stress due to the lack of water resources on multiple locations of the area. However, it is very interesting that even under those conditions, the Mexican part of the NAM region is certainly the most productive in Mexico from the agricultural point of view. Thus, it is very important to have realistic climate scenarios for climate variables such as temperature, precipitation, relative humidity, radiation, etc. This study tries to tackle that problem by generating probabilistic climate scenarios using a weighted CMIP5-GCM ensemble approach based on the Xu et al. (2010) technique which is on itself an improved method from the better known Reliability Ensemble Averaging algorithm of Giorgi and Mearns (2002). In addition, it is compared the 20-plus GCMs individual performances and the weighted ensemble versus observed data (CRU TS2.1) by using different metrics and Taylor diagrams. This study focuses on probabilistic results reaching a certain threshold given the fact that those types of products could be of potential use for agricultural applications.

Polar clouds and radiation in satellite observations, reanalyses, and climate models

NASA Astrophysics Data System (ADS)

Lenaerts, Jan T. M.; Van Tricht, Kristof; Lhermitte, Stef; L'Ecuyer, Tristan S.

2017-04-01

Clouds play a pivotal role in the surface energy budget of the polar regions. Here we use two largely independent data sets of cloud and surface downwelling radiation observations derived by satellite remote sensing (2007-2010) to evaluate simulated clouds and radiation over both polar ice sheets and oceans in state-of-the-art atmospheric reanalyses (ERA-Interim and Modern Era Retrospective-Analysis for Research and Applications-2) and the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate model ensemble. First, we show that, compared to Clouds and the Earth's Radiant Energy System-Energy Balanced and Filled, CloudSat-CALIPSO better represents cloud liquid and ice water path over high latitudes, owing to its recent explicit determination of cloud phase that will be part of its new R05 release. The reanalyses and climate models disagree widely on the amount of cloud liquid and ice in the polar regions. Compared to the observations, we find significant but inconsistent biases in the model simulations of cloud liquid and ice water, as well as in the downwelling radiation components. The CMIP5 models display a wide range of cloud characteristics of the polar regions, especially with regard to cloud liquid water, limiting the representativeness of the multimodel mean. A few CMIP5 models (CNRM, GISS, GFDL, and IPSL_CM5b) clearly outperform the others, which enhances credibility in their projected future cloud and radiation changes over high latitudes. Given the rapid changes in polar regions and global feedbacks involved, future climate model developments should target improved representation of polar clouds. To that end, remote sensing observations are crucial, in spite of large remaining observational uncertainties, which is evidenced by the substantial differences between the two data sets.

Climate change alters low flows in Europe under global warming of 1.5, 2, and 3 °C

NASA Astrophysics Data System (ADS)

Marx, Andreas; Kumar, Rohini; Thober, Stephan; Rakovec, Oldrich; Wanders, Niko; Zink, Matthias; Wood, Eric F.; Pan, Ming; Sheffield, Justin; Samaniego, Luis

2018-02-01

There is growing evidence that climate change will alter water availability in Europe. Here, we investigate how hydrological low flows are affected under different levels of future global warming (i.e. 1.5, 2, and 3 K with respect to the pre-industrial period) in rivers with a contributing area of more than 1000 km2. The analysis is based on a multi-model ensemble of 45 hydrological simulations based on three representative concentration pathways (RCP2.6, RCP6.0, RCP8.5), five Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs: GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M) and three state-of-the-art hydrological models (HMs: mHM, Noah-MP, and PCR-GLOBWB). High-resolution model results are available at a spatial resolution of 5 km across the pan-European domain at a daily temporal resolution. Low river flow is described as the percentile of daily streamflow that is exceeded 90 % of the time. It is determined separately for each GCM/HM combination and warming scenario. The results show that the low-flow change signal amplifies with increasing warming levels. Low flows decrease in the Mediterranean region, while they increase in the Alpine and Northern regions. In the Mediterranean, the level of warming amplifies the signal from -12 % under 1.5 K, compared to the baseline period 1971-2000, to -35 % under global warming of 3 K, largely due to the projected decreases in annual precipitation. In contrast, the signal is amplified from +22 (1.5 K) to +45 % (3 K) in the Alpine region due to changes in snow accumulation. The changes in low flows are significant for regions with relatively large change signals and under higher levels of warming. However, it is not possible to distinguish climate-induced differences in low flows between 1.5 and 2 K warming because of (1) the large inter-annual variability which prevents distinguishing statistical estimates of period-averaged changes for a given GCM/HM combination, and (2) the uncertainty in the multi-model ensemble expressed by the signal-to-noise ratio. The contribution by the GCMs to the uncertainty in the model results is generally higher than the one by the HMs. However, the uncertainty due to HMs cannot be neglected. In the Alpine, Northern, and Mediterranean regions, the uncertainty contribution by the HMs is partly higher than those by the GCMs due to different representations of processes such as snow, soil moisture and evapotranspiration. Based on the analysis results, it is recommended (1) to use multiple HMs in climate impact studies and (2) to embrace uncertainty information on the multi-model ensemble as well as its single members in the adaptation process.

Evaluation of CMIP5 and CORDEX Derived Wind Wave Climate in Arabian Sea and Bay of Bengal

NASA Astrophysics Data System (ADS)

Chowdhury, P.; Behera, M. R.

2017-12-01

Climate change impact on surface ocean wave parameters need robust assessment for effective coastal zone management. Climate model skill to simulate dynamical General Circulation Models (GCMs) and Regional Circulation Models (RCMs) forced wind-wave climate over northern Indian Ocean is assessed in the present work. The historical dynamical wave climate is simulated using surface winds derived from four GCMs and four RCMs, participating in the Coupled Model Inter-comparison Project (CMIP5) and Coordinated Regional Climate Downscaling Experiment (CORDEX-South Asia), respectively, and their ensemble are used to force a spectral wave model. The surface winds derived from GCMs and RCMs are corrected for bias, using Quantile Mapping method, before being forced to the spectral wave model. The climatological properties of wave parameters (significant wave height (Hs), mean wave period (Tp) and direction (θm)) are evaluated relative to ERA-Interim historical wave reanalysis datasets over Arabian Sea (AS) and Bay of Bengal (BoB) regions of the northern Indian Ocean for a period of 27 years. We identify that the nearshore wave climate of AS is better predicted than the BoB by both GCMs and RCMs. Ensemble GCM simulated Hs in AS has a better correlation with ERA-Interim ( 90%) than in BoB ( 80%), whereas ensemble RCM simulated Hs has a low correlation in both regions ( 50% in AS and 45% in BoB). In AS, ensemble GCM simulated Tp has better predictability ( 80%) compared to ensemble RCM ( 65%). However, neither GCM nor RCM could satisfactorily predict Tp in nearshore BoB. Wave direction is poorly simulated by GCMs and RCMs in both AS and BoB, with correlation around 50% with GCMs and 60% with RCMs wind derived simulations. However, upon comparing individual RCMs with their parent GCMs, it is found that few of the RCMs predict wave properties better than their parent GCMs. It may be concluded that there is no consistent added value by RCMs over GCMs forced wind-wave climate over northern Indian Ocean. We also identify that there is little to no significance of choosing a finer resolution GCM ( 1.4°) over a coarse GCM ( 2.8°) in improving skill of GCM forced dynamical wave simulations.

Application of Multi-Model CMIP5 Analysis in Future Drought Adaptation Strategies

NASA Astrophysics Data System (ADS)

Casey, M.; Luo, L.; Lang, Y.

2014-12-01

Drought influences the efficacy of numerous natural and artificial systems including species diversity, agriculture, and infrastructure. Global climate change raises concerns that extend well beyond atmospheric and hydrological disciplines - as climate changes with time, the need for system adaptation becomes apparent. Drought, as a natural phenomenon, is typically defined relative to the climate in which it occurs. Typically a 30-year reference time frame (RTF) is used to determine the severity of a drought event. This study investigates the projected future droughts over North America with different RTFs. Confidence in future hydroclimate projection is characterized by the agreement of long term (2005-2100) multi-model precipitation (P) and temperature (T) projections within the Coupled model Intercomparison Project Phase 5 (CMIP5). Drought severity and the propensity of extreme conditions are measured by the multi-scalar, probabilistic, RTF-based Standard Precipitation Index (SPI) and Standard Precipitation Evapotranspiration Index (SPEI). SPI considers only P while SPEI incorporates Evapotranspiration (E) via T; comparing the two reveals the role of temperature change in future hydroclimate change. Future hydroclimate conditions, hydroclimate extremity, and CMIP5 model agreement are assessed for each Representative Concentration Pathway (RCP 2.6, 4.5, 6.0, 8.5) in regions throughout North America for the entire year and for the boreal seasons. In addition, multiple time scales of SPI and SPEI are calculated to characterize drought at time scales ranging from short to long term. The study explores a simple, standardized method for considering adaptation in future drought assessment, which provides a novel perspective to incorporate adaptation with climate change. The result of the analysis is a multi-dimension, probabilistic summary of the hydrological (P, E) environment a natural or artificial system must adapt to over time. Studies similar to this with specified criteria (SPI/SPEI value, time scale, RCP, etc.) can provide professionals in a variety of disciplines with necessary climatic insight to develop adaptation strategies.

North Tropical Atlantic Climate Variability and Model Biases

NASA Astrophysics Data System (ADS)

Yang, Y.

2017-12-01

Remote forcing from El Niño-Southern Oscillation (ENSO) and local ocean-atmosphere feedback are important for climate variability over the North Tropical Atlantic. These two factors are extracted by the ensemble mean and inter-member difference of a 10-member Pacific Ocean-Global Atmosphere (POGA) experiment, in which sea surface temperatures (SSTs) are restored to the observed anomalies over the tropical Pacific but fully coupled to the atmosphere elsewhere. POGA reasonably captures main features of observed North Tropical Atlantic variability. ENSO forced and local North Tropical Atlantic modes (NTAMs) develop with wind-evaporation-SST feedback, explaining one third and two thirds of total variance respectively. Notable biases, however, exist. The seasonality of the simulated NTAM is delayed by one month, due to the late development of the North Atlantic Oscillation (NAO) in the model. A spurious band of enhanced sea surface temperature (SST) variance (SBEV) is identified over the northern equatorial Atlantic in POGA and 14 out of 23 CMIP5 models. The SBEV is especially pronounced in boreal spring and due to the combined effect of both anomalous atmospheric thermal forcing and oceanic vertical upwelling. While the tropical North Atlantic variability is only weakly correlated with the Atlantic Zonal Mode (AZM) in observations, the SBEV in CMIP5 produces conditions that drive and intensify the AZM variability via triggering the Bjerknes feedback. This partially explains why AZM is strong in some CMIP5 models even though the equatorial cold tongue and easterly trades are biased low.

On the relationships among cloud cover, mixed-phase partitioning, and planetary albedo in GCMs

DOE PAGES

McCoy, Daniel T.; Tan, Ivy; Hartmann, Dennis L.; ...

2016-05-06

In this study, it is shown that CMIP5 global climate models (GCMs) that convert supercooled water to ice at relatively warm temperatures tend to have a greater mean-state cloud fraction and more negative cloud feedback in the middle and high latitude Southern Hemisphere. We investigate possible reasons for these relationships by analyzing the mixed-phase parameterizations in 26 GCMs. The atmospheric temperature where ice and liquid are equally prevalent (T5050) is used to characterize the mixed-phase parameterization in each GCM. Liquid clouds have a higher albedo than ice clouds, so, all else being equal, models with more supercooled liquid water wouldmore » also have a higher planetary albedo. The lower cloud fraction in these models compensates the higher cloud reflectivity and results in clouds that reflect shortwave radiation (SW) in reasonable agreement with observations, but gives clouds that are too bright and too few. The temperature at which supercooled liquid can remain unfrozen is strongly anti-correlated with cloud fraction in the climate mean state across the model ensemble, but we know of no robust physical mechanism to explain this behavior, especially because this anti-correlation extends through the subtropics. A set of perturbed physics simulations with the Community Atmospheric Model Version 4 (CAM4) shows that, if its temperature-dependent phase partitioning is varied and the critical relative humidity for cloud formation in each model run is also tuned to bring reflected SW into agreement with observations, then cloud fraction increases and liquid water path (LWP) decreases with T5050, as in the CMIP5 ensemble.« less

Tropical cyclone genesis potential index over the western North Pacific simulated by CMIP5 models

NASA Astrophysics Data System (ADS)

Song, Yajuan; Wang, Lei; Lei, Xiaoyan; Wang, Xidong

2015-11-01

Tropical cyclone (TC) genesis over the western North Pacific (WNP) is analyzed using 23 CMIP5 (Coupled Model Intercomparison Project Phase 5) models and reanalysis datasets. The models are evaluated according to TC genesis potential index (GPI). The spatial and temporal variations of the GPI are first calculated using three atmospheric reanalysis datasets (ERA-Interim, NCEP/NCAR Reanalysis-1, and NCEP/DOE Reanalysis-2). Spatial distributions of July-October-mean TC frequency based on the GPI from ERA-interim are more consistent with observed ones derived from IBTrACS global TC data. So, the ERA-interim reanalysis dataset is used to examine the CMIP5 models in terms of reproducing GPI during the period 1982-2005. Although most models possess deficiencies in reproducing the spatial distribution of the GPI, their multimodel ensemble (MME) mean shows a reasonable climatological GPI pattern characterized by a high GPI zone along 20°N in the WNP. There was an upward trend of TC genesis frequency during 1982 to 1998, followed by a downward trend. Both MME results and reanalysis data can represent a robust increasing trend during 1982-1998, but the models cannot simulate the downward trend after 2000. Analysis based on future projection experiments shows that the GPI exhibits no significant change in the first half of the 21st century, and then starts to decrease at the end of the 21st century under the representative concentration pathway (RCP) 2.6 scenario. Under the RCP8.5 scenario, the GPI shows an increasing trend in the vicinity of 20°N, indicating more TCs could possibly be expected over the WNP under future global warming.

The Change of Climate and Terrestrial Carbon Cycle over Tibetan Plateau in CMIP5 Models

NASA Astrophysics Data System (ADS)

Li, S.

2015-12-01

Six earth system models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are evaluated over Tibetan Plateau (TP) by comparing the modeled temperature (Tas), precipitation (Pr), net primary production (NPP) and leaf area index (LAI) with the observed Tas, Pr, IGBP NPP and MPIM LAI in the historical, and then we analyzed the change of climate and carbon cycle and explored the relationship between the carbon cycle and main climatic drivers in the historical and representative concentration pathway 4.5 (RCP4.5) simulation over TP. While model results differ, their region spatial distributions from 1971 to 2000 agree reasonably with observed Tas, Pr and proxy LAI and NPP. The climatic variables, LAI and carbon flux vary between two simulations, the ration of NPP to gross primary production (GPP) does not change much in the historical and RCP4.5 scenarios. The linear trends of LAI and carbon flux show an obvious continuous increase from historical climatic period (1971-2000) to the first two climatic periods (2011-2040; 2041-2700) of RCP4.5, then the trends decrease in the third climatic period (2071-2100) of RCP4.5. The cumulative multi model ensemble (MME) net biome production (NBP) is 0.32 kgCm-2yr-1 during 1850 to 2005 and 1.43 kgCm-2yr-1 during 2006 to 2100, the Tibetan Plateau is a carbon sink during the historical scenario, and TP will uptake more carbon from atmosphere during 2006 to 2100 than 1850 to 2005 under RCP4.5 scenario. LAI, GPP, NPP, Ra and Rh appear more related to the Tas than Pr and Rsds, and the Tas is the primary climatic driver for the plant growth and carbon cycle. With the climate change in twenty-first century under RCP4.5 scenario, Tas still is the primary climate driver for the plant growth and carbon cycle, but the effect of temperature on plant growth and carbon cycle gets weaker.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boer, George J.; Smith, Douglas M.; Cassou, Christophe

The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Predictionmore » (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Furthermore, groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.« less

Intensification of Chile-Peru upwelling under climate change: diagnosing the impact of natural and anthropogenic forcing from the IPSL-CM5 model.

NASA Astrophysics Data System (ADS)

Jebri, B.; Khodri, M.; Gastineau, G.; Echevin, V.; Thiria, S.

2017-12-01

Upwelling is critical to the biological production, acidification, and deoxygenation of the ocean's major eastern boundary current ecosystems. A conceptual hypothesis suggests that the winds that favour coastal upwelling intensify with anthropogenic global warming due to increased land-sea temperature contrast. We examine this hypothesis for the dynamics of the Peru-Chile upwelling using a set of four large ensembles of coupled, ocean-atmosphere model simulations with the IPSL model covering the 1940-2014 period. In one large ensemble we prescribe the standard CMIP5 greenhouse gas (GHG) concentrations, anthropogenic aerosol, ozone and volcanic forcings, following the historical experiments through 2005 and RCP8.5 from 2006-2014, while the other ensembles consider separately the GHG, ozone and volcanic forcings. We find evidence for intensification of upwelling-favourable winds with however little evidence of atmospheric pressure gradients in response to increasing land-sea temperature differences. Our analyses reveal poleward migration and intensification of the South Pacific Anticyclone near poleward boundaries of climatological Peruvian and Chilean upwelling zones. This contribution further investigates the physical mechanisms for the Peru-Chile upwelling intensification and the relative role of natural and anthropogenic forcings.

Causes and Implications of Persistent Atmospheric Carbon Dioxide Biases in Earth System Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hoffman, Forrest M; Randerson, James T.; Arora, Vivek K.

The strength of feedbacks between a changing climate and future CO2 concentrations are uncertain and difficult to predict using Earth System Models (ESMs). We analyzed emission-driven simulations--in which atmospheric CO2 levels were computed prognostically--for historical (1850-2005) and future periods (RCP 8.5 for 2006-2100) produced by 15 ESMs for the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). Comparison of ESM prognostic atmospheric CO2 over the historical period with observations indicated that ESMs, on average, had a small positive bias in predictions of contemporary atmospheric CO2. Weak ocean carbon uptake in many ESMs contributed to this bias, based on comparisonsmore » with observations of ocean and atmospheric anthropogenic carbon inventories. We found a significant linear relationship between contemporary atmospheric CO2 biases and future CO2 levels for the multi-model ensemble. We used this relationship to create a contemporary CO2 tuned model (CCTM) estimate of the atmospheric CO2 trajectory for the 21st century. The CCTM yielded CO2 estimates of 600 {plus minus} 14 ppm at 2060 and 947 {plus minus} 35 ppm at 2100, which were 21 ppm and 32 ppm below the multi-model mean during these two time periods. Using this emergent constraint approach, the likely ranges of future atmospheric CO2, CO2-induced radiative forcing, and CO2-induced temperature increases for the RCP 8.5 scenario were considerably narrowed compared to estimates from the full ESM ensemble. Our analysis provided evidence that much of the model-to-model variation in projected CO2 during the 21st century was tied to biases that existed during the observational era, and that model differences in the representation of concentration-carbon feedbacks and other slowly changing carbon cycle processes appear to be the primary driver of this variability. By improving models to more closely match the long-term time series of CO2 from Mauna Loa, our analysis suggests uncertainties in future climate projections can be reduced.« less

Monitoring the Earth System Grid Federation through the ESGF Dashboard

NASA Astrophysics Data System (ADS)

Fiore, S.; Bell, G. M.; Drach, B.; Williams, D.; Aloisio, G.

2012-12-01

The Climate Model Intercomparison Project, phase 5 (CMIP5) is a global effort coordinated by the World Climate Research Programme (WCRP) involving tens of modeling groups spanning 19 countries. It is expected the CMIP5 distributed data archive will total upwards of 3.5 petabytes, stored across several ESGF Nodes on four continents (North America, Europe, Asia, and Australia). The Earth System Grid Federation (ESGF) provides the IT infrastructure to support the CMIP5. In this regard, the monitoring of the distributed ESGF infrastructure represents a crucial part carried out by the ESGF Dashboard. The ESGF Dashboard is a software component of the ESGF stack, responsible for collecting key information about the status of the federation in terms of: 1) Network topology (peer-groups composition), 2) Node type (host/services mapping), 3) Registered users (including their Identity Providers), 4) System metrics (e.g., round-trip time, service availability, CPU, memory, disk, processes, etc.), 5) Download metrics (both at the Node and federation level). The last class of information is very important since it provides a strong insight of the CMIP5 experiment: the data usage statistics. In this regard, CMCC and LLNL have developed a data analytics management system for the analysis of both node-level and federation-level data usage statistics. It provides data usage statistics aggregated by project, model, experiment, variable, realm, peer node, time, ensemble, datasetname (including version), etc. The back-end of the system is able to infer the data usage information of the entire federation, by carrying out: - at node level: a 18-step reconciliation process on the peer node databases (i.e. node manager and publisher DB) which provides a 15-dimension datawarehouse with local statistics and - at global level: an aggregation process which federates the data usage statistics into a 16-dimension datawarehouse with federation-level data usage statistics. The front-end of the Dashboard system exploits a web desktop approach, which joins the pervasivity of a web application with the flexibility of a desktop one.

The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6

DOE PAGES

O'Neill, Brian C.; Tebaldi, Claudia; van Vuuren, Detlef P.; ...

2016-09-28

Projections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. Here, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate a wide rangemore » of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. Furthermore, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2°C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. In order to serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017–2018 time frame, and output from the climate model projections made available and analyses performed over the 2018–2020 period.« less

The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6

DOE Office of Scientific and Technical Information (OSTI.GOV)

O'Neill, Brian C.; Tebaldi, Claudia; van Vuuren, Detlef P.

2016-01-01

Projections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. In this paper, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate amore » wide range of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the same time, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2 °C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. To serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017–2018 time frame, and output from the climate model projections made available and analyses performed over the 2018–2020 period.« less

The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6

NASA Astrophysics Data System (ADS)

O'Neill, Brian C.; Tebaldi, Claudia; van Vuuren, Detlef P.; Eyring, Veronika; Friedlingstein, Pierre; Hurtt, George; Knutti, Reto; Kriegler, Elmar; Lamarque, Jean-Francois; Lowe, Jason; Meehl, Gerald A.; Moss, Richard; Riahi, Keywan; Sanderson, Benjamin M.

2016-09-01

Projections of future climate change play a fundamental role in improving understanding of the climate system as well as characterizing societal risks and response options. The Scenario Model Intercomparison Project (ScenarioMIP) is the primary activity within Phase 6 of the Coupled Model Intercomparison Project (CMIP6) that will provide multi-model climate projections based on alternative scenarios of future emissions and land use changes produced with integrated assessment models. In this paper, we describe ScenarioMIP's objectives, experimental design, and its relation to other activities within CMIP6. The ScenarioMIP design is one component of a larger scenario process that aims to facilitate a wide range of integrated studies across the climate science, integrated assessment modeling, and impacts, adaptation, and vulnerability communities, and will form an important part of the evidence base in the forthcoming Intergovernmental Panel on Climate Change (IPCC) assessments. At the same time, it will provide the basis for investigating a number of targeted science and policy questions that are especially relevant to scenario-based analysis, including the role of specific forcings such as land use and aerosols, the effect of a peak and decline in forcing, the consequences of scenarios that limit warming to below 2 °C, the relative contributions to uncertainty from scenarios, climate models, and internal variability, and long-term climate system outcomes beyond the 21st century. To serve this wide range of scientific communities and address these questions, a design has been identified consisting of eight alternative 21st century scenarios plus one large initial condition ensemble and a set of long-term extensions, divided into two tiers defined by relative priority. Some of these scenarios will also provide a basis for variants planned to be run in other CMIP6-Endorsed MIPs to investigate questions related to specific forcings. Harmonized, spatially explicit emissions and land use scenarios generated with integrated assessment models will be provided to participating climate modeling groups by late 2016, with the climate model simulations run within the 2017-2018 time frame, and output from the climate model projections made available and analyses performed over the 2018-2020 period.

Uncertainty in future agro-climate projections in the United States and benefits of greenhouse gas mitigation

DOE PAGES

Monier, Erwan; Xu, Liyi; Snyder, Richard

2016-04-26

Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios,more » climate sensitivities, and representations of natural variability. By the end of the century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations-although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Lastly, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.« less

Uncertainty in future agro-climate projections in the United States and benefits of greenhouse gas mitigation

NASA Astrophysics Data System (ADS)

Monier, Erwan; Xu, Liyi; Snyder, Richard

2016-05-01

Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios, climate sensitivities, and representations of natural variability. By the end of the century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations—although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Finally, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.

Uncertainty in future agro-climate projections in the United States and benefits of greenhouse gas mitigation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Monier, Erwan; Xu, Liyi; Snyder, Richard

Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios,more » climate sensitivities, and representations of natural variability. By the end of the century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations-although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Lastly, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.« less

Does the Arctic Amplification peak this decade?

NASA Astrophysics Data System (ADS)

Martin, Torge; Haine, Thomas W. N.

2017-04-01

Temperatures rise faster in the Arctic than on global average, a phenomenon known as Arctic Amplification. While this is well established from observations and model simulations, projections of future climate (here: RCP8.5) with models of the Coupled Model Intercomparison Project phase 5 (CMIP5) also indicate that the Arctic Amplification has a maximum. We show this by means of an Arctic Amplification factor (AAF), which we define as the ratio of Arctic mean to global mean surface air temperature (SAT) anomalies. The SAT anomalies are referenced to the period 1960-1980 and smoothed by a 30-year running mean. For October, the multi-model ensemble-mean AAF reaches a maximum in 2017. The maximum moves however to later years as Arctic winter progresses: for the autumn mean SAT (September to November) the maximum AAF is found in 2028 and for winter (December to February) in 2060. Arctic Amplification is driven, amongst others, by the ice-albedo feedback (IAF) as part of the more general surface albedo feedback (involving clouds, snow cover, vegetation changes) and temperature effects (Planck and lapse-rate feedbacks). We note that sea ice retreat and the associated warming of the summer Arctic Ocean are not only an integral part of the IAF but are also involved in the other drivers. In the CMIP5 simulations, the timing of the AAF maximum coincides with the period of fastest ice retreat for the respective month. Presence of at least some sea ice is crucial for the IAF to be effective because of the contrast in surface albedo between ice and open water and the need to turn ocean warming into ice melt. Once large areas of the Arctic Ocean are ice-free, the IAF should be less effective. We thus hypothesize that the ice retreat significantly affects AAF variability and forces a decline of its magnitude after at least half of the Arctic Ocean is ice-free and the ice cover becomes basically seasonal.

Projected changes in significant wave height toward the end of the 21st century: Northeast Atlantic

NASA Astrophysics Data System (ADS)

Aarnes, Ole Johan; Reistad, Magnar; Breivik, Øyvind; Bitner-Gregersen, Elzbieta; Ingolf Eide, Lars; Gramstad, Odin; Magnusson, Anne Karin; Natvig, Bent; Vanem, Erik

2017-04-01

Wind field ensembles from six CMIP5 models force wave model time slices of the northeast Atlantic over the last three decades of the 20th and the 21st centuries. The future wave climate is investigated by considering the RCP4.5 and RCP8.5 emission scenarios. The CMIP5 model selection is based on their ability to reconstruct the present (1971-2000) extratropical cyclone activity, but increased spatial resolution has also been emphasized. In total, the study comprises 35 wave model integrations, each about 30 years long, in total more than 1000 years. Here annual statistics of significant wave height are analyzed, including mean parameters and upper percentiles. There is general agreement among all models considered that the mean significant wave height is expected to decrease by the end of the 21st century. This signal is statistically significant also for higher percentiles, but less evident for annual maxima. The RCP8.5 scenario yields the strongest reduction in wave height. The exception to this is the north western part of the Norwegian Sea and the Barents Sea, where receding ice cover gives longer fetch and higher waves. The upper percentiles are reduced less than the mean wave height, suggesting that the future wave climate has higher variance than the historical period.

High-resolution dynamic downscaling of CMIP5 output over the Tropical Andes

NASA Astrophysics Data System (ADS)

Reichler, Thomas; Andrade, Marcos; Ohara, Noriaki

2015-04-01

Our project is targeted towards making robust predictions of future changes in climate over the tropical part of the South American Andes. This goal is challenging, since tropical lowlands, steep mountains, and snow covered subarctic surfaces meet over relatively short distances, leading to distinct climate regimes within the same domain and pronounced spatial gradients in virtually every climate quantity. We use an innovative approach to solve this problem, including several quadruple nested versions of WRF, a systematic validation strategy to find the version of WRF that best fits our study region, spatial resolutions at the kilometer scale, 20-year-long simulation periods, and bias-corrected output from various CMIP5 simulations that also include the multi-model mean of all CMIP5 models. We show that the simulated changes in climate are consistent with the results from the global climate models and also consistent with two different versions of WRF. We also discuss the expected changes in snow and ice, derived from off-line coupling the regional simulations to a carefully calibrated snow and ice model.

Large scale and cloud-based multi-model analytics experiments on climate change data in the Earth System Grid Federation

NASA Astrophysics Data System (ADS)

Fiore, Sandro; Płóciennik, Marcin; Doutriaux, Charles; Blanquer, Ignacio; Barbera, Roberto; Donvito, Giacinto; Williams, Dean N.; Anantharaj, Valentine; Salomoni, Davide D.; Aloisio, Giovanni

2017-04-01

In many scientific domains such as climate, data is often n-dimensional and requires tools that support specialized data types and primitives to be properly stored, accessed, analysed and visualized. Moreover, new challenges arise in large-scale scenarios and eco-systems where petabytes (PB) of data can be available and data can be distributed and/or replicated, such as the Earth System Grid Federation (ESGF) serving the Coupled Model Intercomparison Project, Phase 5 (CMIP5) experiment, providing access to 2.5PB of data for the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). A case study on climate models intercomparison data analysis addressing several classes of multi-model experiments is being implemented in the context of the EU H2020 INDIGO-DataCloud project. Such experiments require the availability of large amount of data (multi-terabyte order) related to the output of several climate models simulations as well as the exploitation of scientific data management tools for large-scale data analytics. More specifically, the talk discusses in detail a use case on precipitation trend analysis in terms of requirements, architectural design solution, and infrastructural implementation. The experiment has been tested and validated on CMIP5 datasets, in the context of a large scale distributed testbed across EU and US involving three ESGF sites (LLNL, ORNL, and CMCC) and one central orchestrator site (PSNC). The general "environment" of the case study relates to: (i) multi-model data analysis inter-comparison challenges; (ii) addressed on CMIP5 data; and (iii) which are made available through the IS-ENES/ESGF infrastructure. The added value of the solution proposed in the INDIGO-DataCloud project are summarized in the following: (i) it implements a different paradigm (from client- to server-side); (ii) it intrinsically reduces data movement; (iii) it makes lightweight the end-user setup; (iv) it fosters re-usability (of data, final/intermediate products, workflows, sessions, etc.) since everything is managed on the server-side; (v) it complements, extends and interoperates with the ESGF stack; (vi) it provides a "tool" for scientists to run multi-model experiments, and finally; and (vii) it can drastically reduce the time-to-solution for these experiments from weeks to hours. At the time the contribution is being written, the proposed testbed represents the first concrete implementation of a distributed multi-model experiment in the ESGF/CMIP context joining server-side and parallel processing, end-to-end workflow management and cloud computing. As opposed to the current scenario based on search & discovery, data download, and client-based data analysis, the INDIGO-DataCloud architectural solution described in this contribution addresses the scientific computing & analytics requirements by providing a paradigm shift based on server-side and high performance big data frameworks jointly with two-level workflow management systems realized at the PaaS level via a cloud infrastructure.

Narrowing the surface temperature range in CMIP5 simulations over the Arctic

NASA Astrophysics Data System (ADS)

Hao, Mingju; Huang, Jianbin; Luo, Yong; Chen, Xin; Lin, Yanluan; Zhao, Zongci; Xu, Ying

2018-05-01

Much uncertainty exists in reproducing Arctic temperature using different general circulation models (GCMs). Therefore, evaluating the performance of GCMs in reproducing Arctic temperature is critically important. In our study, 32 GCMs in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) during the period 1900-2005 are used, and several metrics, i.e., bias, correlation coefficient ( R), and root mean square error (RMSE), are applied. The Cowtan data set is adopted as the reference data. The results suggest that the GCMs used can reasonably reproduce the Arctic warming trend during the period 1900-2005, as observed in the observational data, whereas a large variation of inter-model differences exists in modeling the Arctic warming magnitude. With respect to the reference data, most GCMs have large cold biases, whereas others have weak warm biases. Additionally, based on statistical thresholds, the models MIROC-ESM, CSIRO-Mk3-6-0, HadGEM2-AO, and MIROC-ESM-CHEM (bias ≤ ±0.10 °C, R ≥ 0.50, and RMSE ≤ 0.60 °C) are identified as well-performing GCMs. The ensemble of the four best-performing GCMs (ES4), with bias, R, and RMSE values of -0.03 °C, 0.72, and 0.39 °C, respectively, performs better than the ensemble with all 32 members, with bias, R, and RMSE values of -0.04 °C, 0.64, and 0.43 °C, respectively. Finally, ES4 is used to produce projections for the next century under the scenarios of RCP2.6, RCP4.5, and RCP8.0. The uncertainty in the projected temperature is greater in the higher emissions scenarios. Additionally, the projected temperature in the cold half year has larger variations than that in the warm half year.

Global Monsoon Change During the Last Glacial Maximum: A Multi-Model Study

NASA Astrophysics Data System (ADS)

Yan, M.; Wang, B.; Liu, J.

2016-12-01

Change of Global Monsoon (GM) during the Last Glacial Maximum (LGM) is investigated using results from the multi-model ensemble of 7 coupled climate models participated in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The GM changes during LGM are identified by comparison of the results from the pre-industrial control run and the LGM run. The results show (1) The annual mean GM precipitation and GM domain are reduced by about 10% and 5%, respectively; (2) The monsoon intensity (demonstrated by the local summer-minus-winter precipitation) is also weakened over most monsoon regions except Australian monsoon; (3) The monsoon precipitation is reduced more during the local summer than winter; (4) Distinct from all other regional monsoons, the Australian monsoon is strengthened and the monsoon area is enlarged. Four major factors contribute to these changes. The lower greenhouse gas concentration and the presence of the ice sheets decrease air temperature and water vapor content, resulting in a general weakening of the GM precipitation and reduction of GM domain. The reduced hemispheric difference in seasonal variation of insolation may contribute to the weakened GM intensity. The changed land-ocean configuration in the vicinity of the Maritime Continent, along with the presence of the ice sheets and lower greenhouse gas concentration, result in strengthened land-ocean and North-South hemispheric thermal contrasts, leading to the unique strengthened Australian monsoon. Although some of the results are consistent with the proxy data, uncertainties remain in different models. More comparison is needed between proxy data and model experiments to better understand the changes of the GM during the LGM.

Constraining the Sensitivity of Amazonian Rainfall with Observations of Surface Temperature

NASA Astrophysics Data System (ADS)

Dolman, A. J.; von Randow, C.; de Oliveira, G. S.; Martins, G.; Nobre, C. A.

2016-12-01

Earth System models generally do a poor job in predicting Amazonian rainfall, necessitating the need to look for observational constraints on their predictability. We use observed surface temperature and precipitation of the Amazon and a set of 21 CMIP5 models to derive an observational constraint of the sensitivity of rainfall to surface temperature (dP/dT). From first principles such a relation between the surface temperature of the earth and the amount of precipitation through the surface energy balance should exist, particularly in the tropics. When de-trended anomalies in surface temperature and precipitation from a set of datasets are plotted, a clear linear relation between surface temperature and precipitation appears. CMIP5 models show a similar relation with relatively cool models having a larger sensitivity, producing more rainfall. Using the ensemble of models and the observed surface temperature we were able to derive an emerging constraint, reducing the dPdt sensitivity of the CMIP5 model from -0.75 mm day-1 0C-1 (+/- 0.54 SD) to -0.77 mm day-1 0C-1 with a reduced uncertainty of about a factor 5. dPdT from the observation is -0.89 mm day-1 0C-1 . We applied the method to wet and dry season separately noticing that in the wet season we shifted the mean and reduced uncertainty, while in the dry season we very much reduced uncertainty only. The method can be applied to other model simulations such as specific deforestation scenarios to constrain the sensitivity of rainfall to surface temperature. We discuss the implications of the constrained sensitivity for future Amazonian predictions.

Trends of atmospheric circulation during singular hot days in Europe

NASA Astrophysics Data System (ADS)

Jézéquel, Aglaé; Cattiaux, Julien; Naveau, Philippe; Radanovics, Sabine; Ribes, Aurélien; Vautard, Robert; Vrac, Mathieu; Yiou, Pascal

2018-05-01

The influence of climate change on mid-latitudes atmospheric circulation is still very uncertain. The large internal variability makes it difficult to extract any statistically significant signal regarding the evolution of the circulation. Here we propose a methodology to calculate dynamical trends tailored to the circulation of specific days by computing the evolution of the distances between the circulation of the day of interest and the other days of the time series. We compute these dynamical trends for two case studies of the hottest days recorded in two different European regions (corresponding to the heat-waves of summer 2003 and 2010). We use the NCEP reanalysis dataset, an ensemble of CMIP5 models, and a large ensemble of a single model (CESM), in order to account for different sources of uncertainty. While we find a positive trend for most models for 2003, we cannot conclude for 2010 since the models disagree on the trend estimates.

Design and development of a community carbon cycle benchmarking system for CMIP5 models

NASA Astrophysics Data System (ADS)

Mu, M.; Hoffman, F. M.; Lawrence, D. M.; Riley, W. J.; Keppel-Aleks, G.; Randerson, J. T.

2013-12-01

Benchmarking has been widely used to assess the ability of atmosphere, ocean, sea ice, and land surface models to capture the spatial and temporal variability of observations during the historical period. For the carbon cycle and terrestrial ecosystems, the design and development of an open-source community platform has been an important goal as part of the International Land Model Benchmarking (ILAMB) project. Here we designed and developed a software system that enables the user to specify the models, benchmarks, and scoring systems so that results can be tailored to specific model intercomparison projects. We used this system to evaluate the performance of CMIP5 Earth system models (ESMs). Our scoring system used information from four different aspects of climate, including the climatological mean spatial pattern of gridded surface variables, seasonal cycle dynamics, the amplitude of interannual variability, and long-term decadal trends. We used this system to evaluate burned area, global biomass stocks, net ecosystem exchange, gross primary production, and ecosystem respiration from CMIP5 historical simulations. Initial results indicated that the multi-model mean often performed better than many of the individual models for most of the observational constraints.

Development of Spatiotemporal Bias-Correction Techniques for Downscaling GCM Predictions

NASA Astrophysics Data System (ADS)

Hwang, S.; Graham, W. D.; Geurink, J.; Adams, A.; Martinez, C. J.

2010-12-01

Accurately representing the spatial variability of precipitation is an important factor for predicting watershed response to climatic forcing, particularly in small, low-relief watersheds affected by convective storm systems. Although Global Circulation Models (GCMs) generally preserve spatial relationships between large-scale and local-scale mean precipitation trends, most GCM downscaling techniques focus on preserving only observed temporal variability on point by point basis, not spatial patterns of events. Downscaled GCM results (e.g., CMIP3 ensembles) have been widely used to predict hydrologic implications of climate variability and climate change in large snow-dominated river basins in the western United States (Diffenbaugh et al., 2008; Adam et al., 2009). However fewer applications to smaller rain-driven river basins in the southeastern US (where preserving spatial variability of rainfall patterns may be more important) have been reported. In this study a new method was developed to bias-correct GCMs to preserve both the long term temporal mean and variance of the precipitation data, and the spatial structure of daily precipitation fields. Forty-year retrospective simulations (1960-1999) from 16 GCMs were collected (IPCC, 2007; WCRP CMIP3 multi-model database: https://esg.llnl.gov:8443/), and the daily precipitation data at coarse resolution (i.e., 280km) were interpolated to 12km spatial resolution and bias corrected using gridded observations over the state of Florida (Maurer et al., 2002; Wood et al, 2002; Wood et al, 2004). In this method spatial random fields which preserved the observed spatial correlation structure of the historic gridded observations and the spatial mean corresponding to the coarse scale GCM daily rainfall were generated. The spatiotemporal variability of the spatio-temporally bias-corrected GCMs were evaluated against gridded observations, and compared to the original temporally bias-corrected and downscaled CMIP3 data for the central Florida. The hydrologic response of two southwest Florida watersheds to the gridded observation data, the original bias corrected CMIP3 data, and the new spatiotemporally corrected CMIP3 predictions was compared using an integrated surface-subsurface hydrologic model developed by Tampa Bay Water.

Regime-Based Evaluation of Cloudiness in CMIP5 Models

NASA Technical Reports Server (NTRS)

Jin, Daeho; Oraiopoulos, Lazaros; Lee, Dong Min

2016-01-01

The concept of Cloud Regimes (CRs) is used to develop a framework for evaluating the cloudiness of 12 fifth Coupled Model Intercomparison Project (CMIP5) models. Reference CRs come from existing global International Satellite Cloud Climatology Project (ISCCP) weather states. The evaluation is made possible by the implementation in several CMIP5 models of the ISCCP simulator generating for each gridcell daily joint histograms of cloud optical thickness and cloud top pressure. Model performance is assessed with several metrics such as CR global cloud fraction (CF), CR relative frequency of occurrence (RFO), their product (long-term average total cloud amount [TCA]), cross-correlations of CR RFO maps, and a metric of resemblance between model and ISCCP CRs. In terms of CR global RFO, arguably the most fundamental metric, the models perform unsatisfactorily overall, except for CRs representing thick storm clouds. Because model CR CF is internally constrained by our method, RFO discrepancies yield also substantial TCA errors. Our findings support previous studies showing that CMIP5 models underestimate cloudiness. The multi-model mean performs well in matching observed RFO maps for many CRs, but is not the best for this or other metrics. When overall performance across all CRs is assessed, some models, despite their shortcomings, apparently outperform Moderate Resolution Imaging Spectroradiometer (MODIS) cloud observations evaluated against ISCCP as if they were another model output. Lastly, cloud simulation performance is contrasted with each model's equilibrium climate sensitivity (ECS) in order to gain insight on whether good cloud simulation pairs with particular values of this parameter.

Are we using the right fuel to drive hydrological models? A climate impact study in the Upper Blue Nile

NASA Astrophysics Data System (ADS)

Liersch, Stefan; Tecklenburg, Julia; Rust, Henning; Dobler, Andreas; Fischer, Madlen; Kruschke, Tim; Koch, Hagen; Fokko Hattermann, Fred

2018-04-01

Climate simulations are the fuel to drive hydrological models that are used to assess the impacts of climate change and variability on hydrological parameters, such as river discharges, soil moisture, and evapotranspiration. Unlike with cars, where we know which fuel the engine requires, we never know in advance what unexpected side effects might be caused by the fuel we feed our models with. Sometimes we increase the fuel's octane number (bias correction) to achieve better performance and find out that the model behaves differently but not always as was expected or desired. This study investigates the impacts of projected climate change on the hydrology of the Upper Blue Nile catchment using two model ensembles consisting of five global CMIP5 Earth system models and 10 regional climate models (CORDEX Africa). WATCH forcing data were used to calibrate an eco-hydrological model and to bias-correct both model ensembles using slightly differing approaches. On the one hand it was found that the bias correction methods considerably improved the performance of average rainfall characteristics in the reference period (1970-1999) in most of the cases. This also holds true for non-extreme discharge conditions between Q20 and Q80. On the other hand, bias-corrected simulations tend to overemphasize magnitudes of projected change signals and extremes. A general weakness of both uncorrected and bias-corrected simulations is the rather poor representation of high and low flows and their extremes, which were often deteriorated by bias correction. This inaccuracy is a crucial deficiency for regional impact studies dealing with water management issues and it is therefore important to analyse model performance and characteristics and the effect of bias correction, and eventually to exclude some climate models from the ensemble. However, the multi-model means of all ensembles project increasing average annual discharges in the Upper Blue Nile catchment and a shift in seasonal patterns, with decreasing discharges in June and July and increasing discharges from August to November.

On the spectral characteristics of the Atlantic multidecadal variability in an ensemble of multi-century simulations

NASA Astrophysics Data System (ADS)

Mavilia, Irene; Bellucci, Alessio; J. Athanasiadis, Panos; Gualdi, Silvio; Msadek, Rym; Ruprich-Robert, Yohan

2018-01-01

The Atlantic multidecadal variability (AMV) is a coherent pattern of variability of the North Atlantic sea surface temperature field affecting several components of the climate system in the Atlantic region and the surrounding areas. The relatively short observational record severely limits our understanding of the physical mechanisms leading to the AMV. The present study shows that the spatial and temporal characteristics of the AMV, as assessed from the historical records, should also be considered as highly uncertain. Using 11 multi-century preindustrial climate simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database, we show that the AMV characteristics are not constant along the simulation when assessed from different 200-year-long periods to match the observed period length. An objective method is proposed to test whether the variations of the AMV characteristics are consistent with stochastic internal variability. For 7 out of the 11 models analysed, the results indicate a non-stationary behaviour for the AMV time series. However, the possibility that the non-stationarity arises from sampling errors can be excluded with high confidence only for one of the 7 models. Therefore, longer time series are needed to robustly assess the AMV characteristics. In addition to any changes imposed to the AMV by external forcings, the detected dependence on the time interval identified in most models suggests that the character of the observed AMV may undergo significant changes in the future.

A protocol for the intercomparison of marine fishery and ecosystem models: Fish-MIP v1.0

NASA Astrophysics Data System (ADS)

Tittensor, Derek P.; Eddy, Tyler D.; Lotze, Heike K.; Galbraith, Eric D.; Cheung, William; Barange, Manuel; Blanchard, Julia L.; Bopp, Laurent; Bryndum-Buchholz, Andrea; Büchner, Matthias; Bulman, Catherine; Carozza, David A.; Christensen, Villy; Coll, Marta; Dunne, John P.; Fernandes, Jose A.; Fulton, Elizabeth A.; Hobday, Alistair J.; Huber, Veronika; Jennings, Simon; Jones, Miranda; Lehodey, Patrick; Link, Jason S.; Mackinson, Steve; Maury, Olivier; Niiranen, Susa; Oliveros-Ramos, Ricardo; Roy, Tilla; Schewe, Jacob; Shin, Yunne-Jai; Silva, Tiago; Stock, Charles A.; Steenbeek, Jeroen; Underwood, Philip J.; Volkholz, Jan; Watson, James R.; Walker, Nicola D.

2018-04-01

Model intercomparison studies in the climate and Earth sciences communities have been crucial to building credibility and coherence for future projections. They have quantified variability among models, spurred model development, contrasted within- and among-model uncertainty, assessed model fits to historical data, and provided ensemble projections of future change under specified scenarios. Given the speed and magnitude of anthropogenic change in the marine environment and the consequent effects on food security, biodiversity, marine industries, and society, the time is ripe for similar comparisons among models of fisheries and marine ecosystems. Here, we describe the Fisheries and Marine Ecosystem Model Intercomparison Project protocol version 1.0 (Fish-MIP v1.0), part of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which is a cross-sectoral network of climate impact modellers. Given the complexity of the marine ecosystem, this class of models has substantial heterogeneity of purpose, scope, theoretical underpinning, processes considered, parameterizations, resolution (grain size), and spatial extent. This heterogeneity reflects the lack of a unified understanding of the marine ecosystem and implies that the assemblage of all models is more likely to include a greater number of relevant processes than any single model. The current Fish-MIP protocol is designed to allow these heterogeneous models to be forced with common Earth System Model (ESM) Coupled Model Intercomparison Project Phase 5 (CMIP5) outputs under prescribed scenarios for historic (from the 1950s) and future (to 2100) time periods; it will be adapted to CMIP phase 6 (CMIP6) in future iterations. It also describes a standardized set of outputs for each participating Fish-MIP model to produce. This enables the broad characterization of differences between and uncertainties within models and projections when assessing climate and fisheries impacts on marine ecosystems and the services they provide. The systematic generation, collation, and comparison of results from Fish-MIP will inform an understanding of the range of plausible changes in marine ecosystems and improve our capacity to define and convey the strengths and weaknesses of model-based advice on future states of marine ecosystems and fisheries. Ultimately, Fish-MIP represents a step towards bringing together the marine ecosystem modelling community to produce consistent ensemble medium- and long-term projections of marine ecosystems.

Changes in Extremely Hot Summers over the Global Land Area under Various Warming Targets.

PubMed

Wang, Lei; Huang, Jianbin; Luo, Yong; Yao, Yao; Zhao, Zongci

2015-01-01

Summer temperature extremes over the global land area were investigated by comparing 26 models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) with observations from the Goddard Institute for Space Studies (GISS) and the Climate Research Unit (CRU). Monthly data of the observations and models were averaged for each season, and statistics were calculated for individual models before averaging them to obtain ensemble means. The summers with temperature anomalies (relative to 1951-1980) exceeding 3σ (σ is based on the local internal variability) are defined as "extremely hot". The models well reproduced the statistical characteristics evolution, and partly captured the spatial distributions of historical summer temperature extremes. If the global mean temperature increases 2°C relative to the pre-industrial level, "extremely hot" summers are projected to occur over nearly 40% of the land area (multi-model ensemble mean projection). Summers that exceed 5σ warming are projected to occur over approximately 10% of the global land area, which were rarely observed during the reference period. Scenarios reaching warming levels of 3°C to 5°C were also analyzed. After exceeding the 5°C warming target, "extremely hot" summers are projected to occur throughout the entire global land area, and summers that exceed 5σ warming would become common over 70% of the land area. In addition, the areas affected by "extremely hot" summers are expected to rapidly expand by more than 25%/°C as the global mean temperature increases by up to 3°C before slowing to less than 16%/°C as the temperature continues to increase by more than 3°C. The area that experiences summers with warming of 5σ or more above the warming target of 2°C is likely to maintain rapid expansion of greater than 17%/°C. To reduce the impacts and damage from severely hot summers, the global mean temperature increase should remain low.

Importance of ensembles in projecting regional climate trends

NASA Astrophysics Data System (ADS)

Arritt, Raymond; Daniel, Ariele; Groisman, Pavel

2016-04-01

We have performed an ensemble of simulations using RegCM4 to examine the ability to reproduce observed trends in precipitation intensity and to project future changes through the 21st century for the central United States. We created a matrix of simulations over the CORDEX North America domain for 1950-2099 by driving the regional model with two different global models (HadGEM2-ES and GFDL-ESM2M, both for RCP8.5), by performing simulations at both 50 km and 25 km grid spacing, and by using three different convective parameterizations. The result is a set of 12 simulations (two GCMs by two resolutions by three convective parameterizations) that can be used to systematically evaluate the influence of simulation design on predicted precipitation. The two global models were selected to bracket the range of climate sensitivity in the CMIP5 models: HadGEM2-ES has the highest ECS of the CMIP5 models, while GFDL-ESM2M has one of the lowestt. Our evaluation metrics differ from many other RCM studies in that we focus on the skill of the models in reproducing past trends rather than the mean climate state. Trends in frequency of extreme precipitation (defined as amounts exceeding 76.2 mm/day) for most simulations are similar to the observed trend but with notable variations depending on RegCM4 configuration and on the driving GCM. There are complex interactions among resolution, choice of convective parameterization, and the driving GCM that carry over into the future climate projections. We also note that biases in the current climate do not correspond to biases in trends. As an example of these points the Emanuel scheme is consistently "wet" (positive bias in precipitation) yet it produced the smallest precipitation increase of the three convective parameterizations when used in simulations driven by HadGEM2-ES. However, it produced the largest increase when driven by GFDL-ESM2M. These findings reiterate that ensembles using multiple RCM configurations and driving GCMs are essential for projecting regional climate change, even when a single RCM is used. This research was sponsored by the U.S. Department of Agriculture National Institute of Food and Agriculture.

An enhanced archive facilitating climate impacts analysis

USGS Publications Warehouse

Maurer, E.P.; Brekke, L.; Pruitt, T.; Thrasher, B.; Long, J.; Duffy, P.; Dettinger, M.; Cayan, D.; Arnold, J.

2014-01-01

We describe the expansion of a publicly available archive of downscaled climate and hydrology projections for the United States. Those studying or planning to adapt to future climate impacts demand downscaled climate model output for local or regional use. The archive we describe attempts to fulfill this need by providing data in several formats, selectable to meet user needs. Our archive has served as a resource for climate impacts modelers, water managers, educators, and others. Over 1,400 individuals have transferred more than 50 TB of data from the archive. In response to user demands, the archive has expanded from monthly downscaled data to include daily data to facilitate investigations of phenomena sensitive to daily to monthly temperature and precipitation, including extremes in these quantities. New developments include downscaled output from the new Coupled Model Intercomparison Project phase 5 (CMIP5) climate model simulations at both the monthly and daily time scales, as well as simulations of surface hydrologi- cal variables. The web interface allows the extraction of individual projections or ensemble statistics for user-defined regions, promoting the rapid assessment of model consensus and uncertainty for future projections of precipitation, temperature, and hydrology. The archive is accessible online (http://gdo-dcp.ucllnl.org/downscaled_ cmip_projections).

Projected sea level rise, gyre circulation and water mass formation in the western North Pacific: CMIP5 inter-model analysis

NASA Astrophysics Data System (ADS)

Terada, Mio; Minobe, Shoshiro

2017-09-01

Future changes in the dynamic sea level (DSL), which is defined as sea-level deviation from the global mean sea level, is investigated over the North Pacific, by analyzing data from the Coupled Model Intercomparison Project Phase 5. The analysis provides more comprehensive descriptions of DSL responses to the global warming in this region than available from previous studies, by using surface and subsurface data until the year 2300 under middle and high greenhouse-gas emission scenarios. The DSL changes in the North Pacific are characterized by a DSL rise in the western North Pacific around the Kuroshio Extension (KE), as also reported by previous studies. Subsurface density analysis indicates that DSL rise around the KE is associated with decrease in density of subtropical mode water (STMW) and with northward KE migration, the former (latter) of which is relatively strong between 2000 and 2100 for both RCP4.5 and RCP8.5 (between 2100 and 2300 for RCP8.5). The STMW density decrease is related to large heat uptake to the south and southeast of Japan, while the northward KE migration is associated with the poleward shift of the wind stress field. These features are commonly found in multi-model ensemble means and the relations among representative quantities produced by different climate models.

Projected sea level rise, gyre circulation and water mass formation in the western North Pacific: CMIP5 inter-model analysis

NASA Astrophysics Data System (ADS)

Terada, Mio; Minobe, Shoshiro

2018-06-01

Future changes in the dynamic sea level (DSL), which is defined as sea-level deviation from the global mean sea level, is investigated over the North Pacific, by analyzing data from the Coupled Model Intercomparison Project Phase 5. The analysis provides more comprehensive descriptions of DSL responses to the global warming in this region than available from previous studies, by using surface and subsurface data until the year 2300 under middle and high greenhouse-gas emission scenarios. The DSL changes in the North Pacific are characterized by a DSL rise in the western North Pacific around the Kuroshio Extension (KE), as also reported by previous studies. Subsurface density analysis indicates that DSL rise around the KE is associated with decrease in density of subtropical mode water (STMW) and with northward KE migration, the former (latter) of which is relatively strong between 2000 and 2100 for both RCP4.5 and RCP8.5 (between 2100 and 2300 for RCP8.5). The STMW density decrease is related to large heat uptake to the south and southeast of Japan, while the northward KE migration is associated with the poleward shift of the wind stress field. These features are commonly found in multi-model ensemble means and the relations among representative quantities produced by different climate models.

Evaluating Land-Atmosphere Moisture Feedbacks in Earth System Models With Spaceborne Observations

NASA Astrophysics Data System (ADS)

Levine, P. A.; Randerson, J. T.; Lawrence, D. M.; Swenson, S. C.

2016-12-01

We have developed a set of metrics for measuring the feedback loop between the land surface moisture state and the atmosphere globally on an interannual time scale. These metrics consider both the forcing of terrestrial water storage (TWS) on subsequent atmospheric conditions as well as the response of TWS to antecedent atmospheric conditions. We designed our metrics to take advantage of more than one decade's worth of satellite observations of TWS from the Gravity Recovery and Climate Experiment (GRACE) along with atmospheric variables from the Atmospheric Infrared Sounder (AIRS), the Global Precipitation Climatology Project (GPCP), and Clouds and the Earths Radiant Energy System (CERES). Metrics derived from spaceborne observations were used to evaluate the strength of the feedback loop in the Community Earth System Model (CESM) Large Ensemble (LENS) and in several models that contributed simulations to Phase 5 of the Coupled Model Intercomparison Project (CMIP5). We found that both forcing and response limbs of the feedback loop were generally stronger in tropical and temperate regions in CMIP5 models and even more so in LENS compared to satellite observations. Our analysis suggests that models may overestimate the strength of the feedbacks between the land surface and the atmosphere, which is consistent with previous studies conducted across different spatial and temporal scales.

Uncertainty in Indian Ocean Dipole response to global warming: the role of internal variability

NASA Astrophysics Data System (ADS)

Hui, Chang; Zheng, Xiao-Tong

2018-01-01

The Indian Ocean Dipole (IOD) is one of the leading modes of interannual sea surface temperature (SST) variability in the tropical Indian Ocean (TIO). The response of IOD to global warming is quite uncertain in climate model projections. In this study, the uncertainty in IOD change under global warming, especially that resulting from internal variability, is investigated based on the community earth system model large ensemble (CESM-LE). For the IOD amplitude change, the inter-member uncertainty in CESM-LE is about 50% of the intermodel uncertainty in the phase 5 of the coupled model intercomparison project (CMIP5) multimodel ensemble, indicating the important role of internal variability in IOD future projection. In CESM-LE, both the ensemble mean and spread in mean SST warming show a zonal positive IOD-like (pIOD-like) pattern in the TIO. This pIOD-like mean warming regulates ocean-atmospheric feedbacks of the interannual IOD mode, and weakens the skewness of the interannual variability. However, as the changes in oceanic and atmospheric feedbacks counteract each other, the inter-member variability in IOD amplitude change is not correlated with that of the mean state change. Instead, the ensemble spread in IOD amplitude change is correlated with that in ENSO amplitude change in CESM-LE, reflecting the close inter-basin relationship between the tropical Pacific and Indian Ocean in this model.

Inferring biogeochemistry past: a millennial-scale multimodel assimilation of multiple paleoecological proxies.

NASA Astrophysics Data System (ADS)

Dietze, M.; Raiho, A.; Fer, I.; Dawson, A.; Heilman, K.; Hooten, M.; McLachlan, J. S.; Moore, D. J.; Paciorek, C. J.; Pederson, N.; Rollinson, C.; Tipton, J.

2017-12-01

The pre-industrial period serves as an essential baseline against which we judge anthropogenic impacts on the earth's systems. However, direct measurements of key biogeochemical processes, such as carbon, water, and nutrient cycling, are absent for this period and there is no direct way to link paleoecological proxies, such as pollen and tree rings, to these processes. Process-based terrestrial ecosystem models provide a way to make inferences about the past, but have large uncertainties and by themselves often fail to capture much of the observed variability. Here we investigate the ability to improve inferences about pre-industrial biogeochemical cycles through the formal assimilation of proxy data into multiple process-based models. A Tobit ensemble filter with explicit estimation of process error was run at five sites across the eastern US for three models (LINKAGES, ED2, LPJ-GUESS). In addition to process error, the ensemble accounted for parameter uncertainty, estimated through the assimilation of the TRY and BETY trait databases, and driver uncertainty, accommodated by probabilistically downscaling and debiasing CMIP5 GCM output then filtering based on paleoclimate reconstructions. The assimilation was informed by four PalEON data products, each of which includes an explicit Bayesian error estimate: (1) STEPPS forest composition estimated from fossil pollen; (2) REFAB aboveground biomass (AGB) estimated from fossil pollen; (3) tree ring AGB and woody net primary productivity (wNPP); and (4) public land survey composition, stem density, and AGB. By comparing ensemble runs with and without data assimilation we are able to assess the information contribution of the proxy data to constraining biogeochemical fluxes, which is driven by the combination of model uncertainty, data uncertainty, and the strength of correlation between observed and unobserved quantities in the model ensemble. To our knowledge this is the first attempt at multi-model data assimilation with terrestrial ecosystem models. Results from the data-model assimilation allow us to assess the consistency across models in post-assimilation inferences about indirectly inferred quantities, such as GPP, soil carbon, and the water budget.

Atmospheric river influence on the intensification of extreme hydrologic events over the Western United States under climate change scenarios

NASA Astrophysics Data System (ADS)

Pagán, Brianna; Ashfaq, Moetasim; Nayak, Munir; Rastogi, Deeksha; Margulis, Steven; Pal, Jeremy

2017-04-01

The Western United States shares limited snowmelt driven water supplies amongst millions of people, a multi-billion dollar agriculture industry and fragile ecosystems. The climatology of the region is highly variable, characterized by the frequent occurrences of both flood and drought conditions that cause increasingly challenging water management issues. Although variable year to year, up to half of California's total precipitation can be linked to atmospheric rivers (ARs). Most notably, ARs have been connected to nearly every major historic flood in the region, establishing its critical role to water supply. Numerous prior studies have considered potential climate change impacts over the Western United States and have generally concluded that warmer temperatures will reduce snowpack and shift runoff timing, causing reductions to water supply. Here we examine the role of ARs as one mechanism for explaining projected increases in flood and drought frequency and intensity under climate change scenarios, vital information for water resource managers. A hierarchical modeling framework to downscale 11 coupled global climate models from CMIP5 is used to form an ensemble of high-resolution dynamically downscaled regional climate model (via RegCM4) simulations at 18-km and hydrological (via VIC) simulations at a 4-km resolution for baseline (1965-2005) and future (2010-2050) periods under RCP 8.5. Each ensemble member's ability to capture observational AR climatology over the baseline period is evaluated. Baseline to future period changes to AR size, duration, seasonal timing, trajectory, magnitude and frequency are presented. These changes to the characterizations of ARs in the region are used to determine if any links exist to changes in snowpack volume, runoff timing, and the occurrence of daily and annual cumulative extreme precipitation and runoff events. Shifts in extreme AR frequency and magnitude are expected to increase flood risks, which without adequate multi-year reservoir storage solutions could further strain water supply resources.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meehl, G A; Covey, C; McAvaney, B

The Coupled Model Intercomparison Project (CMIP) is designed to allow study and intercomparison of multi-model simulations of present-day and future climate. The latter are represented by idealized forcing of compounded 1% per year CO2 increase to the time of CO2 doubling near year 70 in simulations with global coupled models that contain, typically, components representing atmosphere, ocean, sea ice and land surface. Results from CMIP diagnostic subprojects were presented at the Second CMIP Workshop held at the Max Planck Institute for Meteorology in Hamburg, Germany, in September, 2003. Significant progress in diagnosing and understanding results from global coupled models hasmore » been made since the First CMIP Workshop in Melbourne, Australia in 1998. For example, the issue of flux adjustment is slowly fading as more and more models obtain stable multi-century surface climates without them. El Nino variability, usually about half the observed amplitude in the previous generation of coupled models, is now more accurately simulated in the present generation of global coupled models, though there are still biases in simulating the patterns of maximum variability. Typical resolutions of atmospheric component models contained in coupled models is now usually around 2.5 degrees latitude-longitude, with the ocean components often having about twice the atmospheric model resolution, with even higher resolution in the equatorial tropics. Some new-generation coupled models have atmospheric model resolutions of around 1.5 degrees latitude-longitude. Modeling groups now routinely run the CMIP control and 1% CO2 simulations in addition to 20th and 21st century climate simulations with a variety of forcings (e.g. volcanoes, solar variability, anthropogenic sulfate aerosols, ozone, and greenhouse gases (GHGs), with the anthropogenic forcings for future climate as well). However, persistent systematic errors noted in previous generations of global coupled models still are present in the present generation (e.g. over-extensive equatorial Pacific cold tongue, double ITCZ). This points to the next challenge for the global coupled climate modeling community. Planning and imminent commencement of the IPCC Fourth Assessment Report (AR4) has prompted rapid coupled model development, which will lead to an expanded CMIP-like activity to collect and analyze results for the control, 1% CO2, 20th, 21st and 22nd century simulations performed for the AR4. The international climate community is encouraged to become involved in this analysis effort, and details are provided below in how to do so.« less

Assessment of Surface Air Temperature over China Using Multi-criterion Model Ensemble Framework

NASA Astrophysics Data System (ADS)

Li, J.; Zhu, Q.; Su, L.; He, X.; Zhang, X.

2017-12-01

The General Circulation Models (GCMs) are designed to simulate the present climate and project future trends. It has been noticed that the performances of GCMs are not always in agreement with each other over different regions. Model ensemble techniques have been developed to post-process the GCMs' outputs and improve their prediction reliabilities. To evaluate the performances of GCMs, root-mean-square error, correlation coefficient, and uncertainty are commonly used statistical measures. However, the simultaneous achievements of these satisfactory statistics cannot be guaranteed when using many model ensemble techniques. Meanwhile, uncertainties and future scenarios are critical for Water-Energy management and operation. In this study, a new multi-model ensemble framework was proposed. It uses a state-of-art evolutionary multi-objective optimization algorithm, termed Multi-Objective Complex Evolution Global Optimization with Principle Component Analysis and Crowding Distance (MOSPD), to derive optimal GCM ensembles and demonstrate the trade-offs among various solutions. Such trade-off information was further analyzed with a robust Pareto front with respect to different statistical measures. A case study was conducted to optimize the surface air temperature (SAT) ensemble solutions over seven geographical regions of China for the historical period (1900-2005) and future projection (2006-2100). The results showed that the ensemble solutions derived with MOSPD algorithm are superior over the simple model average and any single model output during the historical simulation period. For the future prediction, the proposed ensemble framework identified that the largest SAT change would occur in the South Central China under RCP 2.6 scenario, North Eastern China under RCP 4.5 scenario, and North Western China under RCP 8.5 scenario, while the smallest SAT change would occur in the Inner Mongolia under RCP 2.6 scenario, South Central China under RCP 4.5 scenario, and South Central China under RCP 8.5 scenario.

Performance of CMIP3 and CMIP5 GCMs to simulate observed rainfall characteristics over the Western Himalayan region

NASA Astrophysics Data System (ADS)

Meher, J. K.; Das, L.

2017-12-01

The Western Himalayan Region (WHR) was subject to a significant negative trend in the annual and monsoon rainfall during 1902-2005. Annual and seasonal rainfall change over WHR of India was estimated using 22 rain gauge station rainfall data from the India Meteorological Department. The performance of 13 global climate models (GCMs) from the coupled model intercomparison project phase 3 (CMIP3) and 42 GCMs from CMIP5 was evaluated through multiple analysis: the evaluation of the mean annual cycle, annual cycles of interannual variability, spatial patterns, trends and signal-to-noise ratio. In general, CMIP5 GCMs were more skillful in terms of simulating the annual cycle of interannual variability compared to CMIP3 GCMs. The CMIP3 GCMs failed to reproduce the observed trend whereas 50% of the CMIP5 GCMs reproduced the statistical distribution of short-term (30-years) trend-estimates than for the longer term (99-years). GCMs from both CMIP3 and CMIP5 were able to simulate the spatial distribution of observed rainfall in pre-monsoon and winter months. Based on performance, each model of CMIP3 and CMIP5 was given an overall rank, which puts the high resolution version of the MIROC3.2 model (MIROC3.2 hires) and MIROC5 at the top in CMIP3 and CMIP5 respectively. Robustness of the ranking was judged through a sensitivity analysis, which indicated that ranks were independent during the process of adding or removing any individual method. It also revealed that trend analysis was not a robust method of judging performances of the model as compared to other methods.

How well do simulated last glacial maximum tropical temperatures constrain equilibrium climate sensitivity?

NASA Astrophysics Data System (ADS)

Hopcroft, Peter O.; Valdes, Paul J.

2015-07-01

Previous work demonstrated a significant correlation between tropical surface air temperature and equilibrium climate sensitivity (ECS) in PMIP (Paleoclimate Modelling Intercomparison Project) phase 2 model simulations of the last glacial maximum (LGM). This implies that reconstructed LGM cooling in this region could provide information about the climate system ECS value. We analyze results from new simulations of the LGM performed as part of Coupled Model Intercomparison Project (CMIP5) and PMIP phase 3. These results show no consistent relationship between the LGM tropical cooling and ECS. A radiative forcing and feedback analysis shows that a number of factors are responsible for this decoupling, some of which are related to vegetation and aerosol feedbacks. While several of the processes identified are LGM specific and do not impact on elevated CO2 simulations, this analysis demonstrates one area where the newer CMIP5 models behave in a qualitatively different manner compared with the older ensemble. The results imply that so-called Earth System components such as vegetation and aerosols can have a significant impact on the climate response in LGM simulations, and this should be taken into account in future analyses.

Reflecting on the challenges of building a rich interconnected metadata database to describe the experiments of phase six of the coupled climate model intercomparison project (CMIP6) for the Earth System Documentation Project (ES-DOC) and anticipating the opportunities that tooling and services based on rich metadata can provide.

NASA Astrophysics Data System (ADS)

Pascoe, C. L.

2017-12-01

The Coupled Model Intercomparison Project (CMIP) has coordinated climate model experiments involving multiple international modelling teams since 1995. This has led to a better understanding of past, present, and future climate. The 2017 sixth phase of the CMIP process (CMIP6) consists of a suite of common experiments, and 21 separate CMIP-Endorsed Model Intercomparison Projects (MIPs) making a total of 244 separate experiments. Precise descriptions of the suite of CMIP6 experiments have been captured in a Common Information Model (CIM) database by the Earth System Documentation Project (ES-DOC). The database contains descriptions of forcings, model configuration requirements, ensemble information and citation links, as well as text descriptions and information about the rationale for each experiment. The database was built from statements about the experiments found in the academic literature, the MIP submissions to the World Climate Research Programme (WCRP), WCRP summary tables and correspondence with the principle investigators for each MIP. The database was collated using spreadsheets which are archived in the ES-DOC Github repository and then rendered on the ES-DOC website. A diagramatic view of the workflow of building the database of experiment metadata for CMIP6 is shown in the attached figure.The CIM provides the formalism to collect detailed information from diverse sources in a standard way across all the CMIP6 MIPs. The ES-DOC documentation acts as a unified reference for CMIP6 information to be used both by data producers and consumers. This is especially important given the federated nature of the CMIP6 project. Because the CIM allows forcing constraints and other experiment attributes to be referred to by more than one experiment, we can streamline the process of collecting information from modelling groups about how they set up their models for each experiment. End users of the climate model archive will be able to ask questions enabled by the interconnectedness of the metadata such as "Which MIPs make use of experiment A?" and "Which experiments use forcing constraint B?".

A Spatial Perspective of Droughts and Pluvials in the Tropics and their Relationships to ENSO in CMIP5 Model Simulations

NASA Astrophysics Data System (ADS)

Perez Arango, J. D.; Lintner, B. R.; Lyon, B.

2016-12-01

Although many aspects of the tropical response to ENSO are well-known, the spatial characteristics of the rainfall response to ENSO remain relatively unexplored. Moreover, in current generation climate models, the spatial signatures of the ENSO tropical teleconnection are more uncertain than other aspects of ENSO variability, such as the amplitude of rainfall anomalies. Following the approach of Lyon (2004) and Lyon and Barnston (2005), we analyze here integrated measures of the spatial extent of drought and pluvial conditions in the tropics and their relationship to ENSO in observations as well as simulations of Phase 5 of the Coupled Model Intercomparison Project (CMIP5) with prescribed SST forcing. We compute diagnostics including the model ensemble-means and standard deviations of moderate, intermediate, and severe droughts and pluvials and the lagged correlations with respect to ENSO-based SST indices like NINO3. Overall, in a tropics-wide sense, the models generally capture the areal extent of observed droughts and pluvials and their phasing with respect to ENSO. However, at more local scales, e.g., tropical South America, the simulated metrics agree less strongly with observations, underscoring the role of errors in the spatial patterns of ENSO-induced rainfall anomalies.

Best convective parameterization scheme within RegCM4 to downscale CMIP5 multi-model data for the CORDEX-MENA/Arab domain

NASA Astrophysics Data System (ADS)

Almazroui, Mansour; Islam, Md. Nazrul; Al-Khalaf, A. K.; Saeed, Fahad

2016-05-01

A suitable convective parameterization scheme within Regional Climate Model version 4.3.4 (RegCM4) developed by the Abdus Salam International Centre for Theoretical Physics, Trieste, Italy, is investigated through 12 sensitivity runs for the period 2000-2010. RegCM4 is driven with European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim 6-hourly boundary condition fields for the CORDEX-MENA/Arab domain. Besides ERA-Interim lateral boundary conditions data, the Climatic Research Unit (CRU) data is also used to assess the performance of RegCM4. Different statistical measures are taken into consideration in assessing model performance for 11 sub-domains throughout the analysis domain, out of which 7 (4) sub-domains give drier (wetter) conditions for the area of interest. There is no common best option for the simulation of both rainfall and temperature (with lowest bias); however, one option each for temperature and rainfall has been found to be superior among the 12 options investigated in this study. These best options for the two variables vary from region to region as well. Overall, RegCM4 simulates large pressure and water vapor values along with lower wind speeds compared to the driving fields, which are the key sources of bias in simulating rainfall and temperature. Based on the climatic characteristics of most of the Arab countries located within the study domain, the drier sub-domains are given priority in the selection of a suitable convective scheme, albeit with a compromise for both rainfall and temperature simulations. The most suitable option Grell over Land and Emanuel over Ocean in wet (GLEO wet) delivers a rainfall wet bias of 2.96 % and a temperature cold bias of 0.26 °C, compared to CRU data. An ensemble derived from all 12 runs provides unsatisfactory results for rainfall (28.92 %) and temperature (-0.54 °C) bias in the drier region because some options highly overestimate rainfall (reaching up to 200 %) and underestimate temperature (reaching up to -1.16 °C). Overall, a suitable option (GLEO wet) is recommended for downscaling the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model database using RegCM4 for the CORDEX-MENA/Arab domain for its use in future climate change impact studies.

Characterizing and Addressing the Need for Statistical Adjustment of Global Climate Model Data

NASA Astrophysics Data System (ADS)

White, K. D.; Baker, B.; Mueller, C.; Villarini, G.; Foley, P.; Friedman, D.

2017-12-01

As part of its mission to research and measure the effects of the changing climate, the U. S. Army Corps of Engineers (USACE) regularly uses the World Climate Research Programme's Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model dataset. However, these data are generated at a global level and are not fine-tuned for specific watersheds. This often causes CMIP5 output to vary from locally observed patterns in the climate. Several downscaling methods have been developed to increase the resolution of the CMIP5 data and decrease systemic differences to support decision-makers as they evaluate results at the watershed scale. Evaluating preliminary comparisons of observed and projected flow frequency curves over the US revealed a simple framework for water resources decision makers to plan and design water resources management measures under changing conditions using standard tools. Using this framework as a basis, USACE has begun to explore to use of statistical adjustment to alter global climate model data to better match the locally observed patterns while preserving the general structure and behavior of the model data. When paired with careful measurement and hypothesis testing, statistical adjustment can be particularly effective at navigating the compromise between the locally observed patterns and the global climate model structures for decision makers.

Future climate change enhances rainfall seasonality in a regional model of western Maritime Continent

NASA Astrophysics Data System (ADS)

Kang, Suchul; Im, Eun-Soon; Eltahir, Elfatih A. B.

2018-03-01

In this study, future changes in rainfall due to global climate change are investigated over the western Maritime Continent based on dynamically downscaled climate projections using the MIT Regional Climate Model (MRCM) with 12 km horizontal resolution. A total of nine 30-year regional climate projections driven by multi-GCMs projections (CCSM4, MPI-ESM-MR and ACCESS1.0) under multi-scenarios of greenhouse gases emissions (Historical: 1976-2005, RCP4.5 and RCP8.5: 2071-2100) from phase 5 of the Coupled Model Inter-comparison Project (CMIP5) are analyzed. Focusing on dynamically downscaled rainfall fields, the associated systematic biases originating from GCM and MRCM are removed based on observations using Parametric Quantile Mapping method in order to enhance the reliability of future projections. The MRCM simulations with bias correction capture the spatial patterns of seasonal rainfall as well as the frequency distribution of daily rainfall. Based on projected rainfall changes under both RCP4.5 and RCP8.5 scenarios, the ensemble of MRCM simulations project a significant decrease in rainfall over the western Maritime Continent during the inter-monsoon periods while the change in rainfall is not relevant during wet season. The main mechanism behind the simulated decrease in rainfall is rooted in asymmetries of the projected changes in seasonal dynamics of the meridional circulation along different latitudes. The sinking motion, which is marginally positioned in the reference simulation, is enhanced and expanded under global climate change, particularly in RCP8.5 scenario during boreal fall season. The projected enhancement of rainfall seasonality over the western Maritime Continent suggests increased risk of water stress for natural ecosystems as well as man-made water resources reservoirs.

Forecasting European Wildfires Today and in the Future

NASA Astrophysics Data System (ADS)

Navarro Abellan, Maria; Porras Alegre, Ignasi; María Sole, Josep; Gálvez, Pedro; Bielski, Conrad; Nurmi, Pertti

2017-04-01

Society as a whole is increasingly exposed and vulnerable to natural disasters due to extreme weather events exacerbated by climate change. The increased frequency of wildfires is not only a result of a changing climate, but wildfires themselves also produce a significant amount of greenhouse gases that, in-turn, further contribute to global warming. I-REACT (Improving Resilience to Emergencies through Advanced Cyber Technologies) is an innovation project funded by the European Commission , which aims to use social media, smartphones and wearables to improve natural disaster management by integrating existing services, both local and European, into a platform that supports the entire emergency management cycle. In order to assess the impact of climate change on wildfire hazards, METEOSIM designed two different System Processes (SP) that will be integrated into the I-REACT service that can provide information on a variety of time scales. SP1 - Climate Change Impact The climate change impact on climate variables related to fires is calculated by building an ensemble based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CORDEX data. A validation and an Empirical-Statistical Downscaling (ESD) calibration are done to assess the changes in the past of the climatic variables related to wildfires (temperature, precipitation, wind, relative humidity and Fire Weather Index). Calculations in the trend and the frequency of extreme events of those variables are done for three time scales: near-term (2011-2040), mid-term (2041-2070) and long term (2071-2100). SP2 - Operational daily forecast of the Canadian Forest Fire Weather Index (FWI) Using ensemble data from the ECMWF and from the GLAMEPS (multi-model ensemble) models, both supplied by the Finnish Meteorological Institute (FMI), the Fire Weather Index (FWI) and its index components are produced for each ensemble member within a wide forecast time range, from a few hours up to 10 days resulting in a probabilistic output of the FWI for different regions in Europe. This work will improve the currently available information to various wildfire information users such as fire departments, the civil protection, local authorities, etc., where accurate and reliable information in extreme weather situations are vital for improving planning and risk management.

A Practical Philosophy of Complex Climate Modelling

NASA Technical Reports Server (NTRS)

Schmidt, Gavin A.; Sherwood, Steven

2014-01-01

We give an overview of the practice of developing and using complex climate models, as seen from experiences in a major climate modelling center and through participation in the Coupled Model Intercomparison Project (CMIP).We discuss the construction and calibration of models; their evaluation, especially through use of out-of-sample tests; and their exploitation in multi-model ensembles to identify biases and make predictions. We stress that adequacy or utility of climate models is best assessed via their skill against more naive predictions. The framework we use for making inferences about reality using simulations is naturally Bayesian (in an informal sense), and has many points of contact with more familiar examples of scientific epistemology. While the use of complex simulations in science is a development that changes much in how science is done in practice, we argue that the concepts being applied fit very much into traditional practices of the scientific method, albeit those more often associated with laboratory work.

Inevitable end-of-21st-century trends toward earlier surface runoff timing in California's Sierra Nevada Mountains

NASA Astrophysics Data System (ADS)

Schwartz, M. A.; Hall, A. D.; Sun, F.; Walton, D.; Berg, N.

2015-12-01

Hybrid dynamical-statistical downscaling is used to produce surface runoff timing projections for California's Sierra Nevada, a high-elevation mountain range with significant seasonal snow cover. First, future climate change projections (RCP8.5 forcing scenario, 2081-2100 period) from five CMIP5 global climate models (GCMs) are dynamically downscaled. These projections reveal that future warming leads to a shift toward earlier snowmelt and surface runoff timing throughout the Sierra Nevada region. Relationships between warming and surface runoff timing from the dynamical simulations are used to build a simple statistical model that mimics the dynamical model's projected surface runoff timing changes given GCM input or other statistically-downscaled input. This statistical model can be used to produce surface runoff timing projections for other GCMs, periods, and forcing scenarios to quantify ensemble-mean changes, uncertainty due to intermodel variability and consequences stemming from choice of forcing scenario. For all CMIP5 GCMs and forcing scenarios, significant trends toward earlier surface runoff timing occur at elevations below 2500m. Thus, we conclude that trends toward earlier surface runoff timing by the end-of-the-21st century are inevitable. The changes to surface runoff timing diagnosed in this study have implications for many dimensions of climate change, including impacts on surface hydrology, water resources, and ecosystems.

Statistical downscaling of CMIP5 outputs for projecting future maximum and minimum temperature over the Haihe River Bain, China

NASA Astrophysics Data System (ADS)

Yan, Tiezhu; Shen, Zhenyao; Heng, Lee; Dercon, Gerd

2016-04-01

Future climate change information is important to formulate adaptation and mitigation strategies for climate change. In this study, a statistical downscaling model (SDSM) was established using both NCEP reanalysis data and ground observations (daily maximum and minimum temperature) during the period 1971-2010, and then calibrated model was applied to generate the future maximum and minimum temperature projections using predictors from the two CMIP5 models (MPI-ESM-LR and CNRM-CM5) under two Representative Concentration Pathway (RCP2.6 and RCP8.5) during the period 2011-2100 for the Haihe River Basin, China. Compared to the baseline period, future change in annual and seasonal maximum and minimum temperature was computed after bias correction. The spatial distribution and trend change of annual maximum and minimum temperature were also analyzed using ensemble projections. The results shows that: (1)The downscaling model had a good applicability on reproducing daily and monthly mean maximum and minimum temperature over the whole basin. (2) Bias was observed when using historical predictors from CMIP5 models and the performance of CNRM-CM5 was a little worse than that of MPI-ESM-LR. (3) The change in annual mean maximum and minimum temperature under the two scenarios in 2020s, 2050s and 2070s will increase and magnitude of maximum temperature will be higher than minimum temperature. (4) The increase in temperature in the mountains and along the coastline is remarkably high than the other parts of the studies basin. (5) For annual maximum and minimum temperature, the significant upward trend will be obtained under RCP 8.5 scenario and the magnitude will be 0.37 and 0.39 ℃ per decade, respectively; the increase in magnitude under RCP 2.6 scenario will be upward in 2020s and then decrease in 2050s and 2070s, and the magnitude will be 0.01 and 0.01℃ per decade, respectively.

Climate Central World Weather Attribution (WWA) project: Real-time extreme weather event attribution analysis

NASA Astrophysics Data System (ADS)

Haustein, Karsten; Otto, Friederike; Uhe, Peter; Allen, Myles; Cullen, Heidi

2015-04-01

Extreme weather detection and attribution analysis has emerged as a core theme in climate science over the last decade or so. By using a combination of observational data and climate models it is possible to identify the role of climate change in certain types of extreme weather events such as sea level rise and its contribution to storm surges, extreme heat events and droughts or heavy rainfall and flood events. These analyses are usually carried out after an extreme event has occurred when reanalysis and observational data become available. The Climate Central WWA project will exploit the increasing forecast skill of seasonal forecast prediction systems such as the UK MetOffice GloSea5 (Global seasonal forecasting system) ensemble forecasting method. This way, the current weather can be fed into climate models to simulate large ensembles of possible weather scenarios before an event has fully emerged yet. This effort runs along parallel and intersecting tracks of science and communications that involve research, message development and testing, staged socialization of attribution science with key audiences, and dissemination. The method we employ uses a very large ensemble of simulations of regional climate models to run two different analyses: one to represent the current climate as it was observed, and one to represent the same events in the world that might have been without human-induced climate change. For the weather "as observed" experiment, the atmospheric model uses observed sea surface temperature (SST) data from GloSea5 (currently) and present-day atmospheric gas concentrations to simulate weather events that are possible given the observed climate conditions. The weather in the "world that might have been" experiments is obtained by removing the anthropogenic forcing from the observed SSTs, thereby simulating a counterfactual world without human activity. The anthropogenic forcing is obtained by comparing the CMIP5 historical and natural simulations from a variety of CMIP5 model ensembles. Here, we present results for the UK 2013/14 winter floods as proof of concept and we show validation and testing results that demonstrate the robustness of our method. We also revisit the record temperatures over Europe in 2014 and present a detailed analysis of this attribution exercise as it is one of the events to demonstrate that we can make a sensible statement of how the odds for such a year to occur have changed while it still unfolds.

Natural climate variability and teleconnections to precipitation over the Pacific-North American region in CMIP3 and CMIP5 models

NASA Astrophysics Data System (ADS)

Polade, Suraj D.; Gershunov, Alexander; Cayan, Daniel R.; Dettinger, Michael D.; Pierce, David W.

2013-05-01

climate variability will continue to be an important aspect of future regional climate even in the midst of long-term secular changes. Consequently, the ability of climate models to simulate major natural modes of variability and their teleconnections provides important context for the interpretation and use of climate change projections. Comparisons reported here indicate that the CMIP5 generation of global climate models shows significant improvements in simulations of key Pacific climate mode and their teleconnections to North America compared to earlier CMIP3 simulations. The performance of 14 models with simulations in both the CMIP3 and CMIP5 archives are assessed using singular value decomposition analysis of simulated and observed winter Pacific sea surface temperatures (SSTs) and concurrent precipitation over the contiguous United States and northwestern Mexico. Most of the models reproduce basic features of the key natural mode and their teleconnections, albeit with notable regional deviations from observations in both SST and precipitation. Increasing horizontal resolution in the CMIP5 simulations is an important, but not a necessary, factor in the improvement from CMIP3 to CMIP5.

Natural climate variability and teleconnections to precipitation over the Pacific-North American region in CMIP3 and CMIP5 models

USGS Publications Warehouse

Polade, Suraj D.; Gershunov, Alexander; Cayan, Daniel R.; Dettinger, Michael D.; Pierce, David W.

2013-01-01

Natural climate variability will continue to be an important aspect of future regional climate even in the midst of long-term secular changes. Consequently, the ability of climate models to simulate major natural modes of variability and their teleconnections provides important context for the interpretation and use of climate change projections. Comparisons reported here indicate that the CMIP5 generation of global climate models shows significant improvements in simulations of key Pacific climate mode and their teleconnections to North America compared to earlier CMIP3 simulations. The performance of 14 models with simulations in both the CMIP3 and CMIP5 archives are assessed using singular value decomposition analysis of simulated and observed winter Pacific sea surface temperatures (SSTs) and concurrent precipitation over the contiguous United States and northwestern Mexico. Most of the models reproduce basic features of the key natural mode and their teleconnections, albeit with notable regional deviations from observations in both SST and precipitation. Increasing horizontal resolution in the CMIP5 simulations is an important, but not a necessary, factor in the improvement from CMIP3 to CMIP5.

Mid-Twenty-First-Century Changes in Global Wave Energy Flux: Single-Model, Single-Forcing and Single-Scenario Ensemble Projections

NASA Astrophysics Data System (ADS)

Semedo, Alvaro; Lemos, Gil; Dobrynin, Mikhail; Behrens, Arno; Staneva, Joanna; Miranda, Pedro

2017-04-01

The knowledge of ocean surface wave energy fluxes (or wave power) is of outmost relevance since wave power has a direct impact in coastal erosion, but also in sediment transport and beach nourishment, and ship, as well as in coastal and offshore infrastructures design. Changes in the global wave energy flux pattern can alter significantly the impact of waves in continental shelf and coastal areas. Up until recently the impact of climate change in future global wave climate had received very little attention. Some single model single scenario global wave climate projections, based on CMIP3 scenarios, were pursuit under the auspices of the COWCLIP (coordinated ocean wave climate projections) project, and received some attention in the IPCC (Intergovernmental Panel for Climate Change) AR5 (fifth assessment report). In the present study the impact of a warmer climate in the near future global wave energy flux climate is investigated through a 4-member "coherent" ensemble of wave climate projections: single-model, single-forcing, and single-scenario. In this methodology model variability is reduced, leaving only room for the climate change signal. The four ensemble members were produced with the wave model WAM, forced with wind speed and ice coverage from EC-Earth projections, following the representative concentration pathway with a high emissions scenario 8.5 (RCP8.5). The ensemble present climate reference period (the control run) has been set for 1976 to 2005. The projected changes in the global wave energy flux climate are analyzed for the 2031-2060 period.

A Wetter Future For California?

NASA Astrophysics Data System (ADS)

Luptowitz, R.; Allen, R.

2016-12-01

Future California (CA) precipitation projections, including those from the most recent Climate Model Intercomparison Project (CMIP5), remain uncertain. This uncertainty is related to several factors, including relatively large natural variability, model shortcomings, and because CA lies within a transition zone, where mid-latitude regions are expected to become wetter and subtropical regions drier. Here, we use the Community Earth System Model (CESM) Large Ensemble Project driven by the business-as-usual scenario, and find a robust increase in CA precipitation. This implies CMIP5 model differences are the dominant cause of the large range of future CA precipitation projections. The boreal winter season-when most of the CA precipitation increase occurs-is associated with changes in the mean circulation reminiscent of an El Niño teleconnection, including a southeastward shift of the upper level winds and an increase in storm track activity in the east Pacific, and an increase in CA moisture convergence. We further show that warming of tropical eastern Pacific sea surface temperatures-a robust feature in all models-accounts for these changes. Models that better simulate El Niño-CA precipitation teleconnections, including CESM, tend to yield larger, and more consistent increases in CA precipitation. Our results show that California will become wetter in a warmer world.

The CMIP5 Model Documentation Questionnaire: Development of a Metadata Retrieval System for the METAFOR Common Information Model

NASA Astrophysics Data System (ADS)

Pascoe, Charlotte; Lawrence, Bryan; Moine, Marie-Pierre; Ford, Rupert; Devine, Gerry

2010-05-01

The EU METAFOR Project (http://metaforclimate.eu) has created a web-based model documentation questionnaire to collect metadata from the modelling groups that are running simulations in support of the Coupled Model Intercomparison Project - 5 (CMIP5). The CMIP5 model documentation questionnaire will retrieve information about the details of the models used, how the simulations were carried out, how the simulations conformed to the CMIP5 experiment requirements and details of the hardware used to perform the simulations. The metadata collected by the CMIP5 questionnaire will allow CMIP5 data to be compared in a scientifically meaningful way. This paper describes the life-cycle of the CMIP5 questionnaire development which starts with relatively unstructured input from domain specialists and ends with formal XML documents that comply with the METAFOR Common Information Model (CIM). Each development step is associated with a specific tool. (1) Mind maps are used to capture information requirements from domain experts and build a controlled vocabulary, (2) a python parser processes the XML files generated by the mind maps, (3) Django (python) is used to generate the dynamic structure and content of the web based questionnaire from processed xml and the METAFOR CIM, (4) Python parsers ensure that information entered into the CMIP5 questionnaire is output as CIM compliant xml, (5) CIM compliant output allows automatic information capture tools to harvest questionnaire content into databases such as the Earth System Grid (ESG) metadata catalogue. This paper will focus on how Django (python) and XML input files are used to generate the structure and content of the CMIP5 questionnaire. It will also address how the choice of development tools listed above provided a framework that enabled working scientists (who we would never ordinarily get to interact with UML and XML) to be part the iterative development process and ensure that the CMIP5 model documentation questionnaire reflects what scientists want to know about the models. Keywords: metadata, CMIP5, automatic information capture, tool development

Improve projections of changes in southern African summer rainfall through comprehensive multi-timescale empirical statistical downscaling

NASA Astrophysics Data System (ADS)

Dieppois, B.; Pohl, B.; Eden, J.; Crétat, J.; Rouault, M.; Keenlyside, N.; New, M. G.

2017-12-01

The water management community has hitherto neglected or underestimated many of the uncertainties in climate impact scenarios, in particular, uncertainties associated with decadal climate variability. Uncertainty in the state-of-the-art global climate models (GCMs) is time-scale-dependant, e.g. stronger at decadal than at interannual timescales, in response to the different parameterizations and to internal climate variability. In addition, non-stationarity in statistical downscaling is widely recognized as a key problem, in which time-scale dependency of predictors plays an important role. As with global climate modelling, therefore, the selection of downscaling methods must proceed with caution to avoid unintended consequences of over-correcting the noise in GCMs (e.g. interpreting internal climate variability as a model bias). GCM outputs from the Coupled Model Intercomparison Project 5 (CMIP5) have therefore first been selected based on their ability to reproduce southern African summer rainfall variability and their teleconnections with Pacific sea-surface temperature across the dominant timescales. In observations, southern African summer rainfall has recently been shown to exhibit significant periodicities at the interannual timescale (2-8 years), quasi-decadal (8-13 years) and inter-decadal (15-28 years) timescales, which can be interpret as the signature of ENSO, the IPO, and the PDO over the region. Most of CMIP5 GCMs underestimate southern African summer rainfall variability and their teleconnections with Pacific SSTs at these three timescales. In addition, according to a more in-depth analysis of historical and pi-control runs, this bias is might result from internal climate variability in some of the CMIP5 GCMs, suggesting potential for bias-corrected prediction based empirical statistical downscaling. A multi-timescale regression based downscaling procedure, which determines the predictors across the different timescales, has thus been used to simulate southern African summer rainfall. This multi-timescale procedure shows much better skills in simulating decadal timescales of variability compared to commonly used statistical downscaling approaches.

Ensemble and Bias-Correction Techniques for Air-Quality Model Forecasts of Surface O3 and PM2.5 during the TEXAQS-II Experiment of 2006

EPA Science Inventory

Several air quality forecasting ensembles were created from seven models, running in real-time during the 2006 Texas Air Quality (TEXAQS-II) experiment. These multi-model ensembles incorporated a diverse set of meteorological models, chemical mechanisms, and emission inventories...

Multi-Model Ensemble Wake Vortex Prediction

NASA Technical Reports Server (NTRS)

Koerner, Stephan; Holzaepfel, Frank; Ahmad, Nash'at N.

2015-01-01

Several multi-model ensemble methods are investigated for predicting wake vortex transport and decay. This study is a joint effort between National Aeronautics and Space Administration and Deutsches Zentrum fuer Luft- und Raumfahrt to develop a multi-model ensemble capability using their wake models. An overview of different multi-model ensemble methods and their feasibility for wake applications is presented. The methods include Reliability Ensemble Averaging, Bayesian Model Averaging, and Monte Carlo Simulations. The methodologies are evaluated using data from wake vortex field experiments.

Skills of General Circulation and Earth System Models in reproducing streamflow to the ocean: the case of Congo river

NASA Astrophysics Data System (ADS)

Santini, M.; Caporaso, L.

2017-12-01

Although the importance of water resources in the context of climate change, it is still difficult to correctly simulate the freshwater cycle over the land via General Circulation and Earth System Models (GCMs and ESMs). Existing efforts from the Climate Model Intercomparison Project 5 (CMIP5) were mainly devoted to the validation of atmospheric variables like temperature and precipitation, with low attention to discharge.Here we investigate the present-day performances of GCMs and ESMs participating to CMIP5 in simulating the discharge of the river Congo to the sea thanks to: i) the long-term availability of discharge data for the Kinshasa hydrological station representative of more than 95% of the water flowing in the whole catchment; and ii) the River's still low influence by human intervention, which enables comparison with the (mostly) natural streamflow simulated within CMIP5.Our findings suggest how most of models appear overestimating the streamflow in terms of seasonal cycle, especially in the late winter and spring, while overestimation and variability across models are lower in late summer. Weighted ensemble means are also calculated, based on simulations' performances given by several metrics, showing some improvements of results.Although simulated inter-monthly and inter-annual percent anomalies do not appear significantly different from those in observed data, when translated into well consolidated indicators of drought attributes (frequency, magnitude, timing, duration), usually adopted for more immediate communication to stakeholders and decision makers, such anomalies can be misleading.These inconsistencies produce incorrect assessments towards water management planning and infrastructures (e.g. dams or irrigated areas), especially if models are used instead of measurements, as in case of ungauged basins or for basins with insufficient data, as well as when relying on models for future estimates without a preliminary quantification of model biases.

Hydrological extremes and their agricultural impacts under a changing climate in Texas

NASA Astrophysics Data System (ADS)

Lee, K.; Gao, H.; Huang, M.; Sheffield, J.

2015-12-01

With the changing climate, hydrologic extremes (such as floods, droughts, and heat waves) are becoming more frequent and intensified. Such changes in extreme events are expected to affect agricultural production and food supplies. This study focuses on the State of Texas, which has the largest farm area and the highest value of livestock production in the U.S. The objectives are two-fold: First, to investigate the climatic impact on the occurrence of future hydrologic extreme events; and second, to evaluate the effects of the future extremes on agricultural production. The Variable Infiltration Capacity (VIC) model, which is calibrated and validated over Texas river basins during the historical period, is employed for this study. The VIC model is forced by the statistically downscaled climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5) model ensembles at a spatial resolution of 1/8°. The CMIP5 projections contain four different scenarios in terms of Representative Concentration Pathway (RCP) (i.e. 2.6, 4.5, 6.0 and 8.5 w/m2). To carry out the analysis, VIC outputs forced by the CMIP5 model scenarios over three 30-year periods (1970-1999, 2020-2049 and 2070-2099) are first evaluated to identify how the frequency and the extent of the extreme events will be altered in the ten Texas major river basins. The results suggest that a significant increase in the number of extreme events will occur starting in the first half of the 21st century in Texas. Then, the effects of the predicted hydrologic extreme events on the irrigation water demand are investigated. It is found that future changes in water demand vary by crop type and location, with an east-to-west gradient. The results are expected to contribute to future water management and planning in Texas.

Generating extreme weather event sets from very large ensembles of regional climate models

NASA Astrophysics Data System (ADS)

Massey, Neil; Guillod, Benoit; Otto, Friederike; Allen, Myles; Jones, Richard; Hall, Jim

2015-04-01

Generating extreme weather event sets from very large ensembles of regional climate models Neil Massey, Benoit P. Guillod, Friederike E. L. Otto, Myles R. Allen, Richard Jones, Jim W. Hall Environmental Change Institute, University of Oxford, Oxford, UK Extreme events can have large impacts on societies and are therefore being increasingly studied. In particular, climate change is expected to impact the frequency and intensity of these events. However, a major limitation when investigating extreme weather events is that, by definition, only few events are present in observations. A way to overcome this issue it to use large ensembles of model simulations. Using the volunteer distributed computing (VDC) infrastructure of weather@home [1], we run a very large number (10'000s) of RCM simulations over the European domain at a resolution of 25km, with an improved land-surface scheme, nested within a free-running GCM. Using VDC allows many thousands of climate model runs to be computed. Using observations for the GCM boundary forcings we can run historical "hindcast" simulations over the past 100 to 150 years. This allows us, due to the chaotic variability of the atmosphere, to ascertain how likely an extreme event was, given the boundary forcings, and to derive synthetic event sets. The events in these sets did not actually occur in the observed record but could have occurred given the boundary forcings, with an associated probability. The event sets contain time-series of fields of meteorological variables that allow impact modellers to assess the loss the event would incur. Projections of events into the future are achieved by modelling projections of the sea-surface temperature (SST) and sea-ice boundary forcings, by combining the variability of the SST in the observed record with a range of warming signals derived from the varying responses of SSTs in the CMIP5 ensemble to elevated greenhouse gas (GHG) emissions in three RCP scenarios. Simulating the future with a range of SST responses, as well as a range of RCP scenarios, allows us to assess the uncertainty in the response to elevated GHG emissions that occurs in the CMIP5 ensemble. Numerous extreme weather events can be studied. Firstly, we analyse droughts in Europe with a focus on the UK in the context of the project MaRIUS (Managing the Risks, Impacts and Uncertainties of droughts and water Scarcity). We analyse the characteristics of the simulated droughts, the underlying physical mechanisms, and assess droughts observed in the recent past. Secondly, we analyse windstorms by applying an objective storm-identification and tracking algorithm to the ensemble output, isolating those storms that cause high loss and building a probabilistic storm catalogue, which can be used by impact modellers, insurance loss modellers, etc. Finally, we combine the model output with a heat-stress index to determine the detrimental effect on health of heat waves in Europe. [1] Massey, N. et al., 2014, Q. J. R. Meteorol. Soc.

Predicted Responses of Vegetation to Climate Change: A Global Analysis of Changes in Primary Productivity and Water Use Efficiency in the 21st Century

NASA Astrophysics Data System (ADS)

Bernardes, S.

2016-12-01

Global coupled carbon-climate simulations show considerable variability in outputs for atmospheric and land fields over the 21st century. This variability includes changes in temperature and in the quantity and spatiotemporal distribution of precipitation for large regions on the planet. Studies have considered that reductions in water availability due to decreased precipitation and increased water demand by the atmosphere may negatively affect plant metabolism and reduce carbon uptake. Future increases in carbon dioxide concentrations are expected to affect those interactions and potentially offset reductions in productivity. It is uncertain how plants will adjust their water use efficiency (WUE, plant production per water loss by evapotranspiration) in response to changing environmental conditions. This work investigates predicted changes in WUE in the 21st century by analyzing an ensemble of Earth System Models from the Coupled Model Intercomparison Project 5 (CMIP5), together with flux tower data and products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Two representative concentration pathways were selected to describe possible climate futures (RCP4.5 and RCP8.5). Periods of analysis included 2006-2099 (predicted) and 1850-2005 (reference). Comparisons between modeled, flux and satellite data for IPCC SREX regions were used to address the significant intermodel variability observed for the CMIP5 ensemble (larger variability for RCP8.5, higher intermodel agreement in Southeast Asia, lower intermodel agreement in arid areas). Model skill was evaluated in support of model selection and the spatiotemporal analysis of changes in WUE. Global, regional and latitudinal distributions of departures of projected conditions in relation to historical values are presented for both concentration pathways. Results showed high model sensitivity to different concentration pathways and increase in GPP and WUE for most of the planet (increases consistently higher for RCP8.5). Higher increases in GPP and WUE are predicted to occur over higher latitudes in the northern hemisphere (boreal region), with WUE usually following GPP in changes. Decreases in productivity and WUE occur mostly in the tropics, affecting tropical forests in Central America and in the Amazon.

Impacts of weighting climate models for hydro-meteorological climate change studies

NASA Astrophysics Data System (ADS)

Chen, Jie; Brissette, François P.; Lucas-Picher, Philippe; Caya, Daniel

2017-06-01

Weighting climate models is controversial in climate change impact studies using an ensemble of climate simulations from different climate models. In climate science, there is a general consensus that all climate models should be considered as having equal performance or in other words that all projections are equiprobable. On the other hand, in the impacts and adaptation community, many believe that climate models should be weighted based on their ability to better represent various metrics over a reference period. The debate appears to be partly philosophical in nature as few studies have investigated the impact of using weights in projecting future climate changes. The present study focuses on the impact of assigning weights to climate models for hydrological climate change studies. Five methods are used to determine weights on an ensemble of 28 global climate models (GCMs) adapted from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database. Using a hydrological model, streamflows are computed over a reference (1961-1990) and future (2061-2090) periods, with and without post-processing climate model outputs. The impacts of using different weighting schemes for GCM simulations are then analyzed in terms of ensemble mean and uncertainty. The results show that weighting GCMs has a limited impact on both projected future climate in term of precipitation and temperature changes and hydrology in terms of nine different streamflow criteria. These results apply to both raw and post-processed GCM model outputs, thus supporting the view that climate models should be considered equiprobable.

Fast and slow responses of Southern Ocean sea surface temperature to SAM in coupled climate models

NASA Astrophysics Data System (ADS)

Kostov, Yavor; Marshall, John; Hausmann, Ute; Armour, Kyle C.; Ferreira, David; Holland, Marika M.

2017-03-01

We investigate how sea surface temperatures (SSTs) around Antarctica respond to the Southern Annular Mode (SAM) on multiple timescales. To that end we examine the relationship between SAM and SST within unperturbed preindustrial control simulations of coupled general circulation models (GCMs) included in the Climate Modeling Intercomparison Project phase 5 (CMIP5). We develop a technique to extract the response of the Southern Ocean SST (55°S-70°S) to a hypothetical step increase in the SAM index. We demonstrate that in many GCMs, the expected SST step response function is nonmonotonic in time. Following a shift to a positive SAM anomaly, an initial cooling regime can transition into surface warming around Antarctica. However, there are large differences across the CMIP5 ensemble. In some models the step response function never changes sign and cooling persists, while in other GCMs the SST anomaly crosses over from negative to positive values only 3 years after a step increase in the SAM. This intermodel diversity can be related to differences in the models' climatological thermal ocean stratification in the region of seasonal sea ice around Antarctica. Exploiting this relationship, we use observational data for the time-mean meridional and vertical temperature gradients to constrain the real Southern Ocean response to SAM on fast and slow timescales.

Multi-annual modes in the 20th century temperature variability in reanalyses and CMIP5 models

NASA Astrophysics Data System (ADS)

Järvinen, Heikki; Seitola, Teija; Silén, Johan; Räisänen, Jouni

2016-11-01

A performance expectation is that Earth system models simulate well the climate mean state and the climate variability. To test this expectation, we decompose two 20th century reanalysis data sets and 12 CMIP5 model simulations for the years 1901-2005 of the monthly mean near-surface air temperature using randomised multi-channel singular spectrum analysis (RMSSA). Due to the relatively short time span, we concentrate on the representation of multi-annual variability which the RMSSA method effectively captures as separate and mutually orthogonal spatio-temporal components. This decomposition is a unique way to separate statistically significant quasi-periodic oscillations from one another in high-dimensional data sets.The main results are as follows. First, the total spectra for the two reanalysis data sets are remarkably similar in all timescales, except that the spectral power in ERA-20C is systematically slightly higher than in 20CR. Apart from the slow components related to multi-decadal periodicities, ENSO oscillations with approximately 3.5- and 5-year periods are the most prominent forms of variability in both reanalyses. In 20CR, these are relatively slightly more pronounced than in ERA-20C. Since about the 1970s, the amplitudes of the 3.5- and 5-year oscillations have increased, presumably due to some combination of forced climate change, intrinsic low-frequency climate variability, or change in global observing network. Second, none of the 12 coupled climate models closely reproduce all aspects of the reanalysis spectra, although some models represent many aspects well. For instance, the GFDL-ESM2M model has two nicely separated ENSO periods although they are relatively too prominent as compared with the reanalyses. There is an extensive Supplement and YouTube videos to illustrate the multi-annual variability of the data sets.

Sources of errors in the simulation of south Asian summer monsoon in the CMIP5 GCMs

DOE PAGES

Ashfaq, Moetasim; Rastogi, Deeksha; Mei, Rui; ...

2016-09-19

Accurate simulation of the South Asian summer monsoon (SAM) is still an unresolved challenge. There has not been a benchmark effort to decipher the origin of undesired yet virtually invariable unsuccessfulness of general circulation models (GCMs) over this region. This study analyzes a large ensemble of CMIP5 GCMs to show that most of the simulation errors in the precipitation distribution and their driving mechanisms are systematic and of similar nature across the GCMs, with biases in meridional differential heating playing a critical role in determining the timing of monsoon onset over land, the magnitude of seasonal precipitation distribution and themore » trajectories of monsoon depressions. Errors in the pre-monsoon heat low over the lower latitudes and atmospheric latent heating over the slopes of Himalayas and Karakoram Range induce significant errors in the atmospheric circulations and meridional differential heating. Lack of timely precipitation further exacerbates such errors by limiting local moisture recycling and latent heating aloft from convection. Most of the summer monsoon errors and their sources are reproducible in the land–atmosphere configuration of a GCM when it is configured at horizontal grid spacing comparable to the CMIP5 GCMs. While an increase in resolution overcomes many modeling challenges, coarse resolution is not necessarily the primary driver in the exhibition of errors over South Asia. Ultimately, these results highlight the importance of previously less well known pre-monsoon mechanisms that critically influence the strength of SAM in the GCMs and highlight the importance of land–atmosphere interactions in the development and maintenance of SAM.« less

Sources of errors in the simulation of south Asian summer monsoon in the CMIP5 GCMs

NASA Astrophysics Data System (ADS)

Ashfaq, Moetasim; Rastogi, Deeksha; Mei, Rui; Touma, Danielle; Ruby Leung, L.

2017-07-01

Accurate simulation of the South Asian summer monsoon (SAM) is still an unresolved challenge. There has not been a benchmark effort to decipher the origin of undesired yet virtually invariable unsuccessfulness of general circulation models (GCMs) over this region. This study analyzes a large ensemble of CMIP5 GCMs to show that most of the simulation errors in the precipitation distribution and their driving mechanisms are systematic and of similar nature across the GCMs, with biases in meridional differential heating playing a critical role in determining the timing of monsoon onset over land, the magnitude of seasonal precipitation distribution and the trajectories of monsoon depressions. Errors in the pre-monsoon heat low over the lower latitudes and atmospheric latent heating over the slopes of Himalayas and Karakoram Range induce significant errors in the atmospheric circulations and meridional differential heating. Lack of timely precipitation further exacerbates such errors by limiting local moisture recycling and latent heating aloft from convection. Most of the summer monsoon errors and their sources are reproducible in the land-atmosphere configuration of a GCM when it is configured at horizontal grid spacing comparable to the CMIP5 GCMs. While an increase in resolution overcomes many modeling challenges, coarse resolution is not necessarily the primary driver in the exhibition of errors over South Asia. These results highlight the importance of previously less well known pre-monsoon mechanisms that critically influence the strength of SAM in the GCMs and highlight the importance of land-atmosphere interactions in the development and maintenance of SAM.

Sources of errors in the simulation of south Asian summer monsoon in the CMIP5 GCMs

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ashfaq, Moetasim; Rastogi, Deeksha; Mei, Rui

2016-09-19

Accurate simulation of the South Asian summer monsoon (SAM) is still an unresolved challenge. There has not been a benchmark effort to decipher the origin of undesired yet virtually invariable unsuccessfulness of general circulation models (GCMs) over this region. This study analyzes a large ensemble of CMIP5 GCMs to show that most of the simulation errors in the precipitation distribution and their driving mechanisms are systematic and of similar nature across the GCMs, with biases in meridional differential heating playing a critical role in determining the timing of monsoon onset over land, the magnitude of seasonal precipitation distribution and themore » trajectories of monsoon depressions. Errors in the pre-monsoon heat low over the lower latitudes and atmospheric latent heating over the slopes of Himalayas and Karakoram Range induce significant errors in the atmospheric circulations and meridional differential heating. Lack of timely precipitation further exacerbates such errors by limiting local moisture recycling and latent heating aloft from convection. Most of the summer monsoon errors and their sources are reproducible in the land–atmosphere configuration of a GCM when it is configured at horizontal grid spacing comparable to the CMIP5 GCMs. While an increase in resolution overcomes many modeling challenges, coarse resolution is not necessarily the primary driver in the exhibition of errors over South Asia. These results highlight the importance of previously less well known pre-monsoon mechanisms that critically influence the strength of SAM in the GCMs and highlight the importance of land–atmosphere interactions in the development and maintenance of SAM.« less

ENSO Simulation in Coupled Ocean-Atmosphere Models: Are the Current Models Better?

DOE Office of Scientific and Technical Information (OSTI.GOV)

AchutaRao, K; Sperber, K R

Maintaining a multi-model database over a generation or more of model development provides an important framework for assessing model improvement. Using control integrations, we compare the simulation of the El Nino/Southern Oscillation (ENSO), and its extratropical impact, in models developed for the 2007 Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report with models developed in the late 1990's (the so-called Coupled Model Intercomparison Project-2 [CMIP2] models). The IPCC models tend to be more realistic in representing the frequency with which ENSO occurs, and they are better at locating enhanced temperature variability over the eastern Pacific Ocean. When compared withmore » reanalyses, the IPCC models have larger pattern correlations of tropical surface air temperature than do the CMIP2 models during the boreal winter peak phase of El Nino. However, for sea-level pressure and precipitation rate anomalies, a clear separation in performance between the two vintages of models is not as apparent. The strongest improvement occurs for the modeling groups whose CMIP2 model tended to have the lowest pattern correlations with observations. This has been checked by subsampling the multi-century IPCC simulations in a manner to be consistent with the single 80-year time segment available from CMIP2. Our results suggest that multi-century integrations may be required to statistically assess model improvement of ENSO. The quality of the El Nino precipitation composite is directly related to the fidelity of the boreal winter precipitation climatology, highlighting the importance of reducing systematic model error. Over North America distinct improvement of El Nino forced boreal winter surface air temperature, sea-level pressure, and precipitation rate anomalies in the IPCC models occurs. This improvement, is directly proportional to the skill of the tropical El Nino forced precipitation anomalies.« less

An evaluation of 20th century climate for the Southeastern United States as simulated by Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models

USGS Publications Warehouse

David E. Rupp,

2016-05-05

The 20th century climate for the Southeastern United States and surrounding areas as simulated by global climate models used in the Coupled Model Intercomparison Project Phase 5 (CMIP5) was evaluated. A suite of statistics that characterize various aspects of the regional climate was calculated from both model simulations and observation-based datasets. CMIP5 global climate models were ranked by their ability to reproduce the observed climate. Differences in the performance of the models between regions of the United States (the Southeastern and Northwestern United States) warrant a regional-scale assessment of CMIP5 models.

Spatial patterns of recent Antarctic surface temperature trends and the importance of natural variability: lessons from multiple reconstructions and the CMIP5 models

NASA Astrophysics Data System (ADS)

Smith, Karen L.; Polvani, Lorenzo M.

2017-04-01

The recent annually averaged warming of the Antarctic Peninsula, and of West Antarctica, stands in stark contrast to very small trends over East Antarctica. This asymmetry arises primarily from a highly significant warming of West Antarctica in austral spring and a cooling of East Antarctica in austral autumn. Here we examine whether this East-West asymmetry is a response to anthropogenic climate forcings or a manifestation of natural climate variability. We compare the observed Antarctic surface air temperature trends over two distinct time periods (1960-2005 and 1979-2005), and with those simulated by 40 models participating in Phase 5 of the Coupled Model Intercomparison Project (CMIP5). We find that the observed East-West asymmetry differs substantially between the two periods and, furthermore, that it is completely absent from the forced response seen in the CMIP5 multi-model mean, from which all natural variability is eliminated by the averaging. We also examine the relationship between the Southern Annular mode (SAM) and Antarctic temperature trends, in both models and reanalyses, and again conclude that there is little evidence of anthropogenic SAM-induced driving of the recent temperature trends. These results offer new, compelling evidence pointing to natural climate variability as a key contributor to the recent warming of West Antarctica and of the Peninsula.

New statistical downscaling for Canada

NASA Astrophysics Data System (ADS)

Murdock, T. Q.; Cannon, A. J.; Sobie, S.

2013-12-01

This poster will document the production of a set of statistically downscaled future climate projections for Canada based on the latest available RCM and GCM simulations - the North American Regional Climate Change Assessment Program (NARCCAP; Mearns et al. 2007) and the Coupled Model Intercomparison Project Phase 5 (CMIP5). The main stages of the project included (1) downscaling method evaluation, (2) scenarios selection, (3) production of statistically downscaled results, and (4) applications of results. We build upon a previous downscaling evaluation project (Bürger et al. 2012, Bürger et al. 2013) in which a quantile-based method (Bias Correction/Spatial Disaggregation - BCSD; Werner 2011) provided high skill compared with four other methods representing the majority of types of downscaling used in Canada. Additional quantile-based methods (Bias-Correction/Constructed Analogues; Maurer et al. 2010 and Bias-Correction/Climate Imprint ; Hunter and Meentemeyer 2005) were evaluated. A subset of 12 CMIP5 simulations was chosen based on an objective set of selection criteria. This included hemispheric skill assessment based on the CLIMDEX indices (Sillmann et al. 2013), historical criteria used previously at the Pacific Climate Impacts Consortium (Werner 2011), and refinement based on a modified clustering algorithm (Houle et al. 2012; Katsavounidis et al. 1994). Statistical downscaling was carried out on the NARCCAP ensemble and a subset of the CMIP5 ensemble. We produced downscaled scenarios over Canada at a daily time resolution and 300 arc second (~10 km) spatial resolution from historical runs for 1951-2005 and from RCP 2.6, 4.5, and 8.5 projections for 2006-2100. The ANUSPLIN gridded daily dataset (McKenney et al. 2011) was used as a target. It has national coverage, spans the historical period of interest 1951-2005, and has daily time resolution. It uses interpolation of station data based on thin-plate splines. This type of method has been shown to have superior skill in interpolating RCM data over North America (McGinnis et al. 2012). An early application of the new dataset was to provide projections of climate extremes for adaptation planning by the British Columbia Ministry of Transportation and Infrastructure. Recently, certain stretches of highway have experienced extreme precipitation events resulting in substantial damage to infrastructure. As part of the planning process to refurbish or replace components of these highways, information about the magnitude and frequency of future extreme events are needed to inform the infrastructure design. The increased resolution provided by downscaling improves the representation of topographic features, particularly valley temperature and precipitation effects. A range of extreme values, from simple daily maxima and minima to complex multi-day and threshold-based climate indices were computed and analyzed from the downscaled output. Selected results from this process and how the projections of precipitation extremes are being used in the context of highway infrastructure planning in British Columbia will be presented.

Weighting of NMME temperature and precipitation forecasts across Europe

NASA Astrophysics Data System (ADS)

Slater, Louise J.; Villarini, Gabriele; Bradley, A. Allen

2017-09-01

Multi-model ensemble forecasts are obtained by weighting multiple General Circulation Model (GCM) outputs to heighten forecast skill and reduce uncertainties. The North American Multi-Model Ensemble (NMME) project facilitates the development of such multi-model forecasting schemes by providing publicly-available hindcasts and forecasts online. Here, temperature and precipitation forecasts are enhanced by leveraging the strengths of eight NMME GCMs (CCSM3, CCSM4, CanCM3, CanCM4, CFSv2, GEOS5, GFDL2.1, and FLORb01) across all forecast months and lead times, for four broad climatic European regions: Temperate, Mediterranean, Humid-Continental and Subarctic-Polar. We compare five different approaches to multi-model weighting based on the equally weighted eight single-model ensembles (EW-8), Bayesian updating (BU) of the eight single-model ensembles (BU-8), BU of the 94 model members (BU-94), BU of the principal components of the eight single-model ensembles (BU-PCA-8) and BU of the principal components of the 94 model members (BU-PCA-94). We assess the forecasting skill of these five multi-models and evaluate their ability to predict some of the costliest historical droughts and floods in recent decades. Results indicate that the simplest approach based on EW-8 preserves model skill, but has considerable biases. The BU and BU-PCA approaches reduce the unconditional biases and negative skill in the forecasts considerably, but they can also sometimes diminish the positive skill in the original forecasts. The BU-PCA models tend to produce lower conditional biases than the BU models and have more homogeneous skill than the other multi-models, but with some loss of skill. The use of 94 NMME model members does not present significant benefits over the use of the 8 single model ensembles. These findings may provide valuable insights for the development of skillful, operational multi-model forecasting systems.

Potential impacts of agricultural drought on crop yield variability under a changing climate in Texas

NASA Astrophysics Data System (ADS)

Lee, K.; Leng, G.; Huang, M.; Sheffield, J.; Zhao, G.; Gao, H.

2017-12-01

Texas has the largest farm area in the U.S, and its revenue from crop production ranks third overall. With the changing climate, hydrological extremes such as droughts are becoming more frequent and intensified, causing significant yield reduction in rainfed agricultural systems. The objective of this study is to investigate the potential impacts of agricultural drought on crop yields (corn, sorghum, and wheat) under a changing climate in Texas. The Variable Infiltration Capacity (VIC) model, which is calibrated and validated over 10 major Texas river basins during the historical period, is employed in this study.The model is forced by a set of statistically downscaled climate projections from Coupled Model Intercomparison Project Phase 5 (CMIP5) model ensembles at a spatial resolution of 1/8°. The CMIP5 projections contain four Representative Concentration Pathways (RCP) that represent different greenhouse gas concentration (4.5 and 8.5 w/m2 are selected in this study). To carry out the analysis, VIC simulations from 1950 to 2099 are first analyzed to investigate how the frequency and severity of agricultural droughts will be altered in Texas (under a changing climate). Second, future crop yields are projected using a statistical crop model. Third, the effects of agricultural drought on crop yields are quantitatively analyzed. The results are expected to contribute to future water resources planning, with a goal of mitigating the negative impacts of future droughts on agricultural production in Texas.

CMIP5-downscaled projections for the NW European Shelf Seas: initial results and insights into uncertainties

NASA Astrophysics Data System (ADS)

Tinker, Jonathan; Palmer, Matthew; Lowe, Jason; Howard, Tom

2017-04-01

The North Sea, and wider Northwest European Shelf seas (NWS) are economically, environmentally, and culturally important for a number of European countries. They are protected by European legislation, often with specific reference to the potential impacts of climate change. Coastal climate change projections are an important source of information for effective management of European Shelf Seas. For example, potential changes in the marine environment are a key component of the climate change risk assessments (CCRAs) carried out under the UK Climate Change Act We use the NEMO shelf seas model combined with CMIP5 climate model and EURO-CORDEX regional atmospheric model data to generate new simulations of the NWS. Building on previous work using a climate model perturbed physics ensemble and the POLCOMS, this new model setup is used to provide first indication of the uncertainties associated with: (i) the driving climate model; (ii) the atmospheric downscaling model (iii) the shelf seas downscaling model; (iv) the choice of climate change scenario. Our analysis considers a range of physical marine impacts and the drivers of coastal variability and change, including sea level and the propagation of open ocean signals onto the shelf. The simulations are being carried out as part of the UK Climate Projections 2018 (UKCP18) and will feed into the following UK CCRA.

Changes in Extremely Hot Summers over the Global Land Area under Various Warming Targets

PubMed Central

Wang, Lei; Huang, Jianbin; Luo, Yong; Yao, Yao; Zhao, Zongci

2015-01-01

Summer temperature extremes over the global land area were investigated by comparing 26 models of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) with observations from the Goddard Institute for Space Studies (GISS) and the Climate Research Unit (CRU). Monthly data of the observations and models were averaged for each season, and statistics were calculated for individual models before averaging them to obtain ensemble means. The summers with temperature anomalies (relative to 1951–1980) exceeding 3σ (σ is based on the local internal variability) are defined as “extremely hot”. The models well reproduced the statistical characteristics evolution, and partly captured the spatial distributions of historical summer temperature extremes. If the global mean temperature increases 2°C relative to the pre-industrial level, “extremely hot” summers are projected to occur over nearly 40% of the land area (multi-model ensemble mean projection). Summers that exceed 5σ warming are projected to occur over approximately 10% of the global land area, which were rarely observed during the reference period. Scenarios reaching warming levels of 3°C to 5°C were also analyzed. After exceeding the 5°C warming target, “extremely hot” summers are projected to occur throughout the entire global land area, and summers that exceed 5σ warming would become common over 70% of the land area. In addition, the areas affected by “extremely hot” summers are expected to rapidly expand by more than 25%/°C as the global mean temperature increases by up to 3°C before slowing to less than 16%/°C as the temperature continues to increase by more than 3°C. The area that experiences summers with warming of 5σ or more above the warming target of 2°C is likely to maintain rapid expansion of greater than 17%/°C. To reduce the impacts and damage from severely hot summers, the global mean temperature increase should remain low. PMID:26090931

The Pilot Phase of the Global Soil Wetness Project Phase 3

NASA Astrophysics Data System (ADS)

Kim, H.; Oki, T.

2015-12-01

After the second phase of the Global Soil Wetness Project (GSWP2) as an early global continuous gridded multi-model analysis, a comprehensive set of land surface fluxes and state variables became available. It has been broadly utilized in the hydrology community, and its success has evolved to take advantages of recent scientific progress and to extend the relatively short time span (1986-1995) of the previous project. In the third phase proposed here (GSWP3), an extensive set of quantities for hydro-energy-eco systems will be produced to investigate their long-term (1901-2010) changes. The energy-water-carbon cycles and their interactions are also examined subcomponent-wise with appropriate model verifications in ensemble land simulations. In this study, the preliminary results and problems found from the first round analysis of the GSWP3 pilot study are shown. Also, it is discussed how the global offline simulation activity contributes to wider communities and a bigger scope such as Climate Model Intercomparison Project Phase 6 (CMIP6).

Does the projected pathway to global warming targets matter?

NASA Astrophysics Data System (ADS)

Bärring, Lars; Strandberg, Gustav

2018-02-01

Since the ‘Paris agreement’ in 2015 there has been much focus on what a +1.5 °C or +2 °C warmer world would look like. Since the focus lies on policy relevant global warming targets, or specific warming levels (SWLs), rather than a specific point in time, projections are pooled together to form SWL ensembles based on the target temperature rather than emission scenario. This study uses an ensemble of CMIP5 global model projections to analyse how well SWL ensembles represent the stabilized climate of global warming targets. The results show that the SWL ensembles exhibit significant trends that reflect the transient nature of the RCP scenarios. These trends have clear effect on the timing and clustering of monthly cold and hot extremes, even though the effect on the temperature of the extreme months is less visible. In many regions there is a link between choice of RCP scenario used in the SWL ensemble and climate change signal in the highest monthly temperatures. In other regions there is no such clear-cut link. From this we conclude that comprehensive analyses of what prospects the different global warming targets bring about will require stabilization scenarios. Awaiting such targeted scenarios we suggest that prudent use of SWL scenarios, taking their characteristics and limitations into account, may serve as reasonable proxies in many situations.

Expansion of the On-line Archive "Statistically Downscaled WCRP CMIP3 Climate Projections"

NASA Astrophysics Data System (ADS)

Brekke, L. D.; Pruitt, T.; Maurer, E. P.; Das, T.; Duffy, P.; White, K.

2009-12-01

Presentation highlights status and plans for a public-access archive of downscaled CMIP3 climate projections. Incorporating climate projection information into long-term evaluations of water and energy resources requires analysts to have access to projections at "basin-relevant" resolution. Such projections would ideally be bias-corrected to account for climate model tendencies to systematically simulate historical conditions different than observed. In 2007, the U.S. Bureau of Reclamation, Santa Clara University and Lawrence Livermore National Laboratory (LLNL) collaborated to develop an archive of 112 bias-corrected and spatially disaggregated (BCSD) CMIP3 temperature and precipitation projections. These projections were generated using 16 CMIP3 models to simulate three emissions pathways (A2, A1b, and B1) from one or more initializations (runs). Projections are specified on a monthly time step from 1950-2099 and at 0.125 degree spatial resolution within the North American Land Data Assimilation System domain (i.e. contiguous U.S., southern Canada and northern Mexico). Archive data are freely accessible at LLNL Green Data Oasis (url). Since being launched, the archive has served over 3500 data requests by nearly 500 users in support of a range of planning, research and educational activities. Archive developers continue to look for ways to improve the archive and respond to user needs. One request has been to serve the intermediate datasets generated during the BCSD procedure, helping users to interpret the relative influences of the bias-correction and spatial disaggregation on the transformed CMIP3 output. This request has been addressed with intermediate datasets now posted at the archive web-site. Another request relates closely to studying hydrologic and ecological impacts under climate change, where users are asking for projected diurnal temperature information (e.g., projected daily minimum and maximum temperature) and daily time step resolution. In response, archive developers are adding content in 2010, teaming with Scripps Institution of Oceanography (through their NOAA-RISA California-Nevada Applications Program and the California Climate Change Center) to apply a new daily downscaling technique to a sub-ensemble of the archive’s CMIP3 projections. The new technique, Bias-Corrected Constructed Analogs, combines the BC part of BCSD with a recently developed technique that preserves the daily sequencing structure of CMIP3 projections (Constructed Analogs, or CA). Such data will more easily serve hydrologic and ecological impacts assessments, and offer an opportunity to evaluate projection uncertainty associated with downscaling technique. Looking ahead to the arrival CMIP5 projections, archive collaborators have plans apply both BCSD and BCCA over the contiguous U.S. consistent with CMIP3 applications above, and also apply BCSD globally at a 0.5 degree spatial resolution. The latter effort involves collaboration with U.S. Army Corps of Engineers (USACE) and Climate Central.

Response of the oceanic methane hydrate inventory to future climate change (AR5 RCP 4.5 - 8.5)

NASA Astrophysics Data System (ADS)

Hunter, S. J.; Goldobin, D.; Haywood, A. M.; Ridgwell, A. J.; Rees, J.

2012-12-01

We present results from a study designed to look at the change in global methane hydrate volume in response to AR5 Representative Concentration Pathways (Fifth Assessment Report RCP). We use bottom water conditions derived from 12 climate models within the CMIP5 multi-model ensemble along with a series of linear sea-level models to define boundary conditions. We model the change in global hydrate stability zone volume and hydrate inventory from the pre-industrial era and forward model through the RCP scenarios (to 2100 and 2300) to 5 kyr into the future. We find that thermal effects (i.e. warming induced hydrate dissociation) are dominant even when accompanied by extreme rates of sea level rise (i.e. 15 and 20 mm yr-1). Over the coming century dissociation is focussed within the top 100 m of Arctic and Subarctic sediments, beneath < ˜500 m water depth. Assuming a simple model of hydrate fill fraction (with a nominal 1% average hydrate-fill fraction) estimated globally integrated hydrate dissociation rates at ˜2100 are 120, 140 and 180 Tg CH4 yr-1 for RCP 4.5, 6.0 and 8.5 and at year ˜2300 are 150 and 600 Tg CH4 yr-1 under ECP 4.5 and 8.5 respectively. Under the unmitigated business-as-usual scenario (RCP 8.5) globally-integrated CH4 fluxes from hydrate dissociation could exceed estimates of natural sea-floor levels by 2100. Subsequent oxidation of resulting CH4 within the water column would significantly reduce atmospheric release rates to between ˜0.7 and ˜1.4 Tg CH4 yr-1 at ˜2100.

Simulation and Projection of the Western Pacific Subtropical High by CMIP5 Models

NASA Astrophysics Data System (ADS)

Liu, Y.

2016-12-01

This work examined the performance of 26 coupled climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in the simulation of the present-day temporal variability and spatial pattern of the western Pacific subtropical high (WPSH). The results show that most models are able to capture the spatial distribution and variability of the 500-hPa geopotential height and zonal wind fields in the western subtropical Pacific, with the underestimation of the mean intensities of WPSH. The underestimation may be associated with the cold bias of sea surface temperature in the tropical Indian and western Pacific oceans in the models. To eliminate the impact of the climatology biases, the climatology of these models is replaced by that of the NCEP/NCAR reanalysis in the verification. It is noted that on interdecadal timescales, the models reproduce the shift of WPSH with enhancement and westward extension after the late 1970s. According to assessment of the simulations of the WPSH indices, it is found that some models (CNRM-CM5, FGOALS-g2, FIO-ESM, MIROC-ESM, and MPI-ESM-P) are better than others in simulating WPSH. Then, the ensemble mean of these better models are used to project the future changes of WPSH under three typical representation concentration pathway scenarios (RCP8.5, RCP4.5, and RCP2.6). It is suggested that the WPSH enlarges and strengthens, and its position extends westward under the scenarios, with the largest linear growth trend in RCP8.5, smallest in RCP2.6, and in between in RCP4.5; while the ridge line of WPSH does not show obvious long-term trend. These results may have implications for the attribution and prediction of climate variations and changes in East Asia.

Future change of Asian-Australian monsoon under RCP 4.5 anthropogenic warming scenario

NASA Astrophysics Data System (ADS)

Wang, Bin; Yim, So-Young; Lee, June-Yi; Liu, Jian; Ha, Kyung-Ja

2014-01-01

We investigate the future changes of Asian-Australian monsoon (AAM) system projected by 20 climate models that participated in the phase five of the Coupled Model Intercomparison Project (CMIP5). A metrics for evaluation of the model's performance on AAM precipitation climatology and variability is used to select a subset of seven best models. The CMIP5 models are more skillful than the CMIP3 models in terms of the AAM metrics. The future projections made by the selected multi-model mean suggest the following changes by the end of the 21st century. (1) The total AAM precipitation (as well as the land and oceanic components) will increase significantly (by 4.5 %/°C) mainly due to the increases in Indian summer monsoon (5.0 %/°C) and East Asian summer monsoon (6.4 %/°C) rainfall; the Australian summer monsoon rainfall will increase moderately by 2.6 %/°C. The "warm land-cool ocean" favors the entire AAM precipitation increase by generation of an east-west asymmetry in the sea level pressure field. On the other hand, the warm Northern Hemisphere-cool Southern Hemisphere induced hemispheric SLP difference favors the ASM but reduces the Australian summer monsoon rainfall. The combined effects explain the differences between the Asian and Australian monsoon changes. (2) The low-level tropical AAM circulation will weaken significantly (by 2.3 %/°C) due to atmospheric stabilization that overrides the effect of increasing moisture convergence. Different from the CMIP3 analysis, the EA subtropical summer monsoon circulation will increase by 4.4 %/°C. (3) The Asian monsoon domain over the land area will expand by about 10 %. (4) The spatial structures of the leading mode of interannual variation of AAM precipitation will not change appreciably but the ENSO-AAM relationship will be significantly enhanced.

Koppen bioclimatic evaluation of CMIP historical climate simulations

DOE PAGES

Phillips, Thomas J.; Bonfils, Celine J. W.

2015-06-05

Köppen bioclimatic classification relates generic vegetation types to characteristics of the interactive annual-cycles of continental temperature (T) and precipitation (P). In addition to predicting possible bioclimatic consequences of past or prospective climate change, a Köppen scheme can be used to pinpoint biases in model simulations of historical T and P. In this study a Köppen evaluation of Coupled Model Intercomparison Project (CMIP) simulations of historical climate is conducted for the period 1980–1999. Evaluation of an example CMIP5 model illustrates how errors in simulating Köppen vegetation types (relative to those derived from observational reference data) can be deconstructed and related tomore » model-specific temperature and precipitation biases. Measures of CMIP model skill in simulating the reference Köppen vegetation types are also developed, allowing the bioclimatic performance of a CMIP5 simulation of T and P to be compared quantitatively with its CMIP3 antecedent. Although certain bioclimatic discrepancies persist across model generations, the CMIP5 models collectively display an improved rendering of historical T and P relative to their CMIP3 counterparts. Additionally, the Köppen-based performance metrics are found to be quite insensitive to alternative choices of observational reference data or to differences in model horizontal resolution.« less

Analyzing the future climate change of Upper Blue Nile River basin using statistical downscaling techniques

NASA Astrophysics Data System (ADS)

Fenta Mekonnen, Dagnenet; Disse, Markus

2018-04-01

Climate change is becoming one of the most threatening issues for the world today in terms of its global context and its response to environmental and socioeconomic drivers. However, large uncertainties between different general circulation models (GCMs) and coarse spatial resolutions make it difficult to use the outputs of GCMs directly, especially for sustainable water management at regional scale, which introduces the need for downscaling techniques using a multimodel approach. This study aims (i) to evaluate the comparative performance of two widely used statistical downscaling techniques, namely the Long Ashton Research Station Weather Generator (LARS-WG) and the Statistical Downscaling Model (SDSM), and (ii) to downscale future climate scenarios of precipitation, maximum temperature (Tmax) and minimum temperature (Tmin) of the Upper Blue Nile River basin at finer spatial and temporal scales to suit further hydrological impact studies. The calibration and validation result illustrates that both downscaling techniques (LARS-WG and SDSM) have shown comparable and good ability to simulate the current local climate variables. Further quantitative and qualitative comparative performance evaluation was done by equally weighted and varying weights of statistical indexes for precipitation only. The evaluation result showed that SDSM using the canESM2 CMIP5 GCM was able to reproduce more accurate long-term mean monthly precipitation but LARS-WG performed best in capturing the extreme events and distribution of daily precipitation in the whole data range. Six selected multimodel CMIP3 GCMs, namely HadCM3, GFDL-CM2.1, ECHAM5-OM, CCSM3, MRI-CGCM2.3.2 and CSIRO-MK3 GCMs, were used for downscaling climate scenarios by the LARS-WG model. The result from the ensemble mean of the six GCM showed an increasing trend for precipitation, Tmax and Tmin. The relative change in precipitation ranged from 1.0 to 14.4 % while the change for mean annual Tmax may increase from 0.4 to 4.3 °C and the change for mean annual Tmin may increase from 0.3 to 4.1 °C. The individual result of the HadCM3 GCM has a good agreement with the ensemble mean result. HadCM3 from CMIP3 using A2a and B2a scenarios and canESM2 from CMIP5 GCMs under RCP2.6, RCP4.5 and RCP8.5 scenarios were downscaled by SDSM. The result from the two GCMs under five different scenarios agrees with the increasing direction of three climate variables (precipitation, Tmax and Tmin). The relative change of the downscaled mean annual precipitation ranges from 2.1 to 43.8 % while the change for mean annual Tmax and Tmin may increase in the range from 0.4 to 2.9 °C and from 0.3 to 1.6 °C respectively.

Crop model improvement reduces the uncertainty of the response to temperature of multi-model ensembles

USDA-ARS?s Scientific Manuscript database

To improve climate change impact estimates, multi-model ensembles (MMEs) have been suggested. MMEs enable quantifying model uncertainty, and their medians are more accurate than that of any single model when compared with observations. However, multi-model ensembles are costly to execute, so model i...

Canadian snow and sea ice: assessment of snow, sea ice, and related climate processes in Canada's Earth system model and climate-prediction system

NASA Astrophysics Data System (ADS)

Kushner, Paul J.; Mudryk, Lawrence R.; Merryfield, William; Ambadan, Jaison T.; Berg, Aaron; Bichet, Adéline; Brown, Ross; Derksen, Chris; Déry, Stephen J.; Dirkson, Arlan; Flato, Greg; Fletcher, Christopher G.; Fyfe, John C.; Gillett, Nathan; Haas, Christian; Howell, Stephen; Laliberté, Frédéric; McCusker, Kelly; Sigmond, Michael; Sospedra-Alfonso, Reinel; Tandon, Neil F.; Thackeray, Chad; Tremblay, Bruno; Zwiers, Francis W.

2018-04-01

The Canadian Sea Ice and Snow Evolution (CanSISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models. It is found that the ability of the CanESM2 simulation to capture snow-related climate parameters, such as cold-region surface temperature and precipitation, lies within the range of currently available international models. Accounting for the considerable disagreement among satellite-era observational datasets on the distribution of snow water equivalent, CanESM2 has too much springtime snow mass over Canada, reflecting a broader northern hemispheric positive bias. Biases in seasonal snow cover extent are generally less pronounced. CanESM2 also exhibits retreat of springtime snow generally greater than observational estimates, after accounting for observational uncertainty and internal variability. Sea ice is biased low in the Canadian Arctic, which makes it difficult to assess the realism of long-term sea ice trends there. The strengths and weaknesses of the modelling system need to be understood as a practical tradeoff: the Canadian models are relatively inexpensive computationally because of their moderate resolution, thus enabling their use in operational seasonal prediction and for generating large ensembles of multidecadal simulations. Improvements in climate-prediction systems like CanSIPS rely not just on simulation quality but also on using novel observational constraints and the ready transfer of research to an operational setting. Improvements in seasonal forecasting practice arising from recent research include accurate initialization of snow and frozen soil, accounting for observational uncertainty in forecast verification, and sea ice thickness initialization using statistical predictors available in real time.

Downscale climate change scenarios over the Western Himalayan region of India using multi-generation CMIP experiments

NASA Astrophysics Data System (ADS)

Das, Lalu; Meher, Jitendra K.; Akhter, Javed

2017-04-01

Assessing climate change information over the Western Himalayan Region (WHR) of India is crucial but challenging task due to its limited numbers of station data containing huge missing values. The issues of missing values of station data were replaced the Multiple Imputation Chained Equation (MICE) technique. Finally 22 numbers of rain gauge stations having continuous data during 1901-2005 and 16 numbers stations having continuous temperature data during 1969-2009 were considered as " reference stations for assessing rainfall and temperature trends in addition to evaluation of the GCMs available in the Coupled Model Intercomparison Project, Phase 3 (CMIP3) and phase 5 (CMIP5) over WRH. Station data indicates that the winter warming is higher and rapid (1.05oC) than other seasons and less warming in the post monsoon season in the last 41 years. Area averaged using 22 station data indicates that monsoon and winter rainfall has decreased by -5 mm and -320 mm during 1901-2000 while pre-monsoon and post monsoon showed an increasing trends of 21 mm and 13 mm respectively. Present study is constructed the downscaled climate change information at station locations (22 and 16 stations for rainfall and temperature respectively) over the WHR from the GCMs commonly available in the IPCC's different generations assessment reports namely 2nd, 3rd, 4th and 5th thereafter known as SAR, TAR, AR4 and AR5 respectively. Once the downscaled results are obtained for each generation model outputs, then a comparison of studies is carried out from the results of each generation. Finally an overall model improvement index (OMII) is developed using the downscaling results which is used to investigate the model improvement across generations as well as the improvement of downscaling results obtained from the empirical statistical downscaling (ESD) methods. In general, the results indicate that there is a gradual improvement of GCMs simulations as well as downscaling results across generation. Key words: MICE Techniques, CMIP3, CMIP5, ESD and OMII

New insights for the hydrology of the Rhine based on the new generation climate models

NASA Astrophysics Data System (ADS)

Bouaziz, Laurène; Sperna Weiland, Frederiek; Beersma, Jules; Buiteveld, Hendrik

2014-05-01

Decision makers base their choices of adaptation strategies on climate change projections and their associated hydrological consequences. New insights of climate change gained under the new generation of climate models belonging to the IPCC 5th assessment report may influence (the planning of) adaption measures and/or future expectations. In this study, hydrological impacts of climate change as projected under the new generation of climate models for the Rhine were assessed. Hereto we downscaled 31 General Circulation Models (GCMs), which were developed as part of the Coupled Model Intercomparison Project Phase 5 (CMIP5), using an advanced Delta Change Method for the Rhine basin. Changes in mean monthly, maximum and minimum flows at Lobith were derived with the semi-distributed hydrological model HBV of the Rhine. The projected changes were compared to changes that were previously obtained in the trans-boundary project Rheinblick using eight CMIP3 GCMs and Regional Climate Models (RCMs) for emission scenario A1B. All eight selected CMIP3 models (scenario A1B) predicted for 2071-2100 a decrease in mean monthly flows between June and October. Similar decreases were found for some of the 31 CMIP5 models for Representative Concentration Pathways (RCPs) 4.5, 6.0 and 8.5. However, under each RCP, there were also models that projected an increase in mean flows between June and October and on average the decrease was smaller than for the eight CMIP3 models. For 2071-2100, also the mean annual minimum 7-days discharge decreased less in the CMIP5 model simulations than was projected in CMIP3. When assessing the response of mean monthly flows of the CMIP5 simulation with the CSIRO-Mk3-6-0 and HadGEM2-ES models with respect to initial conditions and RCPs, it was found that natural variability plays a dominant role in the near future (2021-2050), while changes in mean monthly flows are dominated by the radiative forcing in the far future (2071-2100). According to RCP 8.5 model simulations, the change in mean monthly flow from May to November may be half the change in mean monthly flow projected by RCP 4.5. From January to March, RCP 8.5 simulations projected higher changes in mean monthly flows than RCP 4.5 simulations. These new insights based on the CMIP5 simulations imply that for the Rhine, the mean and low flow extremes might not decrease as much in summer as was expected under CMIP3. Stresses on water availability during summer are therefore also less than expected from CMIP3.

Non-stationary Bias Correction of Monthly CMIP5 Temperature Projections over China using a Residual-based Bagging Tree Model

NASA Astrophysics Data System (ADS)

Yang, T.; Lee, C.

2017-12-01

The biases in the Global Circulation Models (GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed Residual-based Bagging Tree (RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modeling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared to the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios (RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44 ºC, 2.59 ºC, and 4.71 ºC by the end of the century, respectively, when compared to the average temperature during 1970 - 1999.

Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model

PubMed Central

Alfieri, Lorenzo; Bradshaw, Catherine; Caesar, John; Feyen, Luc; Friedlingstein, Pierre; Gohar, Laila; Koutroulis, Aristeidis; Lewis, Kirsty; Morfopoulos, Catherine; Papadimitriou, Lamprini; Richardson, Katy J.; Tsanis, Ioannis; Wyser, Klaus

2018-01-01

We projected changes in weather extremes, hydrological impacts and vulnerability to food insecurity at global warming of 1.5°C and 2°C relative to pre-industrial, using a new global atmospheric general circulation model HadGEM3A-GA3.0 driven by patterns of sea-surface temperatures and sea ice from selected members of the 5th Coupled Model Intercomparison Project (CMIP5) ensemble, forced with the RCP8.5 concentration scenario. To provide more detailed representations of climate processes and impacts, the spatial resolution was N216 (approx. 60 km grid length in mid-latitudes), a higher resolution than the CMIP5 models. We used a set of impacts-relevant indices and a global land surface model to examine the projected changes in weather extremes and their implications for freshwater availability and vulnerability to food insecurity. Uncertainties in regional climate responses are assessed, examining ranges of outcomes in impacts to inform risk assessments. Despite some degree of inconsistency between components of the study due to the need to correct for systematic biases in some aspects, the outcomes from different ensemble members could be compared for several different indicators. The projections for weather extremes indices and biophysical impacts quantities support expectations that the magnitude of change is generally larger for 2°C global warming than 1.5°C. Hot extremes become even hotter, with increases being more intense than seen in CMIP5 projections. Precipitation-related extremes show more geographical variation with some increases and some decreases in both heavy precipitation and drought. There are substantial regional uncertainties in hydrological impacts at local scales due to different climate models producing different outcomes. Nevertheless, hydrological impacts generally point towards wetter conditions on average, with increased mean river flows, longer heavy rainfall events, particularly in South and East Asia with the most extreme projections suggesting more than a doubling of flows in the Ganges at 2°C global warming. Some areas are projected to experience shorter meteorological drought events and less severe low flows, although longer droughts and/or decreases in low flows are projected in many other areas, particularly southern Africa and South America. Flows in the Amazon are projected to decline by up to 25%. Increases in either heavy rainfall or drought events imply increased vulnerability to food insecurity, but if global warming is limited to 1.5°C, this vulnerability is projected to remain smaller than at 2°C global warming in approximately 76% of developing countries. At 2°C, four countries are projected to reach unprecedented levels of vulnerability to food insecurity. This article is part of the theme issue ‘The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels’. PMID:29610383

Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model

NASA Astrophysics Data System (ADS)

Betts, Richard A.; Alfieri, Lorenzo; Bradshaw, Catherine; Caesar, John; Feyen, Luc; Friedlingstein, Pierre; Gohar, Laila; Koutroulis, Aristeidis; Lewis, Kirsty; Morfopoulos, Catherine; Papadimitriou, Lamprini; Richardson, Katy J.; Tsanis, Ioannis; Wyser, Klaus

2018-05-01

We projected changes in weather extremes, hydrological impacts and vulnerability to food insecurity at global warming of 1.5°C and 2°C relative to pre-industrial, using a new global atmospheric general circulation model HadGEM3A-GA3.0 driven by patterns of sea-surface temperatures and sea ice from selected members of the 5th Coupled Model Intercomparison Project (CMIP5) ensemble, forced with the RCP8.5 concentration scenario. To provide more detailed representations of climate processes and impacts, the spatial resolution was N216 (approx. 60 km grid length in mid-latitudes), a higher resolution than the CMIP5 models. We used a set of impacts-relevant indices and a global land surface model to examine the projected changes in weather extremes and their implications for freshwater availability and vulnerability to food insecurity. Uncertainties in regional climate responses are assessed, examining ranges of outcomes in impacts to inform risk assessments. Despite some degree of inconsistency between components of the study due to the need to correct for systematic biases in some aspects, the outcomes from different ensemble members could be compared for several different indicators. The projections for weather extremes indices and biophysical impacts quantities support expectations that the magnitude of change is generally larger for 2°C global warming than 1.5°C. Hot extremes become even hotter, with increases being more intense than seen in CMIP5 projections. Precipitation-related extremes show more geographical variation with some increases and some decreases in both heavy precipitation and drought. There are substantial regional uncertainties in hydrological impacts at local scales due to different climate models producing different outcomes. Nevertheless, hydrological impacts generally point towards wetter conditions on average, with increased mean river flows, longer heavy rainfall events, particularly in South and East Asia with the most extreme projections suggesting more than a doubling of flows in the Ganges at 2°C global warming. Some areas are projected to experience shorter meteorological drought events and less severe low flows, although longer droughts and/or decreases in low flows are projected in many other areas, particularly southern Africa and South America. Flows in the Amazon are projected to decline by up to 25%. Increases in either heavy rainfall or drought events imply increased vulnerability to food insecurity, but if global warming is limited to 1.5°C, this vulnerability is projected to remain smaller than at 2°C global warming in approximately 76% of developing countries. At 2°C, four countries are projected to reach unprecedented levels of vulnerability to food insecurity. This article is part of the theme issue `The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'.

Changes in climate extremes, fresh water availability and vulnerability to food insecurity projected at 1.5°C and 2°C global warming with a higher-resolution global climate model.

PubMed

Betts, Richard A; Alfieri, Lorenzo; Bradshaw, Catherine; Caesar, John; Feyen, Luc; Friedlingstein, Pierre; Gohar, Laila; Koutroulis, Aristeidis; Lewis, Kirsty; Morfopoulos, Catherine; Papadimitriou, Lamprini; Richardson, Katy J; Tsanis, Ioannis; Wyser, Klaus

2018-05-13

We projected changes in weather extremes, hydrological impacts and vulnerability to food insecurity at global warming of 1.5°C and 2°C relative to pre-industrial, using a new global atmospheric general circulation model HadGEM3A-GA3.0 driven by patterns of sea-surface temperatures and sea ice from selected members of the 5th Coupled Model Intercomparison Project (CMIP5) ensemble, forced with the RCP8.5 concentration scenario. To provide more detailed representations of climate processes and impacts, the spatial resolution was N216 (approx. 60 km grid length in mid-latitudes), a higher resolution than the CMIP5 models. We used a set of impacts-relevant indices and a global land surface model to examine the projected changes in weather extremes and their implications for freshwater availability and vulnerability to food insecurity. Uncertainties in regional climate responses are assessed, examining ranges of outcomes in impacts to inform risk assessments. Despite some degree of inconsistency between components of the study due to the need to correct for systematic biases in some aspects, the outcomes from different ensemble members could be compared for several different indicators. The projections for weather extremes indices and biophysical impacts quantities support expectations that the magnitude of change is generally larger for 2°C global warming than 1.5°C. Hot extremes become even hotter, with increases being more intense than seen in CMIP5 projections. Precipitation-related extremes show more geographical variation with some increases and some decreases in both heavy precipitation and drought. There are substantial regional uncertainties in hydrological impacts at local scales due to different climate models producing different outcomes. Nevertheless, hydrological impacts generally point towards wetter conditions on average, with increased mean river flows, longer heavy rainfall events, particularly in South and East Asia with the most extreme projections suggesting more than a doubling of flows in the Ganges at 2°C global warming. Some areas are projected to experience shorter meteorological drought events and less severe low flows, although longer droughts and/or decreases in low flows are projected in many other areas, particularly southern Africa and South America. Flows in the Amazon are projected to decline by up to 25%. Increases in either heavy rainfall or drought events imply increased vulnerability to food insecurity, but if global warming is limited to 1.5°C, this vulnerability is projected to remain smaller than at 2°C global warming in approximately 76% of developing countries. At 2°C, four countries are projected to reach unprecedented levels of vulnerability to food insecurity.This article is part of the theme issue 'The Paris Agreement: understanding the physical and social challenges for a warming world of 1.5°C above pre-industrial levels'. © 2018 The Authors.

Drivers of Arctic Ocean warming in CMIP5 models

NASA Astrophysics Data System (ADS)

Burgard, Clara; Notz, Dirk

2017-05-01

We investigate changes in the Arctic Ocean energy budget simulated by 26 general circulation models from the Coupled Model Intercomparison Project Phase 5 framework. Our goal is to understand whether the Arctic Ocean warming between 1961 and 2099 is primarily driven by changes in the net atmospheric surface flux or by changes in the meridional oceanic heat flux. We find that the simulated Arctic Ocean warming is driven by positive anomalies in the net atmospheric surface flux in 11 models, by positive anomalies in the meridional oceanic heat flux in 11 models, and by positive anomalies in both energy fluxes in four models. The different behaviors are mainly characterized by the different changes in meridional oceanic heat flux that lead to different changes in the turbulent heat loss to the atmosphere. The multimodel ensemble mean is hence not representative of a consensus across the models in Arctic climate projections.

Climate pattern-scaling set for an ensemble of 22 GCMs - adding uncertainty to the IMOGEN version 2.0 impact system

NASA Astrophysics Data System (ADS)

Zelazowski, Przemyslaw; Huntingford, Chris; Mercado, Lina M.; Schaller, Nathalie

2018-02-01

Global circulation models (GCMs) are the best tool to understand climate change, as they attempt to represent all the important Earth system processes, including anthropogenic perturbation through fossil fuel burning. However, GCMs are computationally very expensive, which limits the number of simulations that can be made. Pattern scaling is an emulation technique that takes advantage of the fact that local and seasonal changes in surface climate are often approximately linear in the rate of warming over land and across the globe. This allows interpolation away from a limited number of available GCM simulations, to assess alternative future emissions scenarios. In this paper, we present a climate pattern-scaling set consisting of spatial climate change patterns along with parameters for an energy-balance model that calculates the amount of global warming. The set, available for download, is derived from 22 GCMs of the WCRP CMIP3 database, setting the basis for similar eventual pattern development for the CMIP5 and forthcoming CMIP6 ensemble. Critically, it extends the use of the IMOGEN (Integrated Model Of Global Effects of climatic aNomalies) framework to enable scanning across full uncertainty in GCMs for impact studies. Across models, the presented climate patterns represent consistent global mean trends, with a maximum of 4 (out of 22) GCMs exhibiting the opposite sign to the global trend per variable (relative humidity). The described new climate regimes are generally warmer, wetter (but with less snowfall), cloudier and windier, and have decreased relative humidity. Overall, when averaging individual performance across all variables, and without considering co-variance, the patterns explain one-third of regional change in decadal averages (mean percentage variance explained, PVE, 34.25 ± 5.21), but the signal in some models exhibits much more linearity (e.g. MIROC3.2(hires): 41.53) than in others (GISS_ER: 22.67). The two most often considered variables, near-surface temperature and precipitation, have a PVE of 85.44 ± 4.37 and 14.98 ± 4.61, respectively. We also provide an example assessment of a terrestrial impact (changes in mean runoff) and compare projections by the IMOGEN system, which has one land surface model, against direct GCM outputs, which all have alternative representations of land functioning. The latter is noted as an additional source of uncertainty. Finally, current and potential future applications of the IMOGEN version 2.0 modelling system in the areas of ecosystem modelling and climate change impact assessment are presented and discussed.

A First Look at Decadal Hydrological Predictability by Land Surface Ensemble Simulations

NASA Astrophysics Data System (ADS)

Yuan, Xing; Zhu, Enda

2018-03-01

The prediction of terrestrial hydrology at the decadal scale is critical for managing water resources in the face of climate change. Here we conducted an assessment by global land model simulations following the design of the fifth Coupled Model Intercomparison Project (CMIP5) decadal hindcast experiments, specifically testing for the sensitivity to perfect initial or boundary conditions. The memory for terrestrial water storage (TWS) is longer than 6 years over 11% of global land areas where the deep soil moisture and aquifer water have a long memory and a nonnegligible variability. Ensemble decadal predictions based on realistic initial conditions are skillful over 31%, 43%, and 59% of global land areas for TWS, deep soil moisture, and aquifer water, respectively. The fraction of skillful predictions for TWS increases by 10%-16% when conditioned on Pacific Decadal Oscillation and Atlantic Multidecadal Oscillation indices. This study provides a first look at decadal hydrological predictability, with an improved skill when incorporating low-frequency climate information.

Changes of Climate Extremes in Urmia Lake Basin: Observations and Multimodel Ensemble Projections

NASA Astrophysics Data System (ADS)

Ashraf, B.; AghaKouchak, A.

2017-12-01

This study presents an analysis of the changes in temperature and precipitation extremes in Urmia Lake Basin, in Iran in 21th century. The latest observations in the past three decades and multimodel ensemble projections from eleven General Circulation Models (GCMs) under the three Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 scenarios are employed for analysis in this study. The twenty-seven indicative temperature and precipitation indices recommended by the joint World Meteorological Organization CCL/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI) were used for assessing changes in extremes. Results indicate that most warm (cold) extreme temperature indices have shown significantly positive (negative) trends in the Urmia Lake Basin in past three decades, while only slight changes in precipitation extremes can be observed. Ensemble projection from Bayesian Model Averaging (BMA) of Phase 5 of the Coupled Model Intercomparison Project (CMIP5) suggests that the increasing consecutive dry days (CDD), together with the decreasing frost day (FD) and increasing warm nights frequency (TN90) contribute to more frequent/severe droughts in Urmia Lake Basin. Meanwhile, the results show slight increase of annual count of days with precipitation of more than 10 mm (R10), maximum 5-day precipitation total (R5D), simple daily intensity index (SDII), and annual total precipitation with precipitation >95th percentile (R95) in projections. Our finding provides information on how extremes might change in the future from a wide range of scenarios that can potentially be sued for water resource and eco-environmental planning and adaptation strategies.

CMIP5 Scientific Gaps and Recommendations for CMIP6

DOE PAGES

Stouffer, R. J.; Eyring, V.; Meehl, G. A.; ...

2017-01-23

The Coupled Model Intercomparison Project (CMIP) is an ongoing coordinated international activity of numerical experimentation of unprecedented scope and impact on climate science. Its most recent phase, the fifth phase (CMIP5), has created nearly 2 PB of output from dozens of experiments performed by dozens of comprehensive climate models available to the climate science research community. In so doing, it has greatly advanced climate science. While CMIP5 has given answers to important science questions, with the help of a community survey we identify and motivate three broad topics here that guided the scientific framework of the next phase of CMIP,more » that is, CMIP6: (1) How does the Earth system respond to changes in forcing? (2) What are the origins and consequences of systematic model biases? (3) How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? CMIP has demonstrated the power of idealized experiments to better understand how the climate system works. We expect that these idealized approaches will continue to contribute to CMIP6. The quantification of radiative forcings and responses was poor, and thus it requires new methods and experiments to address this gap. There are a number of systematic model biases that appear in all phases of CMIP that remain a major climate modeling challenge. In conclusion, these biases need increased attention to better understand their origins and consequences through targeted experiments. Improving understanding of the mechanisms’ underlying internal climate variability for more skillful decadal climate predictions and long-term projections remains another challenge for CMIP6.« less

CMIP5 Scientific Gaps and Recommendations for CMIP6

DOE Office of Scientific and Technical Information (OSTI.GOV)

Stouffer, R. J.; Eyring, V.; Meehl, G. A.

The Coupled Model Intercomparison Project (CMIP) is an ongoing coordinated international activity of numerical experimentation of unprecedented scope and impact on climate science. Its most recent phase, the fifth phase (CMIP5), has created nearly 2 PB of output from dozens of experiments performed by dozens of comprehensive climate models available to the climate science research community. In so doing, it has greatly advanced climate science. While CMIP5 has given answers to important science questions, with the help of a community survey we identify and motivate three broad topics here that guided the scientific framework of the next phase of CMIP,more » that is, CMIP6: (1) How does the Earth system respond to changes in forcing? (2) What are the origins and consequences of systematic model biases? (3) How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? CMIP has demonstrated the power of idealized experiments to better understand how the climate system works. We expect that these idealized approaches will continue to contribute to CMIP6. The quantification of radiative forcings and responses was poor, and thus it requires new methods and experiments to address this gap. There are a number of systematic model biases that appear in all phases of CMIP that remain a major climate modeling challenge. In conclusion, these biases need increased attention to better understand their origins and consequences through targeted experiments. Improving understanding of the mechanisms’ underlying internal climate variability for more skillful decadal climate predictions and long-term projections remains another challenge for CMIP6.« less

Multi-objective optimization for generating a weighted multi-model ensemble

NASA Astrophysics Data System (ADS)

Lee, H.

2017-12-01

Many studies have demonstrated that multi-model ensembles generally show better skill than each ensemble member. When generating weighted multi-model ensembles, the first step is measuring the performance of individual model simulations using observations. There is a consensus on the assignment of weighting factors based on a single evaluation metric. When considering only one evaluation metric, the weighting factor for each model is proportional to a performance score or inversely proportional to an error for the model. While this conventional approach can provide appropriate combinations of multiple models, the approach confronts a big challenge when there are multiple metrics under consideration. When considering multiple evaluation metrics, it is obvious that a simple averaging of multiple performance scores or model ranks does not address the trade-off problem between conflicting metrics. So far, there seems to be no best method to generate weighted multi-model ensembles based on multiple performance metrics. The current study applies the multi-objective optimization, a mathematical process that provides a set of optimal trade-off solutions based on a range of evaluation metrics, to combining multiple performance metrics for the global climate models and their dynamically downscaled regional climate simulations over North America and generating a weighted multi-model ensemble. NASA satellite data and the Regional Climate Model Evaluation System (RCMES) software toolkit are used for assessment of the climate simulations. Overall, the performance of each model differs markedly with strong seasonal dependence. Because of the considerable variability across the climate simulations, it is important to evaluate models systematically and make future projections by assigning optimized weighting factors to the models with relatively good performance. Our results indicate that the optimally weighted multi-model ensemble always shows better performance than an arithmetic ensemble mean and may provide reliable future projections.

An Evaluation of CMIP5 Precipitation Variability for China Relative to Observations and CMIP3

NASA Astrophysics Data System (ADS)

Frauenfeld, O. W.; Chen, L.

2013-12-01

Precipitation represents an important link between the atmosphere, hydrosphere, and biosphere and is thus a key component of the climate system. As indicated by the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), global surface air temperatures increased by 0.74°C during the 20th century, with further warming of 0.2°C/decade projected by the 2030s. Projected changes in precipitation, however, are much more variable, and exhibit more complex temporal and spatial patterns. This presentation focuses on precipitation variability based on 20 general circulation models (GCMs) participating in the fifth coupled model intercomparison project (CMIP5). Specifically, we focus on China and provide a comprehensive evaluation of the CMIP5 models compared to historical 20th century precipitation variability from two observational precipitation products: the University of East Anglia's Climatic Research Unit (CRU) time series (TS) dataset version 3.10, and the Global Precipitation Climatology Centre (GPCC) version 6. We also reassess the performance of the third CMIP (CMIP3) to quantify potential improvements in CMIP5 over the previous generation of GCMs. Finally, we provide 21st century precipitation projections for China based on three representative concentration pathways (RCP): RCP 8.5, 4.5, and 2.6. These future precipitation projections are presented in light of the observed 20th century biases in the models. We find that CMIP5 models are able to better reproduce the general spatial pattern of observed 20th century precipitation than CMIP3. However, for China as a whole, the annual precipitation magnitude is overestimated in CMIP5, more so than in CMIP3. This smaller overestimation in CMIP3 was primarily driven by a large underestimation of summer precipitation. Spatially, overestimated precipitation magnitudes are evident for most regions of China, especially along the eastern margin of the Tibetan Plateau. Over southeastern China during summer, the precipitation amounts are underestimated. The multidecadal precipitation variability in CMIP5 is muted relative to observations, but improved when compared to CMIP3. We also assess precipitation trends and correlations relative to observations, and again find better agreement for CMIP5 than for CMIP3. Both observations and models indicated precipitation increases over parts of northwestern China, and decreases over the Tibetan Plateau throughout the 20th century. However, for the southeastern and northern regions of China there is poor agreement in precipitation trends. Precipitation is projected to increase across all of China under all the three emission scenarios during the 21st century. The largest significant trend is evident for RCP 8.5, which projects a precipitation increase of 1.5 mm/year, resulting in a 16% increase in precipitation by the end of the century. The smallest increases are projected to occur under the RCP 2.6 scenario, resulting in only a +6% change by 2100. The regions of greatest precipitation increases are the Tibetan Plateau and eastern China during summer, suggesting a potential change in the monsoonal circulation in the future.

A CMIP5 Ensemble Assessment of Climate Change Impact on Durum Wheat Production in North Dakota, USA

NASA Astrophysics Data System (ADS)

Dillon, T. D.; Kirilenko, A.

2016-12-01

North Dakota is the main US and one of the world's leading producers of durum wheat (Triticum durum), the hardest wheat variety with high protein content, used in multiple food products. We investigated potential change in durum wheat production in connection with climate change. The study accounted for variations in environmental conditions by running a dynamic wheat yield model in thirteen climatically different regions of the state. North Dakota climate is representative of highly productive agricultural lands of the Northern Great Plains, which encompass five US states and two Canadian provinces. Eastern part of North Dakota has humid continental climate while the western past is semi-desert with distinct west-to east precipitation gradient. Low mean average temperatures (cir. +4C), and high temperature variability lead to relatively short growing season (cir. 130 days). Combined with limited rainfall (cir. 350 mm in the East and 560 mm in the West), it makes agriculture highly dependent on temperature and precipitation. Accordingly, climate change has high potential impact on crop production in the region. We used the ALMANAC crop growth model to simulate the production of durum wheat. Model performance was estimated by comparison of simulated yields with historical observations; and was found satisfactory (RMSE < 1.00 T/ha*yr). To account for uncertainty in projected future climate, we used an ensemble of 17 CMIP5 GCMs run under four IPCC AR5 RCP scenarios, for two time periods characteristic of the 2040s and the 2070s. GCM output data were further downscaled using MarkSim weather generator. We found statistically significant reductions in mean yields in 96% of model runs for both time periods (t-test for independent samples; p<.05). In 2040s climate, yield decrease varied from 17% for RCP 2.6 to 45% for RCP 8.5; in 2070s climate - from 35% for RCP2.6 to 73% for RCP 8.5. Further research will concentrate on crop fail risk analysis and geographical heterogeneity of simulated changes.

Intermodel spread of the double-ITCZ bias in coupled GCMs tied to land surface temperature in AMIP GCMs

NASA Astrophysics Data System (ADS)

Zhou, Wenyu; Xie, Shang-Ping

2017-08-01

Global climate models (GCMs) have long suffered from biases of excessive tropical precipitation in the Southern Hemisphere (SH). The severity of the double-Intertropical Convergence Zone (ITCZ) bias, defined here as the interhemispheric difference in zonal mean tropical precipitation, varies strongly among models in the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble. Models with a more severe double-ITCZ bias feature warmer tropical sea surface temperature (SST) in the SH, coupled with weaker southeast trades. While previous studies focus on coupled ocean-atmosphere interactions, here we show that the intermodel spread in the severity of the double-ITCZ bias is closely related to land surface temperature biases, which can be further traced back to those in the Atmosphere Model Intercomparison Project (AMIP) simulations. By perturbing land temperature in models, we demonstrate that cooler land can indeed lead to a more severe double-ITCZ bias by inducing the above coupled SST-trade wind pattern in the tropics. The response to land temperature can be consistently explained from both the dynamic and energetic perspectives. Although this intermodel spread from the land temperature variation does not account for the ensemble model mean double-ITCZ bias, identifying the land temperature effect provides insights into simulating a realistic ITCZ for the right reasons.

Activity of convective coupled equatorial wave in tropical Tropopause layer in reanalysis and high-top CMIP5 models

NASA Astrophysics Data System (ADS)

Harza, Alia; Lubis, Sandro W.; Setiawan, Sonni

2018-05-01

The activity of convectively coupled equatorial waves (CCEWs), including Kelvin waves, Mixed Rossby-Gravity (MRG), and Equatorial Rossby (ER), in the tropical tropopause layer (TTL) is investigated in the Reanalysis and nine high-top CMIP5 models using the zonal wave number-frequency spectral analysis with equatorially symmetric-antisymmetric decomposition. We found that the TTL activities in the high-top CMIP5 models show significant difference among the high-top CMIP5 models with respect to the observation. The MIROC and HadGEM2-CC models work best in simulating Kelvin wave in the TTL, while the HadGEM2-CC and MPI-ESM-LR models work best in simulating MRG waves. The ER waves in TTL are best simulated in the MRI-CGCM model. None of the models are good in simulating all waves at once. It is concluded that the broad range of wave activity found in the different CMIP5 models depend on the convective parameterization used by each model and the representation of the tropical stratosphere variability, including the QBO.

Emulating atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 - Part 2: Applications

NASA Astrophysics Data System (ADS)

Meinshausen, M.; Wigley, T. M. L.; Raper, S. C. B.

2011-02-01

Intercomparisons of coupled atmosphere-ocean general circulation models (AOGCMs) and carbon cycle models are important for galvanizing our current scientific knowledge to project future climate. Interpreting such intercomparisons faces major challenges, not least because different models have been forced with different sets of forcing agents. Here, we show how an emulation approach with MAGICC6 can address such problems. In a companion paper (Meinshausen et al., 2011a), we show how the lower complexity carbon cycle-climate model MAGICC6 can be calibrated to emulate, with considerable accuracy, globally aggregated characteristics of these more complex models. Building on that, we examine here the Coupled Model Intercomparison Project's Phase 3 results (CMIP3). If forcing agents missed by individual AOGCMs in CMIP3 are considered, this reduces ensemble average temperature change from pre-industrial times to 2100 under SRES A1B by 0.4 °C. Differences in the results from the 1980 to 1999 base period (as reported in IPCC AR4) to 2100 are negligible, however, although there are some differences in the trajectories over the 21st century. In a second part of this study, we consider the new RCP scenarios that are to be investigated under the forthcoming CMIP5 intercomparison for the IPCC Fifth Assessment Report. For the highest scenario, RCP8.5, relative to pre-industrial levels, we project a median warming of around 4.6 °C by 2100 and more than 7 °C by 2300. For the lowest RCP scenario, RCP3-PD, the corresponding warming is around 1.5 °C by 2100, decreasing to around 1.1 °C by 2300 based on our AOGCM and carbon cycle model emulations. Implied cumulative CO2 emissions over the 21st century for RCP8.5 and RCP3-PD are 1881 GtC (1697 to 2034 GtC, 80% uncertainty range) and 381 GtC (334 to 488 GtC), when prescribing CO2 concentrations and accounting for uncertainty in the carbon cycle. Lastly, we assess the reasons why a previous MAGICC version (4.2) used in IPCC AR4 gave roughly 10% larger warmings over the 21st century compared to the CMIP3 average. We find that forcing differences and the use of slightly too high climate sensitivities inferred from idealized high-forcing runs were the major reasons for this difference.

Realism of the Indian Ocean Dipole in CMIP5 models, and the Implication for Climate Projections

NASA Astrophysics Data System (ADS)

Weller, E.; Cai, W.; Cowan, T.

2012-12-01

An assessment of how well climate models simulate the Indian Ocean Dipole (IOD) is undertaken using coupled models that have partaken in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Compared to CMIP3 models, no substantial improvement is evident in the simulation of the IOD pattern and/or amplitude during its peak season in austral spring (September-October-November, or SON). The majority of CMIP5 models generate a larger variance of sea surface temperature (SST) in the Sumatra-Java upwelling region and an IOD amplitude that is far greater than what is observed. Although the relationship between precipitation and the tropical Indian Ocean SST is well simulated, future projections of SON rainfall changes over IOD-influenced regions are intrinsically linked to the IOD-rainfall teleconnection and IOD amplitude in the model present-day climate. The diversity of the simulated IOD amplitudes in CMIP5 (and CMIP3) models which tend to be overly large, results in a wide range of future modelled SON rainfall trends over IOD-influenced regions. Our results highlight the importance of realistically simulating the present-day IOD properties and the caveat that needs to be exercised in interpreting climate projections in the IOD-affected regions.

Transient Earth system responses to cumulative carbon dioxide emissions: linearities, uncertainties, and probabilities in an observation-constrained model ensemble

NASA Astrophysics Data System (ADS)

Steinacher, M.; Joos, F.

2016-02-01

Information on the relationship between cumulative fossil CO2 emissions and multiple climate targets is essential to design emission mitigation and climate adaptation strategies. In this study, the transient response of a climate or environmental variable per trillion tonnes of CO2 emissions, termed TRE, is quantified for a set of impact-relevant climate variables and from a large set of multi-forcing scenarios extended to year 2300 towards stabilization. An ˜ 1000-member ensemble of the Bern3D-LPJ carbon-climate model is applied and model outcomes are constrained by 26 physical and biogeochemical observational data sets in a Bayesian, Monte Carlo-type framework. Uncertainties in TRE estimates include both scenario uncertainty and model response uncertainty. Cumulative fossil emissions of 1000 Gt C result in a global mean surface air temperature change of 1.9 °C (68 % confidence interval (c.i.): 1.3 to 2.7 °C), a decrease in surface ocean pH of 0.19 (0.18 to 0.22), and a steric sea level rise of 20 cm (13 to 27 cm until 2300). Linearity between cumulative emissions and transient response is high for pH and reasonably high for surface air and sea surface temperatures, but less pronounced for changes in Atlantic meridional overturning, Southern Ocean and tropical surface water saturation with respect to biogenic structures of calcium carbonate, and carbon stocks in soils. The constrained model ensemble is also applied to determine the response to a pulse-like emission and in idealized CO2-only simulations. The transient climate response is constrained, primarily by long-term ocean heat observations, to 1.7 °C (68 % c.i.: 1.3 to 2.2 °C) and the equilibrium climate sensitivity to 2.9 °C (2.0 to 4.2 °C). This is consistent with results by CMIP5 models but inconsistent with recent studies that relied on short-term air temperature data affected by natural climate variability.

Multi-model analysis in hydrological prediction

NASA Astrophysics Data System (ADS)

Lanthier, M.; Arsenault, R.; Brissette, F.

2017-12-01

Hydrologic modelling, by nature, is a simplification of the real-world hydrologic system. Therefore ensemble hydrological predictions thus obtained do not present the full range of possible streamflow outcomes, thereby producing ensembles which demonstrate errors in variance such as under-dispersion. Past studies show that lumped models used in prediction mode can return satisfactory results, especially when there is not enough information available on the watershed to run a distributed model. But all lumped models greatly simplify the complex processes of the hydrologic cycle. To generate more spread in the hydrologic ensemble predictions, multi-model ensembles have been considered. In this study, the aim is to propose and analyse a method that gives an ensemble streamflow prediction that properly represents the forecast probabilities and reduced ensemble bias. To achieve this, three simple lumped models are used to generate an ensemble. These will also be combined using multi-model averaging techniques, which generally generate a more accurate hydrogram than the best of the individual models in simulation mode. This new predictive combined hydrogram is added to the ensemble, thus creating a large ensemble which may improve the variability while also improving the ensemble mean bias. The quality of the predictions is then assessed on different periods: 2 weeks, 1 month, 3 months and 6 months using a PIT Histogram of the percentiles of the real observation volumes with respect to the volumes of the ensemble members. Initially, the models were run using historical weather data to generate synthetic flows. This worked for individual models, but not for the multi-model and for the large ensemble. Consequently, by performing data assimilation at each prediction period and thus adjusting the initial states of the models, the PIT Histogram could be constructed using the observed flows while allowing the use of the multi-model predictions. The under-dispersion has been largely corrected on short-term predictions. For the longer term, the addition of the multi-model member has been beneficial to the quality of the predictions, although it is too early to determine whether the gain is related to the addition of a member or if multi-model member has plus-value itself.

Projecting Global Decadal Change in Water Supply for Strategic Planning: Window Size Sensitivity in CMIP5 GCMs

NASA Astrophysics Data System (ADS)

Luck, M.; Landis, M.; Gassert, F.; Luo, T.; Reig, P.

2013-12-01

Climate adaptation and strategic planning by states, corporations, and long-term investors require reliable information on the range of possible climatic changes. However, most decision makers are incapable of planning over the century-scale time horizons for which global climate models (GCMs) are developed. Even the most forward-looking actors rarely consider scenarios more than several decades into the future. The mismatch in model design and practical demands poses a challenge in extracting useful information on the decadal scale from global climate change models. Here, we explore options and limitations in generating decadal water supply change projections, as evaluated for the World Resources Institute's Aqueduct project's estimates of future change in water stress. Our approach uses an ensemble of six CMIP5 GCMs, selected to represent a broad lineage of models that best reproduce the mean and standard deviation of recent streamflow records in 18 large river basins, bias corrected to GLDAS-2.0 runoff. We examine sensitivity of point estimates of climate normal supply and water supply variability (interannual and seasonal) at the years 2020, 2030, and 2040, with a focus on using temporal windows of different lengths (11-, 21-, and 31-years) to generate the point estimates. With the aim of creating practical information for non-expert audiences, we will discuss the persistent question of 'how can we balance uncertainty and usability in designing scientific data products?'

North Atlantic sub-decadal variability in climate models

NASA Astrophysics Data System (ADS)

Reintges, Annika; Martin, Thomas; Latif, Mojib; Park, Wonsun

2017-04-01

The North Atlantic Oscillation (NAO) is the dominant variability mode for the winter climate of the North Atlantic sector. During a positive (negative) NAO phase, the sea level pressure (SLP) difference between the subtropical Azores high and the subpolar Icelandic low is anomalously strong (weak). This affects, for example, temperature, precipitation, wind, and surface heat flux over the North Atlantic, and over large parts of Europe. In observations we find enhanced sub-decadal variability of the NAO index that goes along with a dipolar sea surface temperature (SST) pattern. The corresponding SLP and SST patterns are reproduced in a control experiment of the Kiel Climate Model (KCM). Large-scale air-sea interaction is suggested to be essential for the North Atlantic sub-decadal variability in the KCM. The Atlantic Meridional Overturning Circulation (AMOC) plays a key role, setting the timescale of the variability by providing a delayed negative feedback to the NAO. The interplay of the NAO and the AMOC on the sub-decadal timescale is further investigated in the CMIP5 model ensemble. For example, the average CMIP5 model AMOC pattern associated with sub-decadal variability is characterized by a deep-reaching dipolar structure, similar to the KCM's sub-decadal AMOC variability pattern. The results suggest that dynamical air-sea interactions are crucial to generate enhanced sub-decadal variability in the North Atlantic climate.

Climate Risk Management in the Anthropocene: From Basic Science to Decisionmaking and Back.

NASA Astrophysics Data System (ADS)

King, A.; Karoly, D. J.

2014-12-01

In this talk I will discuss studies our group has conducted to investigate the role of anthropogenic climate change in the heavy rains of 2010-2012 and the heat and drought of 2013. Using a range of methodologies based on coupled climate models from the CMIP5 archive and very large atmosphere-only ensembles from the Weather@Home Australia-New Zealand ensemble we have found increases in the likelihood of hot extremes, such as the summer of 2012/13 and individual record-breaking hot days within that summer. In contrast, studies of the precipitation extremes that occurred in the summer of 2011/12 found limited evidence for a substantial anthropogenic role in these events. I will also present briefly on avenues of research we are currently pursuing in the Australian community. These include investigating whether anthropogenic climate change has altered the likelihood of weather associated with bushfires and the implementation of perturbed physics in the Weather@Home ensemble to allow us to study the potential role of human-induced climate change on extreme rainfall events.

Ensemble Downscaling of Winter Seasonal Forecasts: The MRED Project

NASA Astrophysics Data System (ADS)

Arritt, R. W.; Mred Team

2010-12-01

The Multi-Regional climate model Ensemble Downscaling (MRED) project is a multi-institutional project that is producing large ensembles of downscaled winter seasonal forecasts from coupled atmosphere-ocean seasonal prediction models. Eight regional climate models each are downscaling 15-member ensembles from the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) and the new NASA seasonal forecast system based on the GEOS5 atmospheric model coupled with the MOM4 ocean model. This produces 240-member ensembles, i.e., 8 regional models x 15 global ensemble members x 2 global models, for each winter season (December-April) of 1982-2003. Results to date show that combined global-regional downscaled forecasts have greatest skill for seasonal precipitation anomalies during strong El Niño events such as 1982-83 and 1997-98. Ensemble means of area-averaged seasonal precipitation for the regional models generally track the corresponding results for the global model, though there is considerable inter-model variability amongst the regional models. For seasons and regions where area mean precipitation is accurately simulated the regional models bring added value by extracting greater spatial detail from the global forecasts, mainly due to better resolution of terrain in the regional models. Our results also emphasize that an ensemble approach is essential to realizing the added value from the combined global-regional modeling system.

Projections of Rainfall and Surface Temperature from CMIP5 Models under RCP4.5 and 8.5 over BIMSTEC Countries

NASA Astrophysics Data System (ADS)

Charan Pattnayak, Kanhu; Kar, Sarat Chandra; Kumari Pattnayak, Rashmita

2015-04-01

Rainfall and surface temperature are the most important climatic variables in the context of climate change. Thus, these variables simulated from fifth phase of the Climate Model Inter-comparison Project (CMIP5) models have been compared against Climatic Research Unit (CRU) observed data and projected for the twenty first century under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 emission scenarios. Results for the seven countries under Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) such as Bangladesh, Bhutan, India, Myanmar, Nepal, Sri Lanka and Thailand have been examined. Six CMIP5 models namely GFDL-CM3, GFDL-ESM2M, GFDL-ESM2G, HadGEM2-AO, HadGEM2-CC and HadGEM2-ES have been chosen for this study. The study period has been considered is from 1861 to 2100. From this period, initial 145 years i.e. 1861 to 2005 is reference or historical period and the later 95 years i.e. 2005 to 2100 is projected period. The climate change in the projected period has been examined with respect to the reference period. In order to validate the models, the mean annual rainfall and temperature has been compared with CRU over the reference period 1901 to 2005. Comparison reveals that most of the models are able to capture the spatial distribution of rainfall and temperature over most of the regions of BIMSTEC countries. Therefore these model data can be used to study the future changes in the 21st Century. Four out six models shows that the rainfall over Central and North India, Thailand and eastern part of Myanmar shows decreasing trend and Bangladesh, Bhutan, Nepal and Sri Lanka shows an increasing trend in both RCP 4.5 and 8.5 scenarios. In case of temperature, all of the models show an increasing trend over all the BIMSTEC countries in both scenarios, however, the rate of increase is relatively less over Sri Lanka than the other countries. Annual cycles of rainfall and temperature over Bangladesh, Myanmar and Thailand reveals that the magnitudes are more in 2070 to 2100 of RCP8.5. Inter-model comparison show that there are large more uncertainties within the CMIP5 model projections.

Temperature-salinity structure of the AMOC in high-resolution ocean simulations and in CMIP5 models

NASA Astrophysics Data System (ADS)

Wang, F.; Xu, X.; Chassignet, E.

2017-12-01

On average, the CMIP5 models represent the AMOC structure, water properties, Heat transport and Freshwater transport reasonably well. For temperature, CMIP5 models exhibit a colder northward upper limb and a warmer southward lower limb. the temperature contrast induces weaker heat transport than observation. For salinity, CMIP5 models exhibit saltier southward lower limb, thus contributes to weaker column freshwater transport. Models have large spread, among them, AMOC strength contributes to Heat transport but not freshwater transport. AMOC structure (the overturning depth) contributes to transport-weighted temperature not transport-weighted salinity in southward lower limb. The salinity contrast in upper and lower limb contributes to freshwater transport, but temperature contrast do not contribute to heat transport.

Constructing optimal ensemble projections for predictive environmental modelling in Northern Eurasia

NASA Astrophysics Data System (ADS)

Anisimov, Oleg; Kokorev, Vasily

2013-04-01

Large uncertainties in climate impact modelling are associated with the forcing climate data. This study is targeted at the evaluation of the quality of GCM-based climatic projections in the specific context of predictive environmental modelling in Northern Eurasia. To accomplish this task, we used the output from 36 CMIP5 GCMs from the IPCC AR-5 data base for the control period 1975-2005 and calculated several climatic characteristics and indexes that are most often used in the impact models, i.e. the summer warmth index, duration of the vegetation growth period, precipitation sums, dryness index, thawing degree-day sums, and the annual temperature amplitude. We used data from 744 weather stations in Russia and neighbouring countries to analyze the spatial patterns of modern climatic change and to delineate 17 large regions with coherent temperature changes in the past few decades. GSM results and observational data were averaged over the coherent regions and compared with each other. Ultimately, we evaluated the skills of individual models, ranked them in the context of regional impact modelling and identified top-end GCMs that "better than average" reproduce modern regional changes of the selected meteorological parameters and climatic indexes. Selected top-end GCMs were used to compose several ensembles, each combining results from the different number of models. Ensembles were ranked using the same algorithm and outliers eliminated. We then used data from top-end ensembles for the 2000-2100 period to construct the climatic projections that are likely to be "better than average" in predicting climatic parameters that govern the state of environment in Northern Eurasia. The ultimate conclusions of our study are the following. • High-end GCMs that demonstrate excellent skills in conventional atmospheric model intercomparison experiments are not necessarily the best in replicating climatic characteristics that govern the state of environment in Northern Eurasia, and independent model evaluation on regional level is necessary to identify "better than average" GCMs. • Each of the ensembles combining results from several "better than average" models replicate selected meteorological parameters and climatic indexes better than any single GCM. The ensemble skills are parameter-specific and depend on models it consists of. The best results are not necessarily those based on the ensemble comprised by all "better than average" models. • Comprehensive evaluation of climatic scenarios using specific criteria narrows the range of uncertainties in environmental projections.

Projections of Declining Surface-Water Availability for the Southwestern United States

NASA Technical Reports Server (NTRS)

Seager, Richard; Ting, Mingfang; Li, Cuihua; Naik, Naomi; Cook, Benjamin; Nakamura, Jennifer; Liu, Haibo

2012-01-01

16 of the CMIP5 models had all the data needed for this work for at least one simulation that was continuous from 1950 to 2040. Details of the models analyzed here are provided in Table S1. The model data analyzed here are available at http://strega.ldeo.columbia.edu:81/expert/home/.naomi/.AR5/.v2/.historical:rcp85/.mmm16/ a. Assessing the climatology of the models Despite increases in horizontal resolution of many models compared to their CMIP3 counterparts none of these models can adequately resolve the topography of the south west United States, such as the Sierra Nevada and Rocky Mountains and the associated orographic precipitation. This requires that caution be used when interpreting the results presented here. To assess the ability of the models to simulate the current hydroclimate, in Figure S1 we show the observed (from the Global Precipitation Climatology Centre gridded rain gauge data, (1)) monthly climatology of precipitation and the same for all the models and the multimodel mean for the California-Nevada, Colorado headwaters and Texas regions. The GPCC data uses rain gauges only and interpolates to regular grids of which we used the 1? by 1? one. Details of the data set can be found in (2). While the models apparently overestimate precipitation in California and Nevada the seasonal cycle with wet winters and dry summers is very well represented. It is also possible that the rain gauge observations are biased low by inadequately sampling the higher mountain regions. How ever the models might also be expected to underestimate orographic precipitation due to inadequate horizontal resolution. The 25 models are also too wet in the Colorado headwaters region but correctly represent the quite even distribution though the year. The bimodal distribution of precipitation in Texas, with peaks in May and September, and the absolute amounts, are well modeled but with the September peak too weak. The positive precipitation bias translates into a positive runoff bias for the Colorado headwaters as also shown in Figure S1. Here the observed runoff values are taken from simulations of the Variable Infiltration Capacity (VIC) land surface-hydrology model (3) forced by observed meteorology (5) that were conducted as part of the North American Land Data Assimilation System project phase 2 ( (NLDAS-2), http://www.emc.ncep.noaa.gov/mmb/nldas/. Runoff for California-Nevada is better simulated but there is a positive bias over Texas despite no strong precipitation bias. To check whether regional climate models better simulate P and runoff in these regions we analyzed the historical simulation with the Regional Climate Model version 3 driven by the National Centers for Environmental Prediction-Department of Energy Reanalysis 2 available from the North American Regional Climate Change Assessment Program (http://www.narccap.ucar.edu). This model configuration retained these biases in P and runoff although they were reduced in amplitude. Given these varying biases we plot P and P - E changes in actual values but apply the simplest bias correction possible to the runoff and soil moisture values and show the modeled changes in terms of percentages of the 20th Century model climatologies. A thorough assessment of the simulation of North American climate in CMIP5 models is conducted in Sheffield at al. (North American Climate in CMIP5 Experiments. Part I: Evaluation of 20th Century Continental and Regional Climatology, manuscript submit ted to J. Climate, available at http://www.climate.noaa.gov/index.jsp?pg=./cpo pa/ mapp/cmip5 publications.html). Sheffield et al. analyze the climatology of precipitation, surface air temperature, low level winds, moisture fluxes, runoff etc. and conclude that the main features of the hydrological cycle, including characteristics of the atmospheric moisture balance and its seasonality, are captured in the CMP5 models subject to biases in total precipitation amounts. We chose to use all available models instead of selecting some and rejecting others based on an assessment of model realism. This is in accord with the suggestions of Mote et al. for CMIP3 (4) but future work needs to revisit this matter for the case of the CMIP5 ensemble.

Pareto-optimal estimates that constrain mean California precipitation change

NASA Astrophysics Data System (ADS)

Langenbrunner, B.; Neelin, J. D.

2017-12-01

Global climate model (GCM) projections of greenhouse gas-induced precipitation change can exhibit notable uncertainty at the regional scale, particularly in regions where the mean change is small compared to internal variability. This is especially true for California, which is located in a transition zone between robust precipitation increases to the north and decreases to the south, and where GCMs from the Climate Model Intercomparison Project phase 5 (CMIP5) archive show no consensus on mean change (in either magnitude or sign) across the central and southern parts of the state. With the goal of constraining this uncertainty, we apply a multiobjective approach to a large set of subensembles (subsets of models from the full CMIP5 ensemble). These constraints are based on subensemble performance in three fields important to California precipitation: tropical Pacific sea surface temperatures, upper-level zonal winds in the midlatitude Pacific, and precipitation over the state. An evolutionary algorithm is used to sort through and identify the set of Pareto-optimal subensembles across these three measures in the historical climatology, and we use this information to constrain end-of-century California wet season precipitation change. This technique narrows the range of projections throughout the state and increases confidence in estimates of positive mean change. Furthermore, these methods complement and generalize emergent constraint approaches that aim to restrict uncertainty in end-of-century projections, and they have applications to even broader aspects of uncertainty quantification, including parameter sensitivity and model calibration.

Design and Application of a Community Land Benchmarking System for Earth System Models

NASA Astrophysics Data System (ADS)

Mu, M.; Hoffman, F. M.; Lawrence, D. M.; Riley, W. J.; Keppel-Aleks, G.; Koven, C. D.; Kluzek, E. B.; Mao, J.; Randerson, J. T.

2015-12-01

Benchmarking has been widely used to assess the ability of climate models to capture the spatial and temporal variability of observations during the historical era. For the carbon cycle and terrestrial ecosystems, the design and development of an open-source community platform has been an important goal as part of the International Land Model Benchmarking (ILAMB) project. Here we developed a new benchmarking software system that enables the user to specify the models, benchmarks, and scoring metrics, so that results can be tailored to specific model intercomparison projects. Evaluation data sets included soil and aboveground carbon stocks, fluxes of energy, carbon and water, burned area, leaf area, and climate forcing and response variables. We used this system to evaluate simulations from the 5th Phase of the Coupled Model Intercomparison Project (CMIP5) with prognostic atmospheric carbon dioxide levels over the period from 1850 to 2005 (i.e., esmHistorical simulations archived on the Earth System Grid Federation). We found that the multi-model ensemble had a high bias in incoming solar radiation across Asia, likely as a consequence of incomplete representation of aerosol effects in this region, and in South America, primarily as a consequence of a low bias in mean annual precipitation. The reduced precipitation in South America had a larger influence on gross primary production than the high bias in incoming light, and as a consequence gross primary production had a low bias relative to the observations. Although model to model variations were large, the multi-model mean had a positive bias in atmospheric carbon dioxide that has been attributed in past work to weak ocean uptake of fossil emissions. In mid latitudes of the northern hemisphere, most models overestimate latent heat fluxes in the early part of the growing season, and underestimate these fluxes in mid-summer and early fall, whereas sensible heat fluxes show the opposite trend.

High-resolution boreal winter precipitation projections over tropical America from CMIP5 models

NASA Astrophysics Data System (ADS)

Palomino-Lemus, Reiner; Córdoba-Machado, Samir; Gámiz-Fortis, Sonia Raquel; Castro-Díez, Yolanda; Esteban-Parra, María Jesús

2017-11-01

Climate-change projections for boreal winter precipitation in Tropical America has been addressed by statistical downscaling (SD) using the principal component regression with sea-level pressure (SLP) as the predictor variable. The SD model developed from the reanalysis of SLP and gridded precipitation GPCC data, has been applied to SLP outputs from 20 CGMS of CMIP5, both from the present climate (1971-2000) and for the future (2071-2100) under the RCP2.6, RCP4.5, and RCP8.5 scenarios. The SD model shows a suitable performance over large regions, presenting a strong bias only in small areas characterized by very dry climate conditions or poor data coverage. The difference in percentage between the projected SD precipitation and the simulated SD precipitation for present climate, ranges from moderate to intense changes in rainfall (positive or negative, depending on the region and the SD GCM model considered), as the radiative forcing increases from the RCP2.6 to RCP8.5. The disparity in the GCMs outputs seems to be the major source of uncertainty in the projected changes, while the scenario considered appears less decisive. Mexico and eastern Brazil are the areas showing the most coherent decreases between SD GCMs, while northwestern and southeastern South America show consistently significant increases. This coherence is corroborated by the results of the ensemble mean which projects positive changes from 10°N towards the south, with exceptions such as eastern Brazil, northern Chile and some smaller areas, such as the center of Colombia, while projected negative changes are the majority found in the northernmost part.

Coupled MODEL Intercomparison Project PHASE 5 (CMIP5) Projected Twenty-First Century Warming over Southern Africa: Role of LOCAL Feedbacks

NASA Astrophysics Data System (ADS)

Shongwe, M.

2014-12-01

The warming rates projected by an ensemble of the Coupled Model Intercomparion Project Phase 5 (CMIP5) global climate models (GCMs) over southern Africa (south of 10 degrees latitude) are investigated. In all RCPs, CMIP5 models project a higher warming rate over the southwestern parts centred around the arid Kalahari and Namib deserts. The higher warming rates over these areas outpace global warming by up to a factor 2 in some GCMs. The projected warming is associated with an increase in heat waves. There is notable consensus across the models with little intermodel spread, suggesting a strong robustness of the projections. Mechanisms underlying the enhanced warming are investigated. A positive soil moisture-temperature feedback is suggested to contribute to the accelerated temperature increase. A decrease in soil moisture is projected by the GCMs over the area of highest warming. The reduction in soil wetness reduces evapotranspiration rates over the area where evaporation is dependent on available soil moisture. The reduction is evapotranspiration affects the partitioning of turbulent energy fluxes from the soil surface into the atmosphere and translates into an increase of the Bowen ratio featuring an increase in sensible relative to latent heat flux. An increase in sensible heat flux leads to an increase in near-surface temperature. The increase in temperature leads to a higher vapour pressure deficit and evaporative demand and evapotranspiration from the dry soils, possibly leading to a further decrease in soil moisture. A precipitation-soil moisture feedback is also suggested. A decrease in mean precipitation and an increase in drought conditions are projected over the area of enhanced warming. The reduced precipitation results in drier soils. The drier soil translates to reduced evapotranspiration for cloud and rainfall formation. However, the role played by the soil moisture-precipitation feedback loop is still inconclusive and characterized by some degree of uncertainty given that the strength of the local moisture recycling has not been explicitly quantified. An alternative mechanism involving the impact of soil moisture anomalies on boundary-layer stability and precipitation formation will be investigated.

Seasonal ENSO phase locking in the Kiel Climate Model: The importance of the equatorial cold sea surface temperature bias

NASA Astrophysics Data System (ADS)

Wengel, C.; Latif, M.; Park, W.; Harlaß, J.; Bayr, T.

2018-02-01

The El Niño/Southern Oscillation (ENSO) is characterized by a seasonal phase locking, with strongest eastern and central equatorial Pacific sea surface temperature (SST) anomalies during boreal winter and weakest SST anomalies during boreal spring. In this study, key feedbacks controlling seasonal ENSO phase locking in the Kiel Climate Model (KCM) are identified by employing Bjerknes index stability analysis. A large ensemble of simulations with the KCM is analyzed, where the individual runs differ in either the number of vertical atmospheric levels or coefficients used in selected atmospheric parameterizations. All integrations use the identical ocean model. The ensemble-mean features realistic seasonal ENSO phase locking. ENSO phase locking is very sensitive to changes in the mean-state realized by the modifications described above. An excessive equatorial cold tongue leads to weak phase locking by reducing the Ekman feedback and thermocline feedback in late boreal fall and early boreal winter. Seasonal ENSO phase locking also is sensitive to the shortwave feedback as part of the thermal damping in early boreal spring, which strongly depends on eastern and central equatorial Pacific SST. The results obtained from the KCM are consistent with those from models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5).

Future projections of synoptic weather types over the Arabian Peninsula during the twenty-first century using an ensemble of CMIP5 models

NASA Astrophysics Data System (ADS)

El Kenawy, Ahmed M.; McCabe, Matthew F.

2017-10-01

An assessment of future change in synoptic conditions over the Arabian Peninsula throughout the twenty-first century was performed using 20 climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) database. We employed the mean sea level pressure (SLP) data from model output together with NCEP/NCAR reanalysis data and compared the relevant circulation types produced by the Lamb classification scheme for the base period 1975-2000. Overall, model results illustrated good agreement with the reanalysis, albeit with a tendency to underestimate cyclonic (C) and southeasterly (SE) patterns and to overestimate anticyclones and directional flows. We also investigated future projections for each circulation-type during the rainy season (December-May) using three Representative Concentration Pathways (RCPs), comprising RCP2.6, RCP4.5, and RCP8.5. Overall, two scenarios (RCP4.5 and RCP 8.5) revealed a statistically significant increase in weather types favoring above normal rainfall in the region (e.g., C and E-types). In contrast, weather types associated with lower amounts of rainfall (e.g., anticyclones) are projected to decrease in winter but increase in spring. For all scenarios, there was consistent agreement on the sign of change (i.e., positive/negative) for the most frequent patterns (e.g., C, SE, E and A-types), whereas the sign was uncertain for less recurrent types (e.g., N, NW, SE, and W). The projected changes in weather type frequencies in the region can be viewed not only as indicators of change in rainfall response but may also be used to inform impact studies pertinent to water resource planning and management, extreme weather analysis, and agricultural production.

Projections of Rainfall and Temperature from CMIP5 Models over BIMSTEC Countries

NASA Astrophysics Data System (ADS)

Pattnayak, K. C.; Kar, S. C.; Ragi, A. R.

2014-12-01

Rainfall and surface temperature are the most important climatic variables in the context of climate change. Thus, these variables simulated from fifth phase of the Climate Model Inter-comparison Project (CMIP5) models have been compared against Climatic Research Unit (CRU) observed data and projected for the twenty first century under the Representative Concentration Pathways (RCPs) 4.5 and 8.5 emission scenarios. Results for the seven countries under Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) such as Bangladesh, Bhutan, India, Myanmar, Nepal, Sri Lanka and Thailand have been examined. Six CMIP5 models namely GFDL-CM3, GFDL-ESM2M, GFDL-ESM2G, HadGEM2-AO, HadGEM2-CC and HadGEM2-ES have been chosen for this study. The study period has been considered is from 1861 to 2100. From this period, initial 145 years i.e. 1861 to 2005 is reference or historical period and the later 95 years i.e. 2005 to 2100 is projected period. The climate change in the projected period has been examined with respect to the reference period. In order to validate the models, the mean annual rainfall and temperature has been compared with CRU over the reference period 1901 to 2005. Comparison reveals that most of the models are able to capture the spatial distribution of rainfall and temperature over most of the regions of BIMSTEC countries. Therefore these model data can be used to study the future changes in the 21st Century. Four out six models shows that the rainfall over Central and North India, Thailand and eastern part of Myanmar shows decreasing trend and Bangladesh, Bhutan, Nepal and Sri Lanka shows an increasing trend in both RCP 4.5 and 8.5 scenarios. In case of temperature, all of the models show an increasing trend over all the BIMSTEC countries in both scenarios, however, the rate of increase is relatively less over Sri Lanka than the other countries. Annual cycles of rainfall and temperature over Bangladesh, Myanmar and Thailand reveals that the magnitudes are more in 2070 to 2100 of RCP8.5. Inter-model comparison show that there are large more uncertainties within the CMIP5 model projections.

Major collapse of the South Pacific Convergence Zone in the future and its consequences on tropical cyclones.

NASA Astrophysics Data System (ADS)

Menkes, C.; Dutheil, C.; Bador, M.; Lengaigne, M.; Lefèvre, J.; Jourdain, N.; Jullien, S.; Vialard, J.; Peltier, A.

2017-12-01

The South Pacific convergence zone (SPCZ) is poorly represented in global climate models, with trademark biases such as the "double ITCZ" or related cold tongue biases. Such biases decrease our confidence in climate change projections for this region. We first show that WRF atmospheric simulations using a 1°x1° regional configuration capture the SPCZ mean state and interannual variability well over the 1980-2016 period. We then perform climate change experiments by adding the RCP8.5, 2080-2100 CMIP5 multi-model mean boundary anomalies to the present conditions. We find a 4° equatorward shift of the SPCZ west of 170°W, and associated 40% rainfall reduction in the southwestern Pacific. These results strongly contrast with previous studies based on CMIP5 simulations that suggest a much weaker southwestern Pacific drying ( 7%). Regional sensitivity experiments show that this CMIP5 weak response can be tracked to climatological CMIP5 SST biases, which weaken the humidity transport reduction in the south Pacific in the future and result in an underestimated projected drying of the southwest Pacific. Our experiments also point toward a large increase in the future SPCZ variability with increased frequency of zonal SPCZ events. Next we explore the fate of tropical cyclones by downscaling the previous models to 20 km. The seasonal and spatial distribution of TC genesis and occurrence are in good agreement with the observations albeit with weaker than observed annual numbers. Our reference simulation is able to simulate intense category 4 and 5 TCs. In the future climate, we show a drastic decrease in the number of TCs ( 75%), and the disappearance of the most intense (Cat 4-5) cyclones. Additional regional simulations using a completely different set of physical parameterizations yield very similar results for the SPCZ collapse, including the strong decrease of TC numbers ( 63%), underlining the robustness of our results.

Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mitchell, Daniel; AchutaRao, Krishna; Allen, Myles

The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 °C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the presentmore » day in worlds that are 1.5 and 2.0 °C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments under low-emission scenarios.Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006–2015), the second two being estimates of a similar decade but under 1.5 and 2 °C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 °C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 °C scenario.« less

Half a degree additional warming, prognosis and projected impacts (HAPPI): background and experimental design

DOE PAGES

Mitchell, Daniel; AchutaRao, Krishna; Allen, Myles; ...

2017-02-08

The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 °C above pre-industrial levels and on related global greenhouse-gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the half a degree additional warming, projections, prognosis and impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the presentmore » day in worlds that are 1.5 and 2.0 °C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of impact models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments under low-emission scenarios.Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slices are proposed, each a decade in length: the first being the most recent observed 10-year period (2006–2015), the second two being estimates of a similar decade but under 1.5 and 2 °C conditions a century in the future. We use the representative concentration pathway 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 °C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 °C scenario.« less

Analyses of Projected Changes in Climate for Sub-Saharan Africa Using a Variable-Resolution Atmospheric Model

NASA Astrophysics Data System (ADS)

Adegoke, J.; Engelbrecht, F.; Vezhapparambu, S.

2012-12-01

The conformal-cubic atmospheric model (CCAM) is employed in this study as a flexible downscaling tool at the climate-change time scale. In the downscaling procedure, the sea-ice and bias-corrected SSTs of 6 CGCMs (CSIRO Mk 3.5, GFDL2.1, GFDL2.0, HadCM2, ECHAM5 and Miroc-Medres) from AR4 of the IPCC were first used as lower-boundary forcing in CCAM simulations performed at a quasi-uniform resolution (about 200 km in the horizontal), which were subsequently downscaled to a resolution of about 60 km over southern and tropical Africa. All the simulations were for the A2 scenario of the Special Report on Emission Scenarios (SRES), and for the period 1961-2100. The SST biases were derived by comparing the simulated and observed present-day climatol¬ogy of SSTs for 1979-1999 for each month of the year; the same monthly bias corrections were applied for the duration of the simulations. CCAM ensemble projected change in annual average temperature and Rainfall for 2071-2100 vs 1961-1990 for tropical Africa will be presented and discussed. In summary, a robust signal of drastic increases in surface temperature (more than 3 degrees C for the period 2071-2100 relative to 1961-1990) is projected across the domain. Temperature increases as large as 5 degrees C are projected over the subtropical regions in the north of the domain. Increase in rainfall over tropical Africa (for the period 2071-2100 relative to 1961-1990) is projected across the domain. This is consistent with an increase in moisture in a generally warmer atmosphere. There is a robust signal of drying along the West African coast - however, the CMIP3 CGCM projections indicate a wide range of possible rainfall futures over this region The projections of East Africa becoming wetter is robust across the CCAM ensemble, consistent with the CGCM projections of CMIP3 and AR4.

South Asian monsoon precipitation in CMIP 5: a link between inter-model spread and the representations of tropical convection

DOE PAGES

Hagos, Samson; Leung, L. Ruby; Ashfaq, Moetasim; ...

2018-03-20

CMIP 5 models exhibit a mean dry bias and a large inter-model spread in simulating South Asian monsoon precipitation but the origins of the bias and spread are not well understood. Using moisture and energy budget analysis that exploits the weak temperature gradients in the tropics, we derived a non-linear relationship between the normalized precipitation and normalized precipitable water that is similar to the non-linear relationship between precipitation and precipitable water found in previous observational studies. About half of the 21 models analyzed fall in the steep gradient of the non-linear relationship where small differences in the normalized precipitable watermore » in the equatorial Indian Ocean (EIO) manifest in large differences in normalized precipitation in the region. Models with larger normalized precipitable water in the EIO during spring contribute disproportionately to the large inter-model spread and multi-model mean dry bias in monsoon precipitation through perturbations of the large-scale winds. Thus the intermodel spread in precipitable water over EIO leads to the dry bias in the multi-model mean South Asian monsoon precipitation. The models with high normalized precipitable water over EIO also project larger response to warming and dominate the inter-model spread in the multi-model projections of monsoon rainfall. Conversely, models on the flat side of the relationship between normalized precipitation and precipitable water are in better agreement with each other and with observations. On average these models project a smaller increase in the projected monsoon precipitation than that from multi-model mean. As a result, this study identified the normalized precipitable water over EIO, which is determined by the relationship between the profiles of convergence and moisture and therefore is an essential outcome of the treatment of convection, as a key metric for understanding model biases and differentiating model skill in simulating South Asian monsoon precipitation.« less

South Asian monsoon precipitation in CMIP 5: a link between inter-model spread and the representations of tropical convection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hagos, Samson; Leung, L. Ruby; Ashfaq, Moetasim

CMIP 5 models exhibit a mean dry bias and a large inter-model spread in simulating South Asian monsoon precipitation but the origins of the bias and spread are not well understood. Using moisture and energy budget analysis that exploits the weak temperature gradients in the tropics, we derived a non-linear relationship between the normalized precipitation and normalized precipitable water that is similar to the non-linear relationship between precipitation and precipitable water found in previous observational studies. About half of the 21 models analyzed fall in the steep gradient of the non-linear relationship where small differences in the normalized precipitable watermore » in the equatorial Indian Ocean (EIO) manifest in large differences in normalized precipitation in the region. Models with larger normalized precipitable water in the EIO during spring contribute disproportionately to the large inter-model spread and multi-model mean dry bias in monsoon precipitation through perturbations of the large-scale winds. Thus the intermodel spread in precipitable water over EIO leads to the dry bias in the multi-model mean South Asian monsoon precipitation. The models with high normalized precipitable water over EIO also project larger response to warming and dominate the inter-model spread in the multi-model projections of monsoon rainfall. Conversely, models on the flat side of the relationship between normalized precipitation and precipitable water are in better agreement with each other and with observations. On average these models project a smaller increase in the projected monsoon precipitation than that from multi-model mean. As a result, this study identified the normalized precipitable water over EIO, which is determined by the relationship between the profiles of convergence and moisture and therefore is an essential outcome of the treatment of convection, as a key metric for understanding model biases and differentiating model skill in simulating South Asian monsoon precipitation.« less

An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index

NASA Astrophysics Data System (ADS)

Ali, Mumtaz; Deo, Ravinesh C.; Downs, Nathan J.; Maraseni, Tek

2018-07-01

Forecasting drought by means of the World Meteorological Organization-approved Standardized Precipitation Index (SPI) is considered to be a fundamental task to support socio-economic initiatives and effectively mitigating the climate-risk. This study aims to develop a robust drought modelling strategy to forecast multi-scalar SPI in drought-rich regions of Pakistan where statistically significant lagged combinations of antecedent SPI are used to forecast future SPI. With ensemble-Adaptive Neuro Fuzzy Inference System ('ensemble-ANFIS') executed via a 10-fold cross-validation procedure, a model is constructed by randomly partitioned input-target data. Resulting in 10-member ensemble-ANFIS outputs, judged by mean square error and correlation coefficient in the training period, the optimal forecasts are attained by the averaged simulations, and the model is benchmarked with M5 Model Tree and Minimax Probability Machine Regression (MPMR). The results show the proposed ensemble-ANFIS model's preciseness was notably better (in terms of the root mean square and mean absolute error including the Willmott's, Nash-Sutcliffe and Legates McCabe's index) for the 6- and 12- month compared to the 3-month forecasts as verified by the largest error proportions that registered in smallest error band. Applying 10-member simulations, ensemble-ANFIS model was validated for its ability to forecast severity (S), duration (D) and intensity (I) of drought (including the error bound). This enabled uncertainty between multi-models to be rationalized more efficiently, leading to a reduction in forecast error caused by stochasticity in drought behaviours. Through cross-validations at diverse sites, a geographic signature in modelled uncertainties was also calculated. Considering the superiority of ensemble-ANFIS approach and its ability to generate uncertainty-based information, the study advocates the versatility of a multi-model approach for drought-risk forecasting and its prime importance for estimating drought properties over confidence intervals to generate better information for strategic decision-making.

Solar forcing for CMIP6 (v3.2)

NASA Astrophysics Data System (ADS)

Matthes, Katja; Funke, Bernd; Andersson, Monika E.; Barnard, Luke; Beer, Jürg; Charbonneau, Paul; Clilverd, Mark A.; Dudok de Wit, Thierry; Haberreiter, Margit; Hendry, Aaron; Jackman, Charles H.; Kretzschmar, Matthieu; Kruschke, Tim; Kunze, Markus; Langematz, Ulrike; Marsh, Daniel R.; Maycock, Amanda C.; Misios, Stergios; Rodger, Craig J.; Scaife, Adam A.; Seppälä, Annika; Shangguan, Ming; Sinnhuber, Miriam; Tourpali, Kleareti; Usoskin, Ilya; van de Kamp, Max; Verronen, Pekka T.; Versick, Stefan

2017-06-01

This paper describes the recommended solar forcing dataset for CMIP6 and highlights changes with respect to CMIP5. The solar forcing is provided for radiative properties, namely total solar irradiance (TSI), solar spectral irradiance (SSI), and the F10.7 index as well as particle forcing, including geomagnetic indices Ap and Kp, and ionization rates to account for effects of solar protons, electrons, and galactic cosmic rays. This is the first time that a recommendation for solar-driven particle forcing has been provided for a CMIP exercise. The solar forcing datasets are provided at daily and monthly resolution separately for the CMIP6 preindustrial control, historical (1850-2014), and future (2015-2300) simulations. For the preindustrial control simulation, both constant and time-varying solar forcing components are provided, with the latter including variability on 11-year and shorter timescales but no long-term changes. For the future, we provide a realistic scenario of what solar behavior could be, as well as an additional extreme Maunder-minimum-like sensitivity scenario. This paper describes the forcing datasets and also provides detailed recommendations as to their implementation in current climate models.For the historical simulations, the TSI and SSI time series are defined as the average of two solar irradiance models that are adapted to CMIP6 needs: an empirical one (NRLTSI2-NRLSSI2) and a semi-empirical one (SATIRE). A new and lower TSI value is recommended: the contemporary solar-cycle average is now 1361.0 W m-2. The slight negative trend in TSI over the three most recent solar cycles in the CMIP6 dataset leads to only a small global radiative forcing of -0.04 W m-2. In the 200-400 nm wavelength range, which is important for ozone photochemistry, the CMIP6 solar forcing dataset shows a larger solar-cycle variability contribution to TSI than in CMIP5 (50 % compared to 35 %).We compare the climatic effects of the CMIP6 solar forcing dataset to its CMIP5 predecessor by using time-slice experiments of two chemistry-climate models and a reference radiative transfer model. The differences in the long-term mean SSI in the CMIP6 dataset, compared to CMIP5, impact on climatological stratospheric conditions (lower shortwave heating rates of -0.35 K day-1 at the stratopause), cooler stratospheric temperatures (-1.5 K in the upper stratosphere), lower ozone abundances in the lower stratosphere (-3 %), and higher ozone abundances (+1.5 % in the upper stratosphere and lower mesosphere). Between the maximum and minimum phases of the 11-year solar cycle, there is an increase in shortwave heating rates (+0.2 K day-1 at the stratopause), temperatures ( ˜ 1 K at the stratopause), and ozone (+2.5 % in the upper stratosphere) in the tropical upper stratosphere using the CMIP6 forcing dataset. This solar-cycle response is slightly larger, but not statistically significantly different from that for the CMIP5 forcing dataset.CMIP6 models with a well-resolved shortwave radiation scheme are encouraged to prescribe SSI changes and include solar-induced stratospheric ozone variations, in order to better represent solar climate variability compared to models that only prescribe TSI and/or exclude the solar-ozone response. We show that monthly-mean solar-induced ozone variations are implicitly included in the SPARC/CCMI CMIP6 Ozone Database for historical simulations, which is derived from transient chemistry-climate model simulations and has been developed for climate models that do not calculate ozone interactively. CMIP6 models without chemistry that perform a preindustrial control simulation with time-varying solar forcing will need to use a modified version of the SPARC/CCMI Ozone Database that includes solar variability. CMIP6 models with interactive chemistry are also encouraged to use the particle forcing datasets, which will allow the potential long-term effects of particles to be addressed for the first time. The consideration of particle forcing has been shown to significantly improve the representation of reactive nitrogen and ozone variability in the polar middle atmosphere, eventually resulting in further improvements in the representation of solar climate variability in global models.

Assessment of NASA GISS CMIP5 and Post-CMIP5 Simulated Clouds and TOA Radiation Budgets Using Satellite Observations

NASA Astrophysics Data System (ADS)

Stanfield, R. E.; Dong, X.; Xi, B.; Kennedy, A. D.; Del Genio, A. D.; Minnis, P.; Loeb, N. G.; Doelling, D.

2013-05-01

Marine Boundary Layer (MBL) Clouds are an extremely important part of the climate system. Their treatment in climate models is a large source of uncertainty that will harm future projection of the Earth's climate. Zhang et al. (2005, CMIP3) compared the GCMs simulated cloud fractions (CF) with NASA CERES and ISCCP results and found that most GCMs underestimated mid-latitude MBL clouds but overestimated their optical depth. The underestimated CF and overestimated cloud optical thickness in the models offset each other when calculating TOA radiation budgets. Recent studies (Jiang et al. 2012; Stanfield et al. 2013; and Dolinar et al. 2013) have found there has not been much improvement from CMIP3 to CMIP5 for MBL clouds. Most GCMs still simulate fewer mid-latitude MBL clouds. In this study, we compare the NASA GISS CMIP5 and Post-CMIP5 results with NASA CERES cloud properties (SYN1deg) and TOA radiation budgets (EBAF), as well as CloudSat-CALIPSO cloud products. Special attention has been paid over the Southern mid-latitudes (~ 30-60 °S) where the total cloud fractions can reach up to 80-90% with MBL clouds being the dominant cloud type. Comparisons have shown that the globally averaged total CFs and TOA radiation budgets from CMIP5 agreed well with satellite observations, however, there are significant regional differences. For example, most CMIP5 models underestimated MBL clouds over the Southern mid-latitudes, including the GISS GCM, resulting in less reflected (or more absorbed) shortwave flux at TOA. The preliminary results from NASA GISS post-CMIP5 have made many improvements, and agree much better with satellite observations. These improvements are attributed to a new PBL parameterization, where more/less clouds can be simulated when the PBL gets deeper/shallower. This update has a large effect on radiation and clouds.

Future Changes to ENSO Temperature and Precipitation Teleconnections Under Warming

NASA Astrophysics Data System (ADS)

Perry, S.; McGregor, S.; Sen Gupta, A.; England, M. H.

2016-12-01

As the dominant mode of interannual climate variability, the El Niño-Southern Oscillation (ENSO) modulates temperature and rainfall globally, additionally contributing to weather extremes. Anthropogenic climate change has the potential to alter the strength and frequency of ENSO and may also alter ENSO-driven atmospheric teleconnections, affecting ecosystems and human activity in regions far removed from the tropical Pacific. State-of-art climate models exhibit considerable disagreement in projections of future changes in ENSO sea surface temperature variability. Despite this uncertainty, recent model studies suggest that the precipitation response to ENSO will be enhanced in the tropical Pacific under future warming, and as such the societal impacts of ENSO will increase. Here we use temperature and precipitation data from an ensemble of 41 CMIP5 models to show where ENSO teleconnections are being enhanced and dampened in a high-emission future scenario (RCP8.5) focusing on the changes that are occurring over land areas globally. Although there is some spread between the model projections, robust changes with strong ensemble agreement are found in certain locations, including amplification of teleconnections in southeast Australia, South America and the Maritime Continent. Our results suggest that in these regions future ENSO events will lead to more extreme temperature and rainfall responses.

Southern Hemisphere extratropical circulation: Recent trends and natural variability

NASA Astrophysics Data System (ADS)

Thomas, Jordan L.; Waugh, Darryn W.; Gnanadesikan, Anand

2015-07-01

Changes in the Southern Annular Mode (SAM), Southern Hemisphere (SH) westerly jet location, and magnitude are linked with changes in ocean circulation along with ocean heat and carbon uptake. Recent trends have been observed in these fields but not much is known about the natural variability. Here we aim to quantify the natural variability of the SH extratropical circulation by using Coupled Model Intercomparison Project Phase 5 (CMIP5) preindustrial control model runs and compare with the observed trends in SAM, jet magnitude, and jet location. We show that trends in SAM are due partly to external forcing but are not outside the natural variability as described by these models. Trends in jet location and magnitude, however, lie outside the unforced natural variability but can be explained by a combination of natural variability and the ensemble mean forced trend. These results indicate that trends in these three diagnostics cannot be used interchangeably.

The Poleward Shift of Storm Tracks Under Climate Change: Tracking Cyclones in CMIP5

NASA Astrophysics Data System (ADS)

Kaspi, Y.; Tamarin, T.

2017-12-01

Extratropical cyclones dominate the distribution of precipitation and wind in the midlatitudes, and therefore their frequency, intensity, and paths have a significant effect on weather and climate. Comprehensive climate models forced by enhanced greenhouse gas emissions suggest that under a climate change scenario, the latitudinal band of storm tracks would shift poleward. While the poleward shift is a robust response across most models, there is currently no consensus on what is the dominant dynamical mechanism. Here we use a Lagrangian approach to study the poleward shift, by employing a storm-tracking algorithm on an ensemble of CMIP5 models forced by increased CO2 emissions. We demonstrate that in addition to a poleward shift in the latitude of storm genesis, associated with the expansion of the Hadley cell, the averaged cyclonic storm also propagates more poleward until it reaches its maximum intensity. A mechanism for enhanced poleward motion of cyclones in a warmer climate is proposed, supported by idealized global warming experiments, and relates the shift to changes in upper level jet and atmospheric water vapour content. Our results imply that under the RCP8.5 climate change scenario, the averaged latitude of peak cyclone intensity shifts poleward by about 1.2○ (1.0○) in the Atlantic (Pacific) storm track in the Northern Hemisphere (NH), and by about 1.6○ in the Southern Hemisphere (SH) storm track. These changes in cyclone tracks can have a significant impact on midlatitude climate.

Weather and seasonal climate prediction for South America using a multi-model superensemble

NASA Astrophysics Data System (ADS)

Chaves, Rosane R.; Ross, Robert S.; Krishnamurti, T. N.

2005-11-01

This work examines the feasibility of weather and seasonal climate predictions for South America using the multi-model synthetic superensemble approach for climate, and the multi-model conventional superensemble approach for numerical weather prediction, both developed at Florida State University (FSU). The effect on seasonal climate forecasts of the number of models used in the synthetic superensemble is investigated. It is shown that the synthetic superensemble approach for climate and the conventional superensemble approach for numerical weather prediction can reduce the errors over South America in seasonal climate prediction and numerical weather prediction.For climate prediction, a suite of 13 models is used. The forecast lead-time is 1 month for the climate forecasts, which consist of precipitation and surface temperature forecasts. The multi-model ensemble is comprised of four versions of the FSU-Coupled Ocean-Atmosphere Model, seven models from the Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction (DEMETER), a version of the Community Climate Model (CCM3), and a version of the predictive Ocean Atmosphere Model for Australia (POAMA). The results show that conditions over South America are appropriately simulated by the Florida State University Synthetic Superensemble (FSUSSE) in comparison to observations and that the skill of this approach increases with the use of additional models in the ensemble. When compared to observations, the forecasts are generally better than those from both a single climate model and the multi-model ensemble mean, for the variables tested in this study.For numerical weather prediction, the conventional Florida State University Superensemble (FSUSE) is used to predict the mass and motion fields over South America. Predictions of mean sea level pressure, 500 hPa geopotential height, and 850 hPa wind are made with a multi-model superensemble comprised of six global models for the period January, February, and December of 2000. The six global models are from the following forecast centers: FSU, Bureau of Meteorology Research Center (BMRC), Japan Meteorological Agency (JMA), National Centers for Environmental Prediction (NCEP), Naval Research Laboratory (NRL), and Recherche en Prevision Numerique (RPN). Predictions of precipitation are made for the period January, February, and December of 2001 with a multi-analysis-multi-model superensemble where, in addition to the six forecast models just mentioned, five additional versions of the FSU model are used in the ensemble, each with a different initialization (analysis) based on different physical initialization procedures. On the basis of observations, the results show that the FSUSE provides the best forecasts of the mass and motion field variables to forecast day 5, when compared to both the models comprising the ensemble and the multi-model ensemble mean during the wet season of December-February over South America. Individual case studies show that the FSUSE provides excellent predictions of rainfall for particular synoptic events to forecast day 3. Copyright

Systematic errors in Monsoon simulation: importance of the equatorial Indian Ocean processes

NASA Astrophysics Data System (ADS)

Annamalai, H.; Taguchi, B.; McCreary, J. P., Jr.; Nagura, M.; Miyama, T.

2015-12-01

H. Annamalai1, B. Taguchi2, J.P. McCreary1, J. Hafner1, M. Nagura2, and T. Miyama2 International Pacific Research Center, University of Hawaii, USA Application Laboratory, JAMSTEC, Japan In climate models, simulating the monsoon precipitation climatology remains a grand challenge. Compared to CMIP3, the multi-model-mean (MMM) errors for Asian-Australian monsoon (AAM) precipitation climatology in CMIP5, relative to GPCP observations, have shown little improvement. One of the implications is that uncertainties in the future projections of time-mean changes to AAM rainfall may not have reduced from CMIP3 to CMIP5. Despite dedicated efforts by the modeling community, the progress in monsoon modeling is rather slow. This leads us to wonder: Has the scientific community reached a "plateau" in modeling mean monsoon precipitation? Our focus here is to better understanding of the coupled air-sea interactions, and moist processes that govern the precipitation characteristics over the tropical Indian Ocean where large-scale errors persist. A series idealized coupled model experiments are performed to test the hypothesis that errors in the coupled processes along the equatorial Indian Ocean during inter-monsoon seasons could potentially influence systematic errors during the monsoon season. Moist static energy budget diagnostics has been performed to identify the leading moist and radiative processes that account for the large-scale errors in the simulated precipitation. As a way forward, we propose three coordinated efforts, and they are: (i) idealized coupled model experiments; (ii) process-based diagnostics and (iii) direct observations to constrain model physics. We will argue that a systematic and coordinated approach in the identification of the various interactive processes that shape the precipitation basic state needs to be carried out, and high-quality observations over the data sparse monsoon region are needed to validate models and further improve model physics.

Probable Maximum Precipitation in the U.S. Pacific Northwest in a Changing Climate

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Xiaodong; Hossain, Faisal; Leung, Lai-Yung

2017-12-22

The safety of large and aging water infrastructures is gaining attention in water management given the accelerated rate of change in landscape, climate and society. In current engineering practice, such safety is ensured by the design of infrastructure for the Probable Maximum Precipitation (PMP). Recently, several physics-based numerical modeling approaches have been proposed to modernize the conventional and ad hoc PMP estimation approach. However, the underlying physics has not been investigated and thus differing PMP estimates are obtained without clarity on their interpretation. In this study, we present a hybrid approach that takes advantage of both traditional engineering wisdom andmore » modern climate science to estimate PMP for current and future climate conditions. The traditional PMP approach is improved and applied to outputs from an ensemble of five CMIP5 models. This hybrid approach is applied in the Pacific Northwest (PNW) to produce ensemble PMP estimation for the historical (1970-2016) and future (2050-2099) time periods. The new historical PMP estimates are verified by comparing them with the traditional estimates. PMP in the PNW will increase by 50% of the current level by 2099 under the RCP8.5 scenario. Most of the increase is caused by warming, which mainly affects moisture availability, with minor contributions from changes in storm efficiency in the future. Moist track change tends to reduce the future PMP. Compared with extreme precipitation, ensemble PMP exhibits higher internal variation. Thus high-quality data of both precipitation and related meteorological fields (temperature, wind fields) are required to reduce uncertainties in the ensemble PMP estimates.« less

Technical note: 3-hourly temporal downscaling of monthly global terrestrial biosphere model net ecosystem exchange

DOE PAGES

Fisher, Joshua B.; Sikka, Munish; Huntzinger, Deborah N.; ...

2016-07-29

Here, the land surface provides a boundary condition to atmospheric forward and flux inversion models. These models require prior estimates of CO 2 fluxes at relatively high temporal resolutions (e.g., 3-hourly) because of the high frequency of atmospheric mixing and wind heterogeneity. However, land surface model CO 2 fluxes are often provided at monthly time steps, typically because the land surface modeling community focuses more on time steps associated with plant phenology (e.g., seasonal) than on sub-daily phenomena. Here, we describe a new dataset created from 15 global land surface models and 4 ensemble products in the Multi-scale Synthesis andmore » Terrestrial Model Intercomparison Project (MsTMIP), temporally downscaled from monthly to 3-hourly output. We provide 3-hourly output for each individual model over 7 years (2004–2010), as well as an ensemble mean, a weighted ensemble mean, and the multi-model standard deviation. Output is provided in three different spatial resolutions for user preferences: 0.5° × 0.5°, 2.0° × 2.5°, and 4.0° × 5.0° (latitude × longitude).« less

A consistent prescription of stratospheric aerosol for both radiation and chemistry in the Community Earth System Model (CESM1)

DOE PAGES

Neely, III, Ryan Reynolds; Conley, Andrew J.; Vitt, Francis; ...

2016-07-25

Here we describe an updated parameterization for prescribing stratospheric aerosol in the National Center for Atmospheric Research (NCAR) Community Earth System Model (CESM1). The need for a new parameterization is motivated by the poor response of the CESM1 (formerly referred to as the Community Climate System Model, version 4, CCSM4) simulations contributed to the Coupled Model Intercomparison Project 5 (CMIP5) to colossal volcanic perturbations to the stratospheric aerosol layer (such as the 1991 Pinatubo eruption or the 1883 Krakatau eruption) in comparison to observations. In particular, the scheme used in the CMIP5 simulations by CESM1 simulated a global mean surface temperature decreasemore » that was inconsistent with the GISS Surface Temperature Analysis (GISTEMP), NOAA's National Climatic Data Center, and the Hadley Centre of the UK Met Office (HADCRUT4). The new parameterization takes advantage of recent improvements in historical stratospheric aerosol databases to allow for variations in both the mass loading and size of the prescribed aerosol. An ensemble of simulations utilizing the old and new schemes shows CESM1's improved response to the 1991 Pinatubo eruption. Most significantly, the new scheme more accurately simulates the temperature response of the stratosphere due to local aerosol heating. Here, results also indicate that the new scheme decreases the global mean temperature response to the 1991 Pinatubo eruption by half of the observed temperature change, and modelled climate variability precludes statements as to the significance of this change.« less

Climate model biases and statistical downscaling for application in hydrologic model

USDA-ARS?s Scientific Manuscript database

Climate change impact studies use global climate model (GCM) simulations to define future temperature and precipitation. The best available bias-corrected GCM output was obtained from Coupled Model Intercomparison Project phase 5 (CMIP5). CMIP5 data (temperature and precipitation) are available in d...

An assessment of historical Antarctic precipitation and temperature trend using CMIP5 models and reanalysis datasets

NASA Astrophysics Data System (ADS)

Tang, Malcolm S. Y.; Chenoli, Sheeba Nettukandy; Samah, Azizan Abu; Hai, Ooi See

2018-03-01

The study of Antarctic precipitation has attracted a lot of attention recently. The reliability of climate models in simulating Antarctic precipitation, however, is still debatable. This work assess the precipitation and surface air temperature (SAT) of Antarctica (90 oS to 60 oS) using 49 Coupled Model Intercomparison Project phase 5 (CMIP5) global climate models and the European Centre for Medium-range Weather Forecasts "Interim" reanalysis (ERA-Interim); the National Centers for Environmental Prediction Climate Forecast System Reanalysis (CFSR); the Japan Meteorological Agency 55-year Reanalysis (JRA-55); and the Modern Era Retrospective-analysis for Research and Applications (MERRA) datasets for 1979-2005 (27 years). For precipitation, the time series show that the MERRA and JRA-55 have significantly increased from 1979 to 2005, while the ERA-Int and CFSR have insignificant changes. The reanalyses also have low correlation with one another (generally less than +0.69). 37 CMIP5 models show increasing trend, 18 of which are significant. The resulting CMIP5 MMM also has a significant increasing trend of 0.29 ± 0.06 mm year-1. For SAT, the reanalyses show insignificant changes and have high correlation with one another, while the CMIP5 MMM shows a significant increasing trend. Nonetheless, the variability of precipitation and SAT of MMM could affect the significance of its trend. One of the many reasons for the large differences of precipitation is the CMIP5 models' resolution.

The Madden-Julian Oscillation in the NCAR Community Earth System Model Coupled Data Assimilation System

NASA Astrophysics Data System (ADS)

Chatterjee, A.; Anderson, J. L.; Moncrieff, M.; Collins, N.; Danabasoglu, G.; Hoar, T.; Karspeck, A. R.; Neale, R. B.; Raeder, K.; Tribbia, J. J.

2014-12-01

We present a quantitative evaluation of the simulated MJO in analyses produced with a coupled data assimilation (CDA) framework developed at the National Center for Atmosphere Research. This system is based on the Community Earth System Model (CESM; previously known as the Community Climate System Model -CCSM) interfaced to a community facility for ensemble data assimilation (Data Assimilation Research Testbed - DART). The system (multi-component CDA) assimilates data into each of the respective ocean/atmosphere/land model components during the assimilation step followed by an exchange of information between the model components during the forecast step. Note that this is an advancement over many existing prototypes of coupled data assimilation systems, which typically assimilate observations only in one of the model components (i.e., single-component CDA). The more realistic treatment of air-sea interactions and improvements to the model mean state in the multi-component CDA recover many aspects of MJO representation, from its space-time structure and propagation (see Figure 1) to the governing relationships between precipitation and sea surface temperature on intra-seasonal scales. Standard qualitative and process-based diagnostics identified by the MJO Task Force (currently under the auspices of the Working Group on Numerical Experimentation) have been used to detect the MJO signals across a suite of coupled model experiments involving both multi-component and single-component DA experiments as well as a free run of the coupled CESM model (i.e., CMIP5 style without data assimilation). Short predictability experiments during the boreal winter are used to demonstrate that the decay rates of the MJO convective anomalies are slower in the multi-component CDA system, which allows it to retain the MJO dynamics for a longer period. We anticipate that the knowledge gained through this study will enhance our understanding of the MJO feedback mechanisms across the air-sea interface, especially regarding ocean impacts on the MJO as well as highlight the capability of coupled data assimilation systems for related tropical intraseasonal variability predictions.

Changing risks of resonance in extreme weather events for higher atmospheric greenhouse gas concentrations

NASA Astrophysics Data System (ADS)

Huntingford, Chris; Mitchell, Dann; Osprey, Scott

2015-04-01

A recent paper by Petoukhov et al (2013) demonstrates that many of the recent major extreme events in the NH may have been caused by resonant conditions driving very high meridional winds around slowly moving centres-of-action. Besides high amplitudes of planetary wave numbers 6,7 and 8, additional features are identified through 4 further conditions that trigger system resonance. These make the potential for high amplitude waves more likely as well as the possibility of more persistent events. A concern is that human-induced climate change could create conditions more conducive to tropospheric Rossby wave resonance, thereby forcing any periods of extreme weather to become more commonplace and longer lasting. Whilst the CMIP5 ensemble provides much information on expected changes, to fully address changing probabilities of extreme event occurrence - which by definition are relatively rare - is, though, best approached through a massive ensemble modeling framework. The climateprediction-dot-net citizen-science massive ensemble GCM modeling framework provides order 104 simulations for sea-surface temperature, sea-ice extent and atmospheric gas composition representative of both pre-industrial and contemporary conditions. Here we present what these families of simulations imply in terms of the changing likelihood of conditions for mid-latitude resonance, and implications for amplitudes of Rossby waves

Assessing uncertainties in crop and pasture ensemble model simulations of productivity and N2 O emissions.

PubMed

Ehrhardt, Fiona; Soussana, Jean-François; Bellocchi, Gianni; Grace, Peter; McAuliffe, Russel; Recous, Sylvie; Sándor, Renáta; Smith, Pete; Snow, Val; de Antoni Migliorati, Massimiliano; Basso, Bruno; Bhatia, Arti; Brilli, Lorenzo; Doltra, Jordi; Dorich, Christopher D; Doro, Luca; Fitton, Nuala; Giacomini, Sandro J; Grant, Brian; Harrison, Matthew T; Jones, Stephanie K; Kirschbaum, Miko U F; Klumpp, Katja; Laville, Patricia; Léonard, Joël; Liebig, Mark; Lieffering, Mark; Martin, Raphaël; Massad, Raia S; Meier, Elizabeth; Merbold, Lutz; Moore, Andrew D; Myrgiotis, Vasileios; Newton, Paul; Pattey, Elizabeth; Rolinski, Susanne; Sharp, Joanna; Smith, Ward N; Wu, Lianhai; Zhang, Qing

2018-02-01

Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed systematically for variables affecting food security and climate change mitigation, within multi-species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi-model ensembles to predict productivity and nitrous oxide (N 2 O) emissions for wheat, maize, rice and temperate grasslands. Using a multi-stage modelling protocol, from blind simulations (stage 1) to partial (stages 2-4) and full calibration (stage 5), 24 process-based biogeochemical models were assessed individually or as an ensemble against long-term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N 2 O emissions. Results showed that across sites and crop/grassland types, 23%-40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N 2 O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N 2 O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2-4) markedly reduced prediction errors of the full model ensemble E-median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N 2 O emissions. Yield-scaled N 2 O emissions (N 2 O emissions divided by crop yields) were ranked accurately by three-model ensembles across crop species and field sites. The potential of using process-based model ensembles to predict jointly productivity and N 2 O emissions at field scale is discussed. © 2017 John Wiley & Sons Ltd.

Risk assessments of regional climate change over Europe: generation of probabilistic ensemble and uncertainty assessment for EURO-CODEX

NASA Astrophysics Data System (ADS)

Yuan, J.; Kopp, R. E.

2017-12-01

Quantitative risk analysis of regional climate change is crucial for risk management and impact assessment of climate change. Two major challenges to assessing the risks of climate change are: CMIP5 model runs, which drive EURO-CODEX downscaling runs, do not cover the full range of uncertainty of future projections; Climate models may underestimate the probability of tail risks (i.e. extreme events). To overcome the difficulties, this study offers a viable avenue, where a set of probabilistic climate ensemble is generated using the Surrogate/Model Mixed Ensemble (SMME) method. The probabilistic ensembles for temperature and precipitation are used to assess the range of uncertainty covered by five bias-corrected simulations from the high-resolution (0.11º) EURO-CODEX database, which are selected by the PESETA (The Projection of Economic impacts of climate change in Sectors of the European Union based on bottom-up Analysis) III project. Results show that the distribution of SMME ensemble is notably wider than both distribution of raw ensemble of GCMs and the spread of the five EURO-CORDEX in RCP8.5. Tail risks are well presented by the SMME ensemble. Both SMME ensemble and EURO-CORDEX projections are aggregated to administrative level, and are integrated into impact functions of PESETA III to assess climate risks in Europe. To further evaluate the uncertainties introduced by the downscaling process, we compare the 5 runs from EURO-CORDEX with runs from the corresponding GCMs. Time series of regional mean, spatial patterns, and climate indices are examined for the future climate (2080-2099) deviating from the present climate (1981-2010). The downscaling processes do not appear to be trend-preserving, e.g. the increase in regional mean temperature from EURO-CORDEX is slower than that from the corresponding GCM. The spatial pattern comparison reveals that the differences between each pair of GCM and EURO-CORDEX are small in winter. In summer, the temperatures of EURO-CORDEX are generally lower than those of GCMs, while the drying trends in precipitation of EURO-CORDEX are smaller than those of GCMs. Climate indices are significantly affected by bias-correction and downscaling process. Our study provides valuable information for selecting climate indices in different regions over Europe.

Assessment of Mediterranean cyclones in the multi-ensemble EC-Earth

NASA Astrophysics Data System (ADS)

Gil, Victoria; Liberato, Margarida L. R.; Trigo, Isabel F.; Trigo, Ricardo M.

2015-04-01

The geographical location and characteristics of the Mediterranean basin make this a particularly active region in terms of cyclone forming and re-development (Trigo et al., 2002). The area is affected by moving depressions, most originated over the North Atlantic, which may later be forced by the orography surrounding the Mediterranean Sea and enhanced by the local source of moisture and heat fluxes over the Sea itself. The present work analyses the response of Mediterranean cyclones to climate change by means of 7 ensemble members of EC-EARTH model from CMIP5 (Fifth Coupled Model Intercomparison Project). We restrict the analysis to a relatively small subset (7 members) of the total number of ensemble members available in order to take into account only the members present in the three selected experiments for robust detection of extra-tropical cyclones in the Mediterranean (Trigo, 2006). We have applied the standard procedure by comparing a common 25-year period of the historical (1980-2004), present day simulations, and the future climate simulations (2074-2098) forced by RCP4.5 and RCP8.5 scenarios. The study area corresponds to the window between 10°W-42°E and 27°N-48°N. The analysis is performed with a focus in spatial distribution density and main characteristics of the overall cyclones for winter (DJF) and summer (JJA) seasons. Despite the discrepancies in cyclone numbers when compared with the ERA Interim common period (reducing to only 72% in DJF and 78% in JJA), the ensemble average matches relatively well the main spatial patterns of areas. Results indicate that the ensemble average is characterized by a small decrease in winter (-3%) and a notable increase in summer (+10%) in total number of cyclones and that the individual ensemble members reveal small spread. Such tendency is particularly pronounced under the high RCP8.5 emission scenario being more moderated under the RCP4.5 scenario. Additionally, an assessment of changes in the annual cycle suggests a slight decrease of the spring maximum and a pronounced increase in the summer maximum. The cyclone characteristics obtained from the ensemble members of EC-Earth indicate that summer cyclones will tend to be slower, less intense but will have a faster deepening phase. Part of the summer enhanced activity is in areas dominated by thermal lows. Trigo I.F., G. R. Bigg and T.D. Davies, 2002: Climatology of cyclogenesis mechanisms in the Mediterranean. Mon. Wea. Rev. 130, 549-569. Trigo, I. F., 2006: Climatology and Interannual Variability of Storm-Tracks in the Euro-Atlantic sector: a comparison between ERA-40 and NCEP/NCAR Reanalyses. Clim. Dynam., 26, 127-143. Acknowledgements: This work was partially supported by FEDER (Fundo Europeu de Desenvolvimento Regional) funds through the COMPETE (Programa Operacional Factores de Competitividade) and by national funds through FCT (Fundação para a Ciência e a Tecnologia, Portugal) under project STORMEx FCOMP-01-0124-FEDER- 019524 (PTDC/AAC-CLI/121339/2010).

From land use to land cover: Restoring the afforestation signal in a coupled integrated assessment - earth system model and the implications for CMIP5 RCP simulations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Di Vittorio, Alan V.; Chini, Louise M.; Bond-Lamberty, Benjamin

2014-11-27

Climate projections depend on scenarios of fossil fuel emissions and land use change, and the IPCC AR5 parallel process assumes consistent climate scenarios across Integrated Assessment and Earth System Models (IAMs and ESMs). To facilitate consistency, CMIP5 used a novel land use harmonization to provide ESMs with seamless, 1500-2100 land use trajectories generated by historical data and four IAMs. However, we have identified and partially addressed a major gap in the CMIP5 land coupling design. The CMIP5 Community ESM (CESM) global afforestation is only 22% of RCP4.5 afforestation from 2005 to 2100. Likewise, only 17% of the Global Change Assessmentmore » Model’s (GCAM’s) 2040 RCP4.5 afforestation signal, and none of the pasture loss, were transmitted to CESM within a newly integrated model. This is a critical problem because afforestation is necessary for achieving the RCP4.5 climate stabilization. We attempted to rectify this problem by modifying only the ESM component of the integrated model, enabling CESM to simulate 66% of GCAM’s afforestation in 2040, and 94% of GCAM’s pasture loss as grassland and shrubland losses. This additional afforestation increases vegetation carbon gain by 19 PgC and decreases atmospheric CO2 gain by 8 ppmv from 2005 to 2040, implying different climate scenarios between CMIP5 GCAM and CESM. Similar inconsistencies likely exist in other CMIP5 model results, primarily because land cover information is not shared between models, with possible contributions from afforestation exceeding model-specific, potentially viable forest area. Further work to harmonize land cover among models will be required to adequately rectify this problem.« less

CMIP5-based global wave climate projections including the entire Arctic Ocean

NASA Astrophysics Data System (ADS)

Casas-Prat, M.; Wang, X. L.; Swart, N.

2018-03-01

This study presents simulations of the global ocean wave climate corresponding to the surface winds and sea ice concentrations as simulated by five CMIP5 (Coupled Model Intercomparison Project Phase 5) climate models for the historical (1979-2005) and RCP8.5 scenario future (2081-2100) periods. To tackle the numerical complexities associated with the inclusion of the North Pole, the WAVEWATCH III (WW3) wave model was used with a customized unstructured Spherical Multi-Cell grid of ∼100 km offshore and ∼50 km along coastlines. The climate model simulated wind and sea ice data, and the corresponding WW3 simulated wave data, were evaluated against reanalysis and hindcast data. The results show that all the five sets of wave simulations projected lower waves in the North Atlantic, corresponding to decreased surface wind speeds there in the warmer climate. The selected CMIP5 models also consistently projected an increase in the surface wind speed in the Southern Hemisphere (SH) mid-high latitudes, which translates in an increase in the WW3 simulated significant wave height (Hs) there. The higher waves are accompanied with increased peak wave period and increased wave age in the East Pacific and Indian Oceans, and a significant counterclockwise rotation in the mean wave direction in the Southern Oceans. The latter is caused by more intense waves from the SH traveling equatorward and developing into swells. Future wave climate in the Arctic Ocean in summer is projected to be predominantly of mixed sea states, with the climatological mean of September maximum Hs ranging mostly 3-4 m. The new waves approaching Arctic coasts will be less fetch-limited as ice retreats since a predominantly southwards mean wave direction is projected in the surrounding seas.

Which climate change path are we following? Bad news from Scots pine

PubMed Central

D’Andrea, Ettore; Rezaie, Negar; Cammarano, Mario; Matteucci, Giorgio

2017-01-01

Current expectations on future climate derive from coordinated experiments, which compile many climate models for sampling the entire uncertainty related to emission scenarios, initial conditions, and modelling process. Quantifying this uncertainty is important for taking decisions that are robust under a wide range of possible future conditions. Nevertheless, if uncertainty is too large, it can prevent from planning specific and effective measures. For this reason, reducing the spectrum of the possible scenarios to a small number of one or a few models that actually represent the climate pathway influencing natural ecosystems would substantially increase our planning capacity. Here we adopt a multidisciplinary approach based on the comparison of observed and expected spatial patterns of response to climate change in order to identify which specific models, among those included in the CMIP5, catch the real climate variation driving the response of natural ecosystems. We used dendrochronological analyses for determining the geographic pattern of recent growth trends for three European species of trees. At the same time, we modelled the climatic niche for the same species and forecasted the suitability variation expected across Europe under each different GCM. Finally, we estimated how well each GCM explains the real response of ecosystems, by comparing the expected variation with the observed growth trends. Doing this, we identified four climatic models that are coherent with the observed trends. These models are close to the highest range limit of the climatic variations expected by the ensemble of the CMIP5 models, suggesting that current predictions of climate change impacts on ecosystems could be underestimated. PMID:29252985

Which climate change path are we following? Bad news from Scots pine.

PubMed

Bombi, Pierluigi; D'Andrea, Ettore; Rezaie, Negar; Cammarano, Mario; Matteucci, Giorgio

2017-01-01

Current expectations on future climate derive from coordinated experiments, which compile many climate models for sampling the entire uncertainty related to emission scenarios, initial conditions, and modelling process. Quantifying this uncertainty is important for taking decisions that are robust under a wide range of possible future conditions. Nevertheless, if uncertainty is too large, it can prevent from planning specific and effective measures. For this reason, reducing the spectrum of the possible scenarios to a small number of one or a few models that actually represent the climate pathway influencing natural ecosystems would substantially increase our planning capacity. Here we adopt a multidisciplinary approach based on the comparison of observed and expected spatial patterns of response to climate change in order to identify which specific models, among those included in the CMIP5, catch the real climate variation driving the response of natural ecosystems. We used dendrochronological analyses for determining the geographic pattern of recent growth trends for three European species of trees. At the same time, we modelled the climatic niche for the same species and forecasted the suitability variation expected across Europe under each different GCM. Finally, we estimated how well each GCM explains the real response of ecosystems, by comparing the expected variation with the observed growth trends. Doing this, we identified four climatic models that are coherent with the observed trends. These models are close to the highest range limit of the climatic variations expected by the ensemble of the CMIP5 models, suggesting that current predictions of climate change impacts on ecosystems could be underestimated.

The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model

NASA Astrophysics Data System (ADS)

Baehr, J.; Fröhlich, K.; Botzet, M.; Domeisen, D. I. V.; Kornblueh, L.; Notz, D.; Piontek, R.; Pohlmann, H.; Tietsche, S.; Müller, W. A.

2015-05-01

A seasonal forecast system is presented, based on the global coupled climate model MPI-ESM as used for CMIP5 simulations. We describe the initialisation of the system and analyse its predictive skill for surface temperature. The presented system is initialised in the atmospheric, oceanic, and sea ice component of the model from reanalysis/observations with full field nudging in all three components. For the initialisation of the ensemble, bred vectors with a vertically varying norm are implemented in the ocean component to generate initial perturbations. In a set of ensemble hindcast simulations, starting each May and November between 1982 and 2010, we analyse the predictive skill. Bias-corrected ensemble forecasts for each start date reproduce the observed surface temperature anomalies at 2-4 months lead time, particularly in the tropics. Niño3.4 sea surface temperature anomalies show a small root-mean-square error and predictive skill up to 6 months. Away from the tropics, predictive skill is mostly limited to the ocean, and to regions which are strongly influenced by ENSO teleconnections. In summary, the presented seasonal prediction system based on a coupled climate model shows predictive skill for surface temperature at seasonal time scales comparable to other seasonal prediction systems using different underlying models and initialisation strategies. As the same model underlying our seasonal prediction system—with a different initialisation—is presently also used for decadal predictions, this is an important step towards seamless seasonal-to-decadal climate predictions.

Climate Change Impact Assessment of Hydro-Climate in Southern Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Ercan, A.; Ishida, K.; Kavvas, M. L.; Chen, Z. R.; Jang, S.; Amin, M. Z. M.; Shaaban, A. J.

2017-12-01

Impacts of climate change on the hydroclimate of the coastal region in the south of Peninsular Malaysia in the 21st century was assessed by means of a regional climate model utilizing an ensemble of 15 different future climate realizations. Coarse resolution Global Climate Models' future projections covering four emission scenarios based on Coupled Model Intercomparison Project phase 3 (CMIP3) datasets were dynamically downscaled to 6 km resolution over the study area. The analyses were made in terms of rainfall, air temperature, evapotranporation, and soil water storage.

The Sensitivity of Regional Precipitation to Global Temperature Change and Forcings

NASA Astrophysics Data System (ADS)

Tebaldi, C.; O'Neill, B. C.; Lamarque, J. F.

2016-12-01

Global policies are most commonly formulated in terms of climate targets, like the much talked about 1.5° and 2°C warming thresholds identified as critical by the recent Paris agreements. But what does a target defined in terms of a globally averaged quantity mean in terms of expected regional changes? And, in particular, what should we expect in terms of significant changes in precipitation over specific regional domains for these and other incrementally different global goals? In this talk I will summarize the result of an analysis that aimed at characterizing the sensitivity of regional temperatures and precipitation amounts to changes in global average temperature. The analysis uses results from a multi-model ensemble (CMIP5), which allows us to address structural uncertainty in future projections, a type of uncertainty particularly relevant when considering precipitation changes. I will show what type of changes in global temperature and forcing levels bring about significant and pervasive changes in regional precipitation, contrasting its sensitivity to that of regional temperature changes. Because of the large internal variability of regional precipitation, I will show that significant changes in average regional precipitation can be detected only for fairly large separations (on the order of 2.5° or 3°C) in global average temperature levels, differently from the much higher sensitivity shown by regional temperatures.

Long-term Ozone Changes and Associated Climate Impacts in CMIP5 Simulations

NASA Technical Reports Server (NTRS)

Eyring, V.; Arblaster, J. M.; Cionni, I.; Sedlacek, J.; Perlwitz, J.; Young, P. J.; Bekki, S.; Bergmann, D.; Cameron-Smith, P.; Collins, W. J.;

Structural Uncertainty in Antarctic sea ice simulations

NASA Astrophysics Data System (ADS)

Schneider, D. P.

2016-12-01

The inability of the vast majority of historical climate model simulations to reproduce the observed increase in Antarctic sea ice has motivated many studies about the quality of the observational record, the role of natural variability versus forced changes, and the possibility of missing or inadequate forcings in the models (such as freshwater discharge from thinning ice shelves or an inadequate magnitude of stratospheric ozone depletion). In this presentation I will highlight another source of uncertainty that has received comparatively little attention: Structural uncertainty, that is, the systematic uncertainty in simulated sea ice trends that arises from model physics and mean-state biases. Using two large ensembles of experiments from the Community Earth System Model (CESM), I will show that the model is predisposed towards producing negative Antarctic sea ice trends during 1979-present, and that this outcome is not simply because the model's decadal variability is out-of-synch with that in nature. In the "Tropical Pacific Pacemaker" ensemble, in which observed tropical Pacific SST anomalies are prescribed, the model produces very realistic atmospheric circulation trends over the Southern Ocean, yet the sea ice trend is negative in every ensemble member. However, if the ensemble-mean trend (commonly interpreted as the forced response) is removed, some ensemble members show a sea ice increase that is very similar to the observed. While this results does confirm the important role of natural variability, it also suggests a strong bias in the forced response. I will discuss the reasons for this systematic bias and explore possible remedies. This an important problem to solve because projections of 21st -Century changes in the Antarctic climate system (including ice sheet surface mass balance changes and related changes in the sea level budget) have a strong dependence on the mean state of and changes in the Antarctic sea ice cover. This problem is not unique to CESM, but is pervasive across CMIP5-class models.

AIRS Obs4MIPs V2 data set

NASA Astrophysics Data System (ADS)

Tian, B.

2017-12-01

The Coupled Model Intercomparison Project (CMIP) has become a central element of national and international assessments of climate change. The CMIP Phase 6 (CMIP6) model experiments will be the foundation for the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report (AR6), scheduled for publication around 2021. To increase the fidelity of the IPCC AR6, the CMIP6 model experiments need rigorous evaluation. The "Observations for Model Intercomparison Projects" (Obs4MIPs) collects, organizes and publishes various well-established satellite data sets for CMIP model evaluation. The Atmospheric Infrared Sounder (AIRS) and Advanced Microwave Sounding Unit (AMSU), the NASA's temperature and humidity sounding system on the Aqua satellite, has provided over a decade-long high-quality tropospheric temperature and moisture sounding data. Under the current support of the NASA Data for Operation and Assessment (NDOA) program, we are generating and publishing the AIRS Obs4MIPs V2 data set including the monthly mean tropospheric air temperature, specific humidity, and relative humidity profiles from September 2002 to September 2016. This will provide the latest AIRS data in Obs4MIPs and assist the climate modeling community to better use the AIRS data for CMIP (including CMIP3, CMIP5, and CMIP6) model evaluation. In this presentation, we will discuss the AIRS Obs4MIPs V2 data set and their possible use for CMIP6 climate model evaluation.

Reproducing multi-model ensemble average with Ensemble-averaged Reconstructed Forcings (ERF) in regional climate modeling

NASA Astrophysics Data System (ADS)

Erfanian, A.; Fomenko, L.; Wang, G.

2016-12-01

Multi-model ensemble (MME) average is considered the most reliable for simulating both present-day and future climates. It has been a primary reference for making conclusions in major coordinated studies i.e. IPCC Assessment Reports and CORDEX. The biases of individual models cancel out each other in MME average, enabling the ensemble mean to outperform individual members in simulating the mean climate. This enhancement however comes with tremendous computational cost, which is especially inhibiting for regional climate modeling as model uncertainties can originate from both RCMs and the driving GCMs. Here we propose the Ensemble-based Reconstructed Forcings (ERF) approach to regional climate modeling that achieves a similar level of bias reduction at a fraction of cost compared with the conventional MME approach. The new method constructs a single set of initial and boundary conditions (IBCs) by averaging the IBCs of multiple GCMs, and drives the RCM with this ensemble average of IBCs to conduct a single run. Using a regional climate model (RegCM4.3.4-CLM4.5), we tested the method over West Africa for multiple combination of (up to six) GCMs. Our results indicate that the performance of the ERF method is comparable to that of the MME average in simulating the mean climate. The bias reduction seen in ERF simulations is achieved by using more realistic IBCs in solving the system of equations underlying the RCM physics and dynamics. This endows the new method with a theoretical advantage in addition to reducing computational cost. The ERF output is an unaltered solution of the RCM as opposed to a climate state that might not be physically plausible due to the averaging of multiple solutions with the conventional MME approach. The ERF approach should be considered for use in major international efforts such as CORDEX. Key words: Multi-model ensemble, ensemble analysis, ERF, regional climate modeling

Systematic Biases of Present-day's Land Surface Air Temperature and Precipitation and Associated Tendency of Future Projection in the Asia Monsoon of the CMIP5 models

NASA Astrophysics Data System (ADS)

Ose, T.

2016-12-01

Seasonally varying land surface air temperature (SAT) is basically responsible for the occurrence of the Asia Monsoon precipitation whereas the precipitation may play more important roles in the appearance and variability of the Asia Monsoon circulations. A simple and basic analysis on model biases of land SAT simulations over the Eurasian Continent is done to find necessary improvements of land surface treatment in the models, their relationship with model precipitation and their influences to future projections. Specifically, the Empirical Orthogonal Function (EOF) analysis is applied to land SATs of the CMIP5 present-day's simulation (the June-July-August average during 1975-1999) ensemble. Associated biases of precipitation and other Asia Monsoon elements are obtained by the regression method onto the obtained EOF coefficients. The first EOF is the SAT bias over the dry region of the Eurasia. Positive deviations of the 1st EOF coefficient indicate northwestward shift of the Asia Monsoon System; northwestward (or inner-continent-ward) shifts of precipitation, the Tibetan High, the low-level jet, the Pacific High and so on. The second EOF is the SAT bias over the northeast Eurasia. It is interesting that warmer land SAT bias over the northeast Asia is related to more wet condition over East Asia like in early summer; southward shift of westerly jet and precipitation band in East Asia. The third one indicates the SAT bias over the Eurasian region between the 1st and 2nd EOF SAT regions. However, this EOF may be characterized by the accompanied model precipitation bias over the subtropical Northwest Pacific like in late summer; northeastward shift of upper westerly jet in the eastern Asia and the weak Pacific High in the subtropical Northwest Pacific. The most intrigued feature is a connection of the 3rd EOF with the future change of SAT in the extra-tropical Northern Hemisphere in the CMIP5 projections. This fact may indicate that precipitation climatology in the Asia Monsoon is an important factor in the heat budget of the northern summer in the future change as well as the present-day simulation.

Multi-model ensembles for assessment of flood losses and associated uncertainty

NASA Astrophysics Data System (ADS)

Figueiredo, Rui; Schröter, Kai; Weiss-Motz, Alexander; Martina, Mario L. V.; Kreibich, Heidi

2018-05-01

Flood loss modelling is a crucial part of risk assessments. However, it is subject to large uncertainty that is often neglected. Most models available in the literature are deterministic, providing only single point estimates of flood loss, and large disparities tend to exist among them. Adopting any one such model in a risk assessment context is likely to lead to inaccurate loss estimates and sub-optimal decision-making. In this paper, we propose the use of multi-model ensembles to address these issues. This approach, which has been applied successfully in other scientific fields, is based on the combination of different model outputs with the aim of improving the skill and usefulness of predictions. We first propose a model rating framework to support ensemble construction, based on a probability tree of model properties, which establishes relative degrees of belief between candidate models. Using 20 flood loss models in two test cases, we then construct numerous multi-model ensembles, based both on the rating framework and on a stochastic method, differing in terms of participating members, ensemble size and model weights. We evaluate the performance of ensemble means, as well as their probabilistic skill and reliability. Our results demonstrate that well-designed multi-model ensembles represent a pragmatic approach to consistently obtain more accurate flood loss estimates and reliable probability distributions of model uncertainty.

A new paradigm for predicting zonal-mean climate and climate change

NASA Astrophysics Data System (ADS)

Armour, K.; Roe, G.; Donohoe, A.; Siler, N.; Markle, B. R.; Liu, X.; Feldl, N.; Battisti, D. S.; Frierson, D. M.

2016-12-01

How will the pole-to-equator temperature gradient, or large-scale patterns of precipitation, change under global warming? Answering such questions typically involves numerical simulations with comprehensive general circulation models (GCMs) that represent the complexities of climate forcing, radiative feedbacks, and atmosphere and ocean dynamics. Yet, our understanding of these predictions hinges on our ability to explain them through the lens of simple models and physical theories. Here we present evidence that zonal-mean climate, and its changes, can be understood in terms of a moist energy balance model that represents atmospheric heat transport as a simple diffusion of latent and sensible heat (as a down-gradient transport of moist static energy, with a diffusivity coefficient that is nearly constant with latitude). We show that the theoretical underpinnings of this model derive from the principle of maximum entropy production; that its predictions are empirically supported by atmospheric reanalyses; and that it successfully predicts the behavior of a hierarchy of climate models - from a gray radiation aquaplanet moist GCM, to comprehensive GCMs participating in CMIP5. As an example of the power of this paradigm, we show that, given only patterns of local radiative feedbacks and climate forcing, the moist energy balance model accurately predicts the evolution of zonal-mean temperature and atmospheric heat transport as simulated by the CMIP5 ensemble. These results suggest that, despite all of its dynamical complexity, the atmosphere essentially responds to energy imbalances by simply diffusing latent and sensible heat down-gradient; this principle appears to explain zonal-mean climate and its changes under global warming.

From Past to future: the Paleoclimate Modelling Intercomparison Project's contribution to CMIP6

NASA Astrophysics Data System (ADS)

Kageyama, Masa; Braconnot, Pascale; Harrison, Sandy; Haywood, Alan; Jungclaus, Johann; Otto-Bliesner, Bette; Abe-Ouchi, Ayako

2016-04-01

Since the 1990s, PMIP has developed with the following objectives: 1/to evaluate the ability of climate models used for climate prediction in simulating well-documented past climates outside the range of present and recent climate variability; 2/to understand the mechanisms of these climate changes, in particular the role of the different climate feedbacks. To achieve these goals, PMIP has actively fostered paleo-data syntheses, multi-model analyses, including analyses of relationships between model results from past and future simulations, and model-data comparisons. For CMIP6, PMIP will focus on five past periods: - the Last Millennium (850 CE - present), to analyse natural climate variability on multidecadal or longer time-scales - the mid-Holocene, 6000 years ago, to compare model runs with paleodata for a period of warmer climate in the Northern Hemisphere, with an enhanced hydrological cycle - the Last Glacial Maximum, 21000 years ago, to evaluate the ability of climate models to represent a cold climate extreme and examine whether paleoinformation about this period can help and constrain climate sensitivity - the Last InterGlacial (~127,000 year ago), which provides a benchmark for a period of high sea-level stand - the mid-Pliocene warm period (~3.2 million years ago), which allows for the evaluation of the model's long-term response to a CO2 level analogous to the modern one. This poster will present the rationale of these "PMIP4-CMIP6" experiments. Participants are invited to come and discuss about the experimental set-up and the model output to be distributed via CMIP6. For more information and discussion of the PMIP4-CMIP6 experimental design, please visit: https://wiki.lsce.ipsl.fr/pmip3/doku.php/pmip3:cmip6:design:index

Koeppen Bioclimatic Metrics for Evaluating CMIP5 Simulations of Historical Climate

NASA Astrophysics Data System (ADS)

Phillips, T. J.; Bonfils, C.

2012-12-01

The classic Koeppen bioclimatic classification scheme associates generic vegetation types (e.g. grassland, tundra, broadleaf or evergreen forests, etc.) with regional climate zones defined by the observed amplitude and phase of the annual cycles of continental temperature (T) and precipitation (P). Koeppen classification thus can provide concise, multivariate metrics for evaluating climate model performance in simulating the regional magnitudes and seasonalities of climate variables that are of critical importance for living organisms. In this study, 14 Koeppen vegetation types are derived from annual-cycle climatologies of T and P in some 3 dozen CMIP5 simulations of 1980-1999 climate, a period when observational data provides a reliable global validation standard. Metrics for evaluating the ability of the CMIP5 models to simulate the correct locations and areas of the vegetation types, as well as measures of overall model performance, also are developed. It is found that the CMIP5 models are most deficient in simulating 1) the climates of the drier zones (e.g. desert, savanna, grassland, steppe vegetation types) that are located in the Southwestern U.S. and Mexico, Eastern Europe, Southern Africa, and Central Australia, as well as 2) the climate of regions such as Central Asia and Western South America where topography plays a central role. (Detailed analysis of regional biases in the annual cycles of T and P of selected simulations exemplifying general model performance problems also are to be presented.) The more encouraging results include evidence for a general improvement in CMIP5 performance relative to that of older CMIP3 models. Within CMIP5 also, the more complex Earth Systems Models (ESMs) with prognostic biogeochemistry perform comparably to the corresponding global models that simulate only the "physical" climate. Acknowledgments This work was funded by the U.S. Department of Energy Office of Science and was performed at the Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Global Warming Impacts on Heating and Cooling Degree-Days in the United States

NASA Astrophysics Data System (ADS)

Petri, Y.; Caldeira, K.

2014-12-01

Anthropogenic climate change is expected to significantly alter residential air conditioning and space heating requirements, which account for 41% of U.S. household energy expenditures. The degree-day method can be used for reliable estimation of weather related building energy consumption and costs, as well as outdoor climatic thermal comfort. Here, we use U.S. Climate Normals developed by NOAA based on weather station observations along with Climate Model Intercomparison Project phase 5 (CMIP5) multi-model ensemble simulations. We add the projected change in heating and cooling degree-days based on the climate models to the estimates based on the NOAA U.S. Climate Normals to project future heating and cooling degree-days. We find locations with the lowest and highest combined index of cooling (CDDs) and heating degree-days (HDDs) for the historical period (1981 - 2010) and future period (2080 - 2099) under the Representation Concentration Pathway 8.5 (RCP8.5) climate change scenario. Our results indicate that in both time frames and among the lower 48 states, coastal areas in the West and South California will have the smallest degree-day sum (CDD + HDD), and hence from a climatic perspective become the best candidates for residential real estate. The Rocky Mountains region in Wyoming, in addition to northern Minnesota and North Dakota, will have the greatest CDD + HDD. While global warming is projected to reduce the median heating and cooling demand (- 5%) at the end of the century, CDD + HDD will decrease in the North, with an opposite effect in the South. This work could be helpful in deciding where to live in the United States based on present and future thermal comfort, and could also provide a basis for estimates of changes in heating and cooling energy demand.

Localized Multi-Model Extremes Metrics for the Fourth National Climate Assessment

NASA Astrophysics Data System (ADS)

Thompson, T. R.; Kunkel, K.; Stevens, L. E.; Easterling, D. R.; Biard, J.; Sun, L.

2017-12-01

We have performed localized analysis of scenario-based datasets for the Fourth National Climate Assessment (NCA4). These datasets include CMIP5-based Localized Constructed Analogs (LOCA) downscaled simulations at daily temporal resolution and 1/16th-degree spatial resolution. Over 45 temperature and precipitation extremes metrics have been processed using LOCA data, including threshold, percentile, and degree-days calculations. The localized analysis calculates trends in the temperature and precipitation extremes metrics for relatively small regions such as counties, metropolitan areas, climate zones, administrative areas, or economic zones. For NCA4, we are currently addressing metropolitan areas as defined by U.S. Census Bureau Metropolitan Statistical Areas. Such localized analysis provides essential information for adaptation planning at scales relevant to local planning agencies and businesses. Nearly 30 such regions have been analyzed to date. Each locale is defined by a closed polygon that is used to extract LOCA-based extremes metrics specific to the area. For each metric, single-model data at each LOCA grid location are first averaged over several 30-year historical and future periods. Then, for each metric, the spatial average across the region is calculated using model weights based on both model independence and reproducibility of current climate conditions. The range of single-model results is also captured on the same localized basis, and then combined with the weighted ensemble average for each region and each metric. For example, Boston-area cooling degree days and maximum daily temperature is shown below for RCP8.5 (red) and RCP4.5 (blue) scenarios. We also discuss inter-regional comparison of these metrics, as well as their relevance to risk analysis for adaptation planning.

An Assessment of Actual and Potential Building Climate Zone Change and Variability From the Last 30 Years Through 2100 Using NASA's MERRA and CMIP5 Simulations

NASA Technical Reports Server (NTRS)

Stackhouse, Paul W., Jr.; Chandler, William S.; Hoell, James M.; Westberg, David; Zhang, Taiping

2015-01-01

Background: In the US, residential and commercial building infrastructure combined consumes about 40% of total energy usage and emits about 39% of total CO2 emission (DOE/EIA "Annual Energy Outlook 2013"). Building codes, as used by local and state enforcement entities are typically tied to the dominant climate within an enforcement jurisdiction classified according to various climate zones. These climate zones are based upon a 30-year average of local surface observations and are developed by DOE and ASHRAE. Establishing the current variability and potential changes to future building climate zones is very important for increasing the energy efficiency of buildings and reducing energy costs and emissions in the future. Objectives: This paper demonstrates the usefulness of using NASA's Modern Era Retrospective-analysis for Research and Applications (MERRA) atmospheric data assimilation to derive the DOE/ASHRAE building climate zone maps and then using MERRA to define the last 30 years of variability in climate zones for the Continental US. An atmospheric assimilation is a global atmospheric model optimized to satellite, atmospheric and surface in situ measurements. Using MERRA as a baseline, we then evaluate the latest Climate Model Inter-comparison Project (CMIP) climate model Version 5 runs to assess potential variability in future climate zones under various assumptions. Methods: We derive DOE/ASHRAE building climate zones using surface and temperature data products from MERRA. We assess these zones using the uncertainties derived by comparison to surface measurements. Using statistical tests, we evaluate variability of the climate zones in time and assess areas in the continental US for statistically significant trends by region. CMIP 5 produced a data base of over two dozen detailed climate model runs under various greenhouse gas forcing assumptions. We evaluate the variation in building climate zones for 3 different decades using an ensemble and quartile statistics to provide an assessment of potential building climate zone changes relative to the uncertainties demonstrated using MERRA. Findings and Conclusions: These results show that there is a statistically significant increase in the area covered by warmer climate zones and a tendency for a reduction of area in colder climate zones in some limited regions. The CMIP analysis shows that models vary from relatively little building climate zone change for the least sensitive and conservation assumptions to a warming of at most 3 zones for certain areas, particularly the north central US by the end of the 21st century.

Interactions between Antarctic sea ice and large-scale atmospheric modes in CMIP5 models

NASA Astrophysics Data System (ADS)

Schroeter, Serena; Hobbs, Will; Bindoff, Nathaniel L.

2017-03-01

The response of Antarctic sea ice to large-scale patterns of atmospheric variability varies according to sea ice sector and season. In this study, interannual atmosphere-sea ice interactions were explored using observations and reanalysis data, and compared with simulated interactions by models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Simulated relationships between atmospheric variability and sea ice variability generally reproduced the observed relationships, though more closely during the season of sea ice advance than the season of sea ice retreat. Atmospheric influence on sea ice is known to be strongest during advance, and it appears that models are able to capture the dominance of the atmosphere during advance. Simulations of ocean-atmosphere-sea ice interactions during retreat, however, require further investigation. A large proportion of model ensemble members overestimated the relative importance of the Southern Annular Mode (SAM) compared with other modes of high southern latitude climate, while the influence of tropical forcing was underestimated. This result emerged particularly strongly during the season of sea ice retreat. The zonal patterns of the SAM in many models and its exaggerated influence on sea ice overwhelm the comparatively underestimated meridional influence, suggesting that simulated sea ice variability would become more zonally symmetric as a result. Across the seasons of sea ice advance and retreat, three of the five sectors did not reveal a strong relationship with a pattern of large-scale atmospheric variability in one or both seasons, indicating that sea ice in these sectors may be influenced more strongly by atmospheric variability unexplained by the major atmospheric modes, or by heat exchange in the ocean.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Levine, Paul A.; Randerson, James T.; Swenson, Sean C.

The relationship between terrestrial water storage (TWS) and atmospheric processes has important implications for predictability of climatic extremes and projection of future climate change. In places where moisture availability limits evapotranspiration (ET), variability in TWS has the potential to influence surface energy fluxes and atmospheric conditions. Where atmospheric conditions, in turn, influence moisture availability, a full feedback loop exists. Here we developed a novel approach for measuring the strength of both components of this feedback loop, i.e., the forcing of the atmosphere by variability in TWS and the response of TWS to atmospheric variability, using satellite observations of TWS, precipitation,more » solar radiation, and vapor pressure deficit during 2002–2014. Our approach defines metrics to quantify the relationship between TWS anomalies and climate globally on a seasonal to interannual timescale. Metrics derived from the satellite data were used to evaluate the strength of the feedback loop in 38 members of the Community Earth System Model (CESM) Large Ensemble (LENS) and in six models that contributed simulations to phase 5 of the Coupled Model Intercomparison Project (CMIP5). We found that both forcing and response limbs of the feedback loop in LENS were stronger than in the satellite observations in tropical and temperate regions. Feedbacks in the selected CMIP5 models were not as strong as those found in LENS, but were still generally stronger than those estimated from the satellite measurements. Consistent with previous studies conducted across different spatial and temporal scales, our analysis suggests that models may overestimate the strength of the feedbacks between the land surface and the atmosphere. Lastly, we describe several possible mechanisms that may contribute to this bias, and discuss pathways through which models may overestimate ET or overestimate the sensitivity of ET to TWS.« less

Selecting climate simulations for impact studies based on multivariate patterns of climate change.

PubMed

Mendlik, Thomas; Gobiet, Andreas

In climate change impact research it is crucial to carefully select the meteorological input for impact models. We present a method for model selection that enables the user to shrink the ensemble to a few representative members, conserving the model spread and accounting for model similarity. This is done in three steps: First, using principal component analysis for a multitude of meteorological parameters, to find common patterns of climate change within the multi-model ensemble. Second, detecting model similarities with regard to these multivariate patterns using cluster analysis. And third, sampling models from each cluster, to generate a subset of representative simulations. We present an application based on the ENSEMBLES regional multi-model ensemble with the aim to provide input for a variety of climate impact studies. We find that the two most dominant patterns of climate change relate to temperature and humidity patterns. The ensemble can be reduced from 25 to 5 simulations while still maintaining its essential characteristics. Having such a representative subset of simulations reduces computational costs for climate impact modeling and enhances the quality of the ensemble at the same time, as it prevents double-counting of dependent simulations that would lead to biased statistics. The online version of this article (doi:10.1007/s10584-015-1582-0) contains supplementary material, which is available to authorized users.

Process-oriented Observational Metrics for CMIP6 Climate Model Assessments

NASA Astrophysics Data System (ADS)

Jiang, J. H.; Su, H.

2016-12-01

Observational metrics based on satellite observations have been developed and effectively applied during post-CMIP5 model evaluation and improvement projects. As new physics and parameterizations continue to be included in models for the upcoming CMIP6, it is important to continue objective comparisons between observations and model results. This talk will summarize the process-oriented observational metrics and methodologies for constraining climate models with A-Train satellite observations and support CMIP6 model assessments. We target parameters and processes related to atmospheric clouds and water vapor, which are critically important for Earth's radiative budget, climate feedbacks, and water and energy cycles, and thus reduce uncertainties in climate models.

MVL spatiotemporal analysis for model intercomparison in EPS: application to the DEMETER multi-model ensemble

NASA Astrophysics Data System (ADS)

Fernández, J.; Primo, C.; Cofiño, A. S.; Gutiérrez, J. M.; Rodríguez, M. A.

2009-08-01

In a recent paper, Gutiérrez et al. (Nonlinear Process Geophys 15(1):109-114, 2008) introduced a new characterization of spatiotemporal error growth—the so called mean-variance logarithmic (MVL) diagram—and applied it to study ensemble prediction systems (EPS); in particular, they analyzed single-model ensembles obtained by perturbing the initial conditions. In the present work, the MVL diagram is applied to multi-model ensembles analyzing also the effect of model formulation differences. To this aim, the MVL diagram is systematically applied to the multi-model ensemble produced in the EU-funded DEMETER project. It is shown that the shared building blocks (atmospheric and ocean components) impose similar dynamics among different models and, thus, contribute to poorly sampling the model formulation uncertainty. This dynamical similarity should be taken into account, at least as a pre-screening process, before applying any objective weighting method.

Next Generation Climate Change Experiments Needed to Advance Knowledge and for Assessment of CMIP6

DOE Office of Scientific and Technical Information (OSTI.GOV)

Katzenberger, John; Arnott, James; Wright, Alyson

2014-10-30

The Aspen Global Change Institute hosted a technical science workshop entitled, “Next generation climate change experiments needed to advance knowledge and for assessment of CMIP6,” on August 4-9, 2013 in Aspen, CO. Jerry Meehl (NCAR), Richard Moss (PNNL), and Karl Taylor (LLNL) served as co-chairs for the workshop which included the participation of 32 scientists representing most of the major climate modeling centers for a total of 160 participant days. In August 2013, AGCI gathered a high level meeting of representatives from major climate modeling centers around the world to assess achievements and lessons learned from the most recent generationmore » of coordinated modeling experiments known as the Coupled Model Intercomparison Project – 5 (CMIP5) as well as to scope out the science questions and coordination structure desired for the next anticipated phase of modeling experiments called CMIP6. The workshop allowed for reflection on the coordination of the CMIP5 process as well as intercomparison of model results, such as were assessed in the most recent IPCC 5th Assessment Report, Working Group 1. For example, this slide from Masahiro Watanabe examines performance on a range of models capturing Atlantic Meridional Overturning Circulation (AMOC).« less

Impact of the Montreal Protocol on Antarctic Surface Mass Balance

NASA Astrophysics Data System (ADS)

Previdi, M. J.; Polvani, L. M.

2016-12-01

We investigate the impact of the Montreal Protocol, and associated phase-out of ozone-depleting substances (ODSs), on the surface mass balance (SMB) of Antarctica, using simulations from the Community Earth System Model-Whole Atmosphere Community Climate Model (CESM-WACCM). The effect of ODSs on Antarctic SMB is first established by contrasting two sets of WACCM integrations (each with 6 ensemble members) for the period 1956-2005: one set that includes the full suite of natural and anthropogenic forcings, and a second set identical to the first but with atmospheric concentrations of ODSs held fixed at year 1955 levels. We find that holding ODSs fixed reduces the simulated increase in Antarctic SMB by nearly 60% in the ensemble mean, due to a suppression of Antarctic-mean warming. Having established this SMB impact of ODSs, we next examine three sets of future WACCM integrations (each with 3 ensemble members) for the period 2006-2065. The first two of these are the CMIP5 RCP4.5 and RCP8.5 integrations that include decreases in ODSs due to the implementation of the Montreal Protocol, and increases in other well-mixed greenhouse gases such as CO2. The third set of future integrations represents a hypothetical "world avoided" scenario in which the Montreal Protocol is assumed to have never been implemented, resulting in drastic increases in ODSs during the next several decades. In the world avoided, the simulated increase in Antarctic SMB is substantially larger than the other two scenarios, exceeding the SMB increases occurring under RCP4.5 and RCP8.5 by a factor of 3.7 and 1.9, respectively. The implications of this for future global sea-level rise will be discussed.

Simulations of the Mid-Pliocene Warm Period Using Two Versions of the NASA-GISS ModelE2-R Coupled Model

NASA Technical Reports Server (NTRS)

Chandler, M. A.; Sohl, L. E.; Jonas, J. A.; Dowsett, H. J.; Kelley, M.

2013-01-01

The mid-Pliocene Warm Period (mPWP) bears many similarities to aspects of future global warming as projected by the Intergovernmental Panel on Climate Change (IPCC, 2007). Both marine and terrestrial data point to high-latitude temperature amplification, including large decreases in sea ice and land ice, as well as expansion of warmer climate biomes into higher latitudes. Here we present our most recent simulations of the mid-Pliocene climate using the CMIP5 version of the NASAGISS Earth System Model (ModelE2-R). We describe the substantial impact associated with a recent correction made in the implementation of the Gent-McWilliams ocean mixing scheme (GM), which has a large effect on the simulation of ocean surface temperatures, particularly in the North Atlantic Ocean. The effect of this correction on the Pliocene climate results would not have been easily determined from examining its impact on the preindustrial runs alone, a useful demonstration of how the consequences of code improvements as seen in modern climate control runs do not necessarily portend the impacts in extreme climates.Both the GM-corrected and GM-uncorrected simulations were contributed to the Pliocene Model Intercomparison Project (PlioMIP) Experiment 2. Many findings presented here corroborate results from other PlioMIP multi-model ensemble papers, but we also emphasize features in the ModelE2-R simulations that are unlike the ensemble means. The corrected version yields results that more closely resemble the ocean core data as well as the PRISM3D reconstructions of the mid-Pliocene, especially the dramatic warming in the North Atlantic and Greenland-Iceland-Norwegian Sea, which in the new simulation appears to be far more realistic than previously found with older versions of the GISS model. Our belief is that continued development of key physical routines in the atmospheric model, along with higher resolution and recent corrections to mixing parameterisations in the ocean model, have led to an Earth System Model that will produce more accurate projections of future climate.

Probable Maximum Precipitation in the U.S. Pacific Northwest in a Changing Climate: PMP UNDER CLIMATE CHANGE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chen, Xiaodong; Hossain, Faisal; Leung, L. Ruby

The safety of large and aging water infrastructures is gaining attention in water management given the accelerated rate of change in landscape, climate and society. In current engineering practice, such safety is ensured by the design of infrastructure for the Probable Maximum Precipitation (PMP). Recently, several physics-based numerical modeling approaches have been proposed to modernize the conventional and ad hoc PMP estimation approach. However, the underlying physics has not been investigated and thus differing PMP estimates are obtained without clarity on their interpretation. In this study, we present a hybrid approach that takes advantage of both traditional engineering wisdom andmore » modern climate science to estimate PMP for current and future climate conditions. The traditional PMP approach is improved and applied to outputs from an ensemble of five CMIP5 models. This hybrid approach is applied in the Pacific Northwest (PNW) to produce ensemble PMP estimation for the historical (1970-2016) and future (2050-2099) time periods. The new historical PMP estimates are verified by comparing them with the traditional estimates. PMP in the PNW will increase by 50% of the current level by 2099 under the RCP8.5 scenario. Most of the increase is caused by warming, which mainly affects moisture availability, with minor contributions from changes in storm efficiency in the future. Moist track change tends to reduce the future PMP. Compared with extreme precipitation, ensemble PMP exhibits higher internal variation. Thus high-quality data of both precipitation and related meteorological fields (temperature, wind fields) are required to reduce uncertainties in the ensemble PMP estimates.« less

Statistical analysis of simulated global soil moisture and its memory in an ensemble of CMIP5 general circulation models

NASA Astrophysics Data System (ADS)

Wiß, Felix; Stacke, Tobias; Hagemann, Stefan

2014-05-01

Soil moisture and its memory can have a strong impact on near surface temperature and precipitation and have the potential to promote severe heat waves, dry spells and floods. To analyze how soil moisture is simulated in recent general circulation models (GCMs), soil moisture data from a 23 model ensemble of Atmospheric Model Intercomparison Project (AMIP) type simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are examined for the period 1979 to 2008 with regard to parameterization and statistical characteristics. With respect to soil moisture processes, the models vary in their maximum soil and root depth, the number of soil layers, the water-holding capacity, and the ability to simulate freezing which all together leads to very different soil moisture characteristics. Differences in the water-holding capacity are resulting in deviations in the global median soil moisture of more than one order of magnitude between the models. In contrast, the variance shows similar absolute values when comparing the models to each other. Thus, the input and output rates by precipitation and evapotranspiration, which are computed by the atmospheric component of the models, have to be in the same range. Most models simulate great variances in the monsoon areas of the tropics and north western U.S., intermediate variances in Europe and eastern U.S., and low variances in the Sahara, continental Asia, and central and western Australia. In general, the variance decreases with latitude over the high northern latitudes. As soil moisture trends in the models were found to be negligible, the soil moisture anomalies were calculated by subtracting the 30 year monthly climatology from the data. The length of the memory is determined from the soil moisture anomalies by calculating the first insignificant autocorrelation for ascending monthly lags (insignificant autocorrelation folding time). The models show a great spread of autocorrelation length from a few months in the tropics, north western Canada, eastern U.S. and northern Europe up to few years in the Sahara, the Arabian Peninsula, continental Eurasia and central U.S. Some models simulate very long memory all over the globe. This behavior is associated with differences between the models in the maximum root and soil depth. Models with shallow roots and deep soils exhibit longer memories than models with similar soil and root depths. Further analysis will be conducted to clearly divide models into groups based on their inter-model spatial correlation of simulated soil moisture characteristics.

Attribution of the July–August 2013 heat event in Central and Eastern China to anthropogenic greenhouse gas emissions

DOE PAGES

Ma, Shuangmei; Zhou, Tianjun; Stone, Dáithí A.; ...

2017-05-19

In the midsummer of 2013, Central and Eastern China (CEC) was hit by an extraordinary heat event, with the region experiencing the warmest July-August on record. To explore how human-induced greenhouse gas emissions and natural internal variability contributed to this heat event, we compare observed July-August mean surface air temperature wit h that simulated by climate models. We find that both atmospheric natural variability and anthropogenic factors contributed to this heat event. This extreme warm midsummer was associated with a positive high-pressure anomaly that was closely related to the stochastic behavior of atmospheric circulation. Diagnosis of CMIP5 models and largemore » ensembles of two atmospheric models indicates that human influence has substantially increased the chance of warm mid-summers such as 2013 in CEC, although the exact estimated increase depends on the selection of climate models.« less

Attribution of the July–August 2013 heat event in Central and Eastern China to anthropogenic greenhouse gas emissions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ma, Shuangmei; Zhou, Tianjun; Stone, Dáithí A.

In the midsummer of 2013, Central and Eastern China (CEC) was hit by an extraordinary heat event, with the region experiencing the warmest July-August on record. To explore how human-induced greenhouse gas emissions and natural internal variability contributed to this heat event, we compare observed July-August mean surface air temperature wit h that simulated by climate models. We find that both atmospheric natural variability and anthropogenic factors contributed to this heat event. This extreme warm midsummer was associated with a positive high-pressure anomaly that was closely related to the stochastic behavior of atmospheric circulation. Diagnosis of CMIP5 models and largemore » ensembles of two atmospheric models indicates that human influence has substantially increased the chance of warm mid-summers such as 2013 in CEC, although the exact estimated increase depends on the selection of climate models.« less

The western Pacific monsoon in CMIP5 models: Model evaluation and projections

NASA Astrophysics Data System (ADS)

Brown, Josephine R.; Colman, Robert A.; Moise, Aurel F.; Smith, Ian N.

2013-11-01

ability of 35 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to simulate the western Pacific (WP) monsoon is evaluated over four representative regions around Timor, New Guinea, the Solomon Islands and Palau. Coupled model simulations are compared with atmosphere-only model simulations (with observed sea surface temperatures, SSTs) to determine the impact of SST biases on model performance. Overall, the CMIP5 models simulate the WP monsoon better than previous-generation Coupled Model Intercomparison Project Phase 3 (CMIP3) models, but some systematic biases remain. The atmosphere-only models are better able to simulate the seasonal cycle of zonal winds than the coupled models, but display comparable biases in the rainfall. The CMIP5 models are able to capture features of interannual variability in response to the El Niño-Southern Oscillation. In climate projections under the RCP8.5 scenario, monsoon rainfall is increased over most of the WP monsoon domain, while wind changes are small. Widespread rainfall increases at low latitudes in the summer hemisphere appear robust as a large majority of models agree on the sign of the change. There is less agreement on rainfall changes in winter. Interannual variability of monsoon wet season rainfall is increased in a warmer climate, particularly over Palau, Timor and the Solomon Islands. A subset of the models showing greatest skill in the current climate confirms the overall projections, although showing markedly smaller rainfall increases in the western equatorial Pacific. The changes found here may have large impacts on Pacific island countries influenced by the WP monsoon.

(Un)certainty in climate change impacts on global energy consumption

NASA Astrophysics Data System (ADS)

van Ruijven, B. J.; De Cian, E.; Sue Wing, I.

2017-12-01

Climate change is expected to have an influence on the energy sector, especially on energy demand. For many locations, this change in energy demand is a balance between increase of demand for space cooling and a decrease of space heating demand. We perform a large-scale uncertainty analysis to characterize climate change risk on energy consumption as driven by climate and socioeconomic uncertainty. We combine a dynamic econometric model1 with multiple realizations of temperature projections from all 21 CMIP5 models (from the NASA Earth Exchange Global Daily Downscaled Projections2) under moderate (RCP4.5) and vigorous (RCP8.5) warming. Global spatial population projections for five SSPs are combined with GDP projections to construct scenarios for future energy demand driven by socioeconomic change. Between the climate models, we find a median global increase in climate-related energy demand of around 24% by 2050 under RCP8.5 with an interquartile range of 18-38%. Most climate models agree on increases in energy demand of more than 25% or 50% in tropical regions, the Southern USA and Southern China (see Figure). With respect to socioeconomic scenarios, we find wide variations between the SSPs for the number of people in low-income countries who are exposed to increases in energy demand. Figure attached: Number of models that agree on total climate-related energy consumption to increase or decrease by more than 0, 10, 25 or 50% by 2050 under RCP8.5 and SSP5 as result of the CMIP5 ensemble of temperature projections. References1. De Cian, E. & Sue Wing, I. Global Energy Demand in a Warming Climate. (FEEM, 2016). 2. Thrasher, B., Maurer, E. P., McKellar, C. & Duffy, P. B. Technical Note: Bias correcting climate model simulated daily temperature extremes with quantile mapping. Hydrol Earth Syst Sci 16, 3309-3314 (2012).

Tropical Carbon Response to Seasonal Phasing and Intensity of Precipitation in CMIP5 Earth System Models

NASA Astrophysics Data System (ADS)

Basile, S.; Keppel-Aleks, G.

2016-12-01

Carbon cycling and water fluxes are connected over land. Understanding the current sensitivity of tropical ecosystems to climate drivers, such as precipitation, at short timescales is important for projecting future trends in the land sink of anthropogenic CO2. Several recent studies have shown that interannual droughts in 2005 and 2010 reduced net carbon uptake in the Amazon rainforest. In 2011 Southern Hemisphere semi-arid regions, especially Australian ecosystems, were found to largely contribute to the above average increase in the land carbon sink following consecutive wet seasons under La Nina conditions. Earth system models (ESMs) are able to simulate these sensitivities with varying degrees of fidelity, and ESMs also show a wide range of changes in precipitation phasing and intensity by 2100. Unsurprisingly, model projections of the land carbon sink also vary widely, with some simulations showing land becoming a CO2 source to the atmosphere. To constrain projections of the tropical land carbon balance among an ensemble of ESMs, we analyzed seasonal and interannual precipitation-carbon relationships in Coupled Model Intercomparison Project Phase 5 (CMIP5) ESMs for the period from 1982-2006. The sensitivity of net biospheric production on land (NBP) to precipitation was quantified on seasonal and annual timescales, and NBP was spatially correlated to precipitation across tropical and subtropical regions (+/- 30 degrees) within humid and semi-arid ecosystems. This analysis was expanded to soil moisture and drought metrics were used to distinguish between wet and dry seasons. Large scale precipitation was used to resolve Intertropical Convergence Zone (ITCZ) movement and convective precipitation was used to diagnose the short-term NBP response within the wet season. Results revealed a spread in NBP sensitivity to precipitation intensity as well as how individual models simulated precipitation phasing across different tropical regions.

Climate Change Impacts on the Upper Indus Hydrology: Sources, Shifts and Extremes

PubMed Central

Immerzeel, W. W.; Kraaijenbrink, P. D. A.; Shrestha, A. B.; Bierkens, M. F. P.

2016-01-01

The Indus basin heavily depends on its upstream mountainous part for the downstream supply of water while downstream demands are high. Since downstream demands will likely continue to increase, accurate hydrological projections for the future supply are important. We use an ensemble of statistically downscaled CMIP5 General Circulation Model outputs for RCP4.5 and RCP8.5 to force a cryospheric-hydrological model and generate transient hydrological projections for the entire 21st century for the upper Indus basin. Three methodological advances are introduced: (i) A new precipitation dataset that corrects for the underestimation of high-altitude precipitation is used. (ii) The model is calibrated using data on river runoff, snow cover and geodetic glacier mass balance. (iii) An advanced statistical downscaling technique is used that accounts for changes in precipitation extremes. The analysis of the results focuses on changes in sources of runoff, seasonality and hydrological extremes. We conclude that the future of the upper Indus basin’s water availability is highly uncertain in the long run, mainly due to the large spread in the future precipitation projections. Despite large uncertainties in the future climate and long-term water availability, basin-wide patterns and trends of seasonal shifts in water availability are consistent across climate change scenarios. Most prominent is the attenuation of the annual hydrograph and shift from summer peak flow towards the other seasons for most ensemble members. In addition there are distinct spatial patterns in the response that relate to monsoon influence and the importance of meltwater. Analysis of future hydrological extremes reveals that increases in intensity and frequency of extreme discharges are very likely for most of the upper Indus basin and most ensemble members. PMID:27828994

Climate Change Impacts on the Upper Indus Hydrology: Sources, Shifts and Extremes.

PubMed

Lutz, A F; Immerzeel, W W; Kraaijenbrink, P D A; Shrestha, A B; Bierkens, M F P

2016-01-01

The Indus basin heavily depends on its upstream mountainous part for the downstream supply of water while downstream demands are high. Since downstream demands will likely continue to increase, accurate hydrological projections for the future supply are important. We use an ensemble of statistically downscaled CMIP5 General Circulation Model outputs for RCP4.5 and RCP8.5 to force a cryospheric-hydrological model and generate transient hydrological projections for the entire 21st century for the upper Indus basin. Three methodological advances are introduced: (i) A new precipitation dataset that corrects for the underestimation of high-altitude precipitation is used. (ii) The model is calibrated using data on river runoff, snow cover and geodetic glacier mass balance. (iii) An advanced statistical downscaling technique is used that accounts for changes in precipitation extremes. The analysis of the results focuses on changes in sources of runoff, seasonality and hydrological extremes. We conclude that the future of the upper Indus basin's water availability is highly uncertain in the long run, mainly due to the large spread in the future precipitation projections. Despite large uncertainties in the future climate and long-term water availability, basin-wide patterns and trends of seasonal shifts in water availability are consistent across climate change scenarios. Most prominent is the attenuation of the annual hydrograph and shift from summer peak flow towards the other seasons for most ensemble members. In addition there are distinct spatial patterns in the response that relate to monsoon influence and the importance of meltwater. Analysis of future hydrological extremes reveals that increases in intensity and frequency of extreme discharges are very likely for most of the upper Indus basin and most ensemble members.

Evaluation of simulated ocean carbon in the CMIP5 earth system models

NASA Astrophysics Data System (ADS)

Orr, James; Brockmann, Patrick; Seferian, Roland; Servonnat, Jérôme; Bopp, Laurent

2013-04-01

We maintain a centralized model output archive containing output from the previous generation of Earth System Models (ESMs), 7 models used in the IPCC AR4 assessment. Output is in a common format located on a centralized server and is publicly available through a web interface. Through the same interface, LSCE/IPSL has also made available output from the Coupled Model Intercomparison Project (CMIP5), the foundation for the ongoing IPCC AR5 assessment. The latter includes ocean biogeochemical fields from more than 13 ESMs. Modeling partners across 3 EU projects refer to the combined AR4-AR5 archive and comparison as OCMIP5, building on previous phases of OCMIP (Ocean Carbon Cycle Intercomparison Project) and making a clear link to IPCC AR5 (CMIP5). While now focusing on assessing the latest generation of results (AR5, CMIP5), this effort is also able to put them in context (AR4). For model comparison and evaluation, we have also stored computed derived variables (e.g., those needed to assess ocean acidification) and key fields regridded to a common 1°x1° grid, thus complementing the standard CMIP5 archive. The combined AR4-AR5 output (OCMIP5) has been used to compute standard quantitative metrics, both global and regional, and those have been synthesized with summary diagrams. In addition, for key biogeochemical fields we have deconvolved spatiotemporal components of the mean square error in order to constrain which models go wrong where. Here we will detail results from these evaluations which have exploited gridded climatological data. The archive, interface, and centralized evaluation provide a solid technical foundation, upon which collaboration and communication is being broadened in the ocean biogeochemical modeling community. Ultimately we aim to encourage wider use of the OCMIP5 archive.

Mapping groundwater contamination risk of multiple aquifers using multi-model ensemble of machine learning algorithms.

PubMed

Barzegar, Rahim; Moghaddam, Asghar Asghari; Deo, Ravinesh; Fijani, Elham; Tziritis, Evangelos

2018-04-15

Constructing accurate and reliable groundwater risk maps provide scientifically prudent and strategic measures for the protection and management of groundwater. The objectives of this paper are to design and validate machine learning based-risk maps using ensemble-based modelling with an integrative approach. We employ the extreme learning machines (ELM), multivariate regression splines (MARS), M5 Tree and support vector regression (SVR) applied in multiple aquifer systems (e.g. unconfined, semi-confined and confined) in the Marand plain, North West Iran, to encapsulate the merits of individual learning algorithms in a final committee-based ANN model. The DRASTIC Vulnerability Index (VI) ranged from 56.7 to 128.1, categorized with no risk, low and moderate vulnerability thresholds. The correlation coefficient (r) and Willmott's Index (d) between NO 3 concentrations and VI were 0.64 and 0.314, respectively. To introduce improvements in the original DRASTIC method, the vulnerability indices were adjusted by NO 3 concentrations, termed as the groundwater contamination risk (GCR). Seven DRASTIC parameters utilized as the model inputs and GCR values utilized as the outputs of individual machine learning models were served in the fully optimized committee-based ANN-predictive model. The correlation indicators demonstrated that the ELM and SVR models outperformed the MARS and M5 Tree models, by virtue of a larger d and r value. Subsequently, the r and d metrics for the ANN-committee based multi-model in the testing phase were 0.8889 and 0.7913, respectively; revealing the superiority of the integrated (or ensemble) machine learning models when compared with the original DRASTIC approach. The newly designed multi-model ensemble-based approach can be considered as a pragmatic step for mapping groundwater contamination risks of multiple aquifer systems with multi-model techniques, yielding the high accuracy of the ANN committee-based model. Copyright © 2017 Elsevier B.V. All rights reserved.

CMIP6 Citation Services and the Data Services of the IPCC Data Distribution Centre for AR6

NASA Astrophysics Data System (ADS)

Stockhause, Martina; Lautenschlager, Michael

2017-04-01

As a result of the experiences from CMIP5 the two services contributed by DKRZ to the CMIP research infrastructure have been improved for CMIP6: the Citation Services and the Services of the IPCC Data Distribution Centre (DDC, http://ipcc-data.org). 1. Data Citation Services: Within CMIP5 it took a couple of years before the data was citable with their DataCite DOIs. The DataCite DOI registration by the WDC Climate at DKRZ (World Data Center Climate at the Climate Computing Center) requires data transfer and long-term archival at DKRZ according to DDC's quality standards. Based on a request from WGCM (Working Group on Climate Models) an additional early citation possibility for the evolving CMIP6 data was added to the citation service (http://cmip6cite.wdc-climate.de). 2. IPCC DDC Services: WDC Climate has been hosting the IPCC DDC's Reference Data Archive for the climate model output underlying the IPCC Assessment Reports (ARs) since the Second Assessment Report in 1995. One task of the DDC is the support of the IPCC Working Groups (WGs) and their authors. The WG support was not sufficient for AR5 resulting in WG I setting up and maintaining their own CMIP5 data repository hosting a data subset. The DDC will open DKRZ's CMIP data pool as an additional DDC service for the IPCC authors using a synergy with the interests of the national climate community. Within the PICO the Citation and the IPCC DDC services will be presented from a user's perspective. The connections to and integration into the infrastructure for CMIP6 (see https://www.earthsystemcog.org/projects/wip/) will be explained.

Hydrological and biogeochemical constraints on terrestrial carbon cycle feedbacks

NASA Astrophysics Data System (ADS)

Mystakidis, Stefanos; Seneviratne, Sonia I.; Gruber, Nicolas; Davin, Edouard L.

2017-01-01

The feedbacks between climate, atmospheric CO2 concentration and the terrestrial carbon cycle are a major source of uncertainty in future climate projections with Earth systems models. Here, we use observation-based estimates of the interannual variations in evapotranspiration (ET), net biome productivity (NBP), as well as the present-day sensitivity of NBP to climate variations, to constrain globally the terrestrial carbon cycle feedbacks as simulated by models that participated in the fifth phase of the coupled model intercomparison project (CMIP5). The constraints result in a ca. 40% lower response of NBP to climate change and a ca. 30% reduction in the strength of the CO2 fertilization effect relative to the unconstrained multi-model mean. While the unconstrained CMIP5 models suggest an increase in the cumulative terrestrial carbon storage (477 PgC) in response to an idealized scenario of 1%/year atmospheric CO2 increase, the constraints imply a ca. 19% smaller change. Overall, the applied emerging constraint approach offers a possibility to reduce uncertainties in the projections of the terrestrial carbon cycle, which is a key determinant of the future trajectory of atmospheric CO2 concentration and resulting climate change.

Percentile-Based ETCCDI Temperature Extremes Indices for CMIP5 Model Output: New Results through Semiparametric Quantile Regression Approach

NASA Astrophysics Data System (ADS)

Li, L.; Yang, C.

2017-12-01

Climate extremes often manifest as rare events in terms of surface air temperature and precipitation with an annual reoccurrence period. In order to represent the manifold characteristics of climate extremes for monitoring and analysis, the Expert Team on Climate Change Detection and Indices (ETCCDI) had worked out a set of 27 core indices based on daily temperature and precipitation data, describing extreme weather and climate events on an annual basis. The CLIMDEX project (http://www.climdex.org) had produced public domain datasets of such indices for data from a variety of sources, including output from global climate models (GCM) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Among the 27 ETCCDI indices, there are six percentile-based temperature extremes indices that may fall into two groups: exceedance rates (ER) (TN10p, TN90p, TX10p and TX90p) and durations (CSDI and WSDI). Percentiles must be estimated prior to the calculation of the indices, and could more or less be biased by the adopted algorithm. Such biases will in turn be propagated to the final results of indices. The CLIMDEX used an empirical quantile estimator combined with a bootstrap resampling procedure to reduce the inhomogeneity in the annual series of the ER indices. However, there are still some problems remained in the CLIMDEX datasets, namely the overestimated climate variability due to unaccounted autocorrelation in the daily temperature data, seasonally varying biases and inconsistency between algorithms applied to the ER indices and to the duration indices. We now present new results of the six indices through a semiparametric quantile regression approach for the CMIP5 model output. By using the base-period data as a whole and taking seasonality and autocorrelation into account, this approach successfully addressed the aforementioned issues and came out with consistent results. The new datasets cover the historical and three projected (RCP2.6, RCP4.5 and RCP8.5) emission scenarios run a multimodel ensemble of 19 members. We analyze changes in the six indices on global and regional scales over the 21st century relative to either the base period 1961-1990 or the reference period 1981-2000, and compare the results with those based on the CLIMDEX datasets.

Carbon-nitrogen interactions in idealized simulations with JSBACH (version 3.10)

NASA Astrophysics Data System (ADS)

Goll, Daniel S.; Winkler, Alexander J.; Raddatz, Thomas; Dong, Ning; Prentice, Ian Colin; Ciais, Philippe; Brovkin, Victor

2017-05-01

Recent advances in the representation of soil carbon decomposition and carbon-nitrogen interactions implemented previously into separate versions of the land surface scheme JSBACH are here combined in a single version, which is set to be used in the upcoming 6th phase of coupled model intercomparison project (CMIP6).Here we demonstrate that the new version of JSBACH is able to reproduce the spatial variability in the reactive nitrogen-loss pathways as derived from a compilation of δ15N data (R = 0. 76, root mean square error (RMSE) = 0. 2, Taylor score = 0. 83). The inclusion of carbon-nitrogen interactions leads to a moderate reduction (-10 %) of the carbon-concentration feedback (βL) and has a negligible effect on the sensitivity of the land carbon cycle to warming (γL) compared to the same version of the model without carbon-nitrogen interactions in idealized simulations (1 % increase in atmospheric carbon dioxide per year). In line with evidence from elevated carbon dioxide manipulation experiments, pronounced nitrogen scarcity is alleviated by (1) the accumulation of nitrogen due to enhanced nitrogen inputs by biological nitrogen fixation and reduced losses by leaching and volatilization. Warming stimulated turnover of organic nitrogen further counteracts scarcity.The strengths of the land carbon feedbacks of the recent version of JSBACH, with βL = 0. 61 Pg ppm-1 and γL = -27. 5 Pg °C-1, are 34 and 53 % less than the averages of CMIP5 models, although the CMIP5 version of JSBACH simulated βL and γL, which are 59 and 42 % higher than multi-model average. These changes are primarily due to the new decomposition model, indicating the importance of soil organic matter decomposition for land carbon feedbacks.

Improving Climate Projections Using "Intelligent" Ensembles

NASA Technical Reports Server (NTRS)

Baker, Noel C.; Taylor, Patrick C.

2015-01-01

Recent changes in the climate system have led to growing concern, especially in communities which are highly vulnerable to resource shortages and weather extremes. There is an urgent need for better climate information to develop solutions and strategies for adapting to a changing climate. Climate models provide excellent tools for studying the current state of climate and making future projections. However, these models are subject to biases created by structural uncertainties. Performance metrics-or the systematic determination of model biases-succinctly quantify aspects of climate model behavior. Efforts to standardize climate model experiments and collect simulation data-such as the Coupled Model Intercomparison Project (CMIP)-provide the means to directly compare and assess model performance. Performance metrics have been used to show that some models reproduce present-day climate better than others. Simulation data from multiple models are often used to add value to projections by creating a consensus projection from the model ensemble, in which each model is given an equal weight. It has been shown that the ensemble mean generally outperforms any single model. It is possible to use unequal weights to produce ensemble means, in which models are weighted based on performance (called "intelligent" ensembles). Can performance metrics be used to improve climate projections? Previous work introduced a framework for comparing the utility of model performance metrics, showing that the best metrics are related to the variance of top-of-atmosphere outgoing longwave radiation. These metrics improve present-day climate simulations of Earth's energy budget using the "intelligent" ensemble method. The current project identifies several approaches for testing whether performance metrics can be applied to future simulations to create "intelligent" ensemble-mean climate projections. It is shown that certain performance metrics test key climate processes in the models, and that these metrics can be used to evaluate model quality in both current and future climate states. This information will be used to produce new consensus projections and provide communities with improved climate projections for urgent decision-making.

Assessment and simulation of global terrestrial latent heat flux by synthesis of CMIP5 climate models and surface eddy covariance observations

Treesearch

Yunjun Yao; Shunlin Liang; Xianglan Li; Shaomin Liu; Jiquan Chen; Xiaotong Zhang; Kun Jia; Bo Jiang; Xianhong Xie; Simon Munier; Meng Liu; Jian Yu; Anders Lindroth; Andrej Varlagin; Antonio Raschi; Asko Noormets; Casimiro Pio; Georg Wohlfahrt; Ge Sun; Jean-Christophe Domec; Leonardo Montagnani; Magnus Lund; Moors Eddy; Peter D. Blanken; Thomas Grunwald; Sebastian Wolf; Vincenzo Magliulo

2016-01-01

The latent heat flux (LE) between the terrestrial biosphere and atmosphere is a major driver of the globalhydrological cycle. In this study, we evaluated LE simulations by 45 general circulation models (GCMs)in the Coupled Model Intercomparison Project Phase 5 (CMIP5) by a comparison...

Aeronomic Impacts of a Revision to the Solar Irradiance Forcing for CMIP6

NASA Astrophysics Data System (ADS)

Marsh, D. R.; Chiodo, G.

2016-12-01

In preparation for the sixth phase of the Coupled Model Intercomparison Project (CMIP6), a revised solar forcing dataset has been the assembled as part of the Solar Influences activity of the Stratospheretroposphere Processes And their Role in Climate (SPARC) project. The new dataset differs significantly from the previous dataset used by CMIP5 models in the distribution of the mean solar spectral irradiance, particularly in the ultraviolet (UV). For example, in the 300 to 350 nm band the irradiance in the new model is reduced by approximately 0.7 Wm2. To put this in perspective, that change amounts to approximately 4 to 6 times the magnitude of the solar cycle variation in that band. Using the NCAR Whole Atmosphere Community Climate Model (WACCM), we assess the impact on stratospheric composition and dynamics of this revision to the solar irradiance by comparing WACCM experiments that are forced by either the CMIP5 or CMIP6 solar forcing dataset. We find that ozone in the middle stratosphere decreases by approximately 3% in the experiments forced with the CMIP6 dataset. At the stratopause ozone increases by over 1.6% in response to a 2% decrease in odd-hydrogen species (HOx = {H, OH and HO2} ) above 35 km. HOx reductions are caused by a decrease in the Hartley band irradiance that creates O(1D) from ozone photolysis; the reaction with O(1D) being the primary way in which H2O is converted to HOx. The reduction in UV irradiance in the CMIP6 forcing dataset also leads to a cooling of the stratosphere and lower mesosphere of up to 1.6K. Considering that smaller irradiance changes that occur over the solar cycle have been implicated in changes in surface climate, our study suggest that the mean state of climate models used in CMIP6 may be significantly different than those used in CMIP5, as a result of changes in the mean solar irradiance forcing.

Uncertainty Quantification in Climate Modeling and Projection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qian, Yun; Jackson, Charles; Giorgi, Filippo

The projection of future climate is one of the most complex problems undertaken by the scientific community. Although scientists have been striving to better understand the physical basis of the climate system and to improve climate models, the overall uncertainty in projections of future climate has not been significantly reduced (e.g., from the IPCC AR4 to AR5). With the rapid increase of complexity in Earth system models, reducing uncertainties in climate projections becomes extremely challenging. Since uncertainties always exist in climate models, interpreting the strengths and limitations of future climate projections is key to evaluating risks, and climate change informationmore » for use in Vulnerability, Impact, and Adaptation (VIA) studies should be provided with both well-characterized and well-quantified uncertainty. The workshop aimed at providing participants, many of them from developing countries, information on strategies to quantify the uncertainty in climate model projections and assess the reliability of climate change information for decision-making. The program included a mixture of lectures on fundamental concepts in Bayesian inference and sampling, applications, and hands-on computer laboratory exercises employing software packages for Bayesian inference, Markov Chain Monte Carlo methods, and global sensitivity analyses. The lectures covered a range of scientific issues underlying the evaluation of uncertainties in climate projections, such as the effects of uncertain initial and boundary conditions, uncertain physics, and limitations of observational records. Progress in quantitatively estimating uncertainties in hydrologic, land surface, and atmospheric models at both regional and global scales was also reviewed. The application of Uncertainty Quantification (UQ) concepts to coupled climate system models is still in its infancy. The Coupled Model Intercomparison Project (CMIP) multi-model ensemble currently represents the primary data for assessing reliability and uncertainties of climate change information. An alternative approach is to generate similar ensembles by perturbing parameters within a single-model framework. One of workshop’s objectives was to give participants a deeper understanding of these approaches within a Bayesian statistical framework. However, there remain significant challenges still to be resolved before UQ can be applied in a convincing way to climate models and their projections.« less

Projections of long-term changes in solar radiation based on CMIP5 climate models and their influence on energy yields of photovoltaic systems

NASA Astrophysics Data System (ADS)

Wild, Martin; Folini, Doris; Henschel, Florian; Müller, Björn

2015-04-01

Traditionally, for the planning and assessment of solar energy systems, the amount of solar radiation (sunlight) incident on the Earth's surface is assumed to be constant over the years. However, with changing climate and air pollution levels, solar resources may no longer be stable over time and undergo substantial decadal changes. Observational records covering the past decades confirm long-term changes in this quantity. Here we examine, how the latest generation of climate models used for the 5th IPCC report projects potential changes in surface solar radiation over the coming decades, and how this may affect, in combination with the expected greenhouse warming, solar power output from photovoltaic (PV) systems. For this purpose, projections up to the mid 21th century from 39 state of the art climate models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are analysed globally and for selected key regions with major solar power production capacity. The large model ensemble allows to assess the degree of consistency of their projections. Models are largely consistent in the sign of the projected changes in solar radiation under cloud-free conditions as well as in surface temperatures over most of the globe, while still reasonably consistent over a considerable part of the globe in the sign of changes in cloudiness and associated changes in solar radiation. A first order estimate of the impact of solar radiation and temperature changes on energy yields of PV systems under the RPC8.5 scenario indicates statistically significant decreases in PV outputs in large parts of the world, but notable exceptions with positive trends in parts of Europe and the South-East of China. Projected changes between 2006 and 2049 under the RCP8.5 scenario overall are on the order of 1 % per decade for horizontal planes, but may be larger for tilted or tracked planes as well as on shorter (decadal) timescales. Related References: Wild, M., Folini, D., Henschel, F., and Müller, B. 2015: Projections of long-term changes in solar radiation based on CMIP5 climate models and their influence on energy yields of photovoltaic systems, submitted. Muller, B., Wild, M., Driesse, A., and Behrens, K., 2014: Rethinking solar resource assessments in the context of global dimming and brightening, Solar Energy, 99, 272-282. Wild, M. 2012: Enlightening Global Dimming and Brightening. Bull. Amer. Meteor. Soc., 93, 27-37, doi:10.1175/BAMS-D-11-00074.1

Climate Change Impacts on Peak Electricity Consumption: US vs. Europe.

NASA Astrophysics Data System (ADS)

Auffhammer, M.

2016-12-01

It has been suggested that climate change impacts on the electric sector will account for the majority of global economic damages by the end of the current century and beyond. This finding is at odds with the relatively modest increase in climate driven impacts on consumption. Comprehensive high frequency load balancing authority level data have not been used previously to parameterize the relationship between electric demand and temperature for any major economy. Using statistical models we analyze multi-year data from load balancing authorities in the United States of America and the European Union, which are responsible for more than 90% of the electricity delivered to residential, industrial, commercial and agricultural customers. We couple the estimated response functions between total daily consumption and daily peak load with an ensemble of downscaled GCMs from the CMIP5 archive to simulate climate change driven impacts on both outcomes. We show moderate and highly spatially heterogeneous changes in consumption. The results of our peak load simulations, however, suggest significant changes in the intensity and frequency of peak events throughout the United States and Europe. As the electricity grid is built to endure maximum load, which usually occurs on the hottest day of the year, our findings have significant implications for the construction of costly peak generating and transmission capacity.

Interhemispheric Temperature Asymmetry in Historical Observations and Future Projections

NASA Astrophysics Data System (ADS)

Friedman, A. R.; Hwang, Y.; Chiang, J. C.; Frierson, D. M.

2013-12-01

The surface temperature contrast between the northern and southern hemispheres -- the interhemispheric temperature asymmetry (ITA) -- is an emerging indicator of global climate change, especially relevant to the latitude of the tropical rain bands. We investigate the ITA over historical observations and in Coupled Model Intercomparison Project phase 5 (CMIP5) historical simulations and future projections. We find that the uneven spatial impacts of greenhouse gas forcing cause amplified warming in the Arctic and northern landmasses, resulting in an increase of the ITA. However, anthropogenic sulfate aerosols, which are disproportionately emitted in the northern hemisphere, masked these effects on the ITA until around 1980. The implementation of air pollution regulations in North America and Europe combined with increased global emissions of greenhouse gases have resulted in a significant positive ITA trend since 1980. The CMIP5 historical multimodel ensembles simulate this positive ITA trend, though not its full magnitude. We explore how natural variability may account for some of the differences between the simulated and observed ITA. Future simulations project a substantial increase of the ITA over the twenty-first century, well outside its twentieth-century variability. This is largely in response to continued greenhouse gas emissions, though anthropogenic aerosol emissions are also important in some scenarios. We discuss the potential implications of this northern warming in causing a northward shift in tropical rainfall.

Statistical post-processing of seasonal multi-model forecasts: Why is it so hard to beat the multi-model mean?

NASA Astrophysics Data System (ADS)

Siegert, Stefan

2017-04-01

Initialised climate forecasts on seasonal time scales, run several months or even years ahead, are now an integral part of the battery of products offered by climate services world-wide. The availability of seasonal climate forecasts from various modeling centres gives rise to multi-model ensemble forecasts. Post-processing such seasonal-to-decadal multi-model forecasts is challenging 1) because the cross-correlation structure between multiple models and observations can be complicated, 2) because the amount of training data to fit the post-processing parameters is very limited, and 3) because the forecast skill of numerical models tends to be low on seasonal time scales. In this talk I will review new statistical post-processing frameworks for multi-model ensembles. I will focus particularly on Bayesian hierarchical modelling approaches, which are flexible enough to capture commonly made assumptions about collective and model-specific biases of multi-model ensembles. Despite the advances in statistical methodology, it turns out to be very difficult to out-perform the simplest post-processing method, which just recalibrates the multi-model ensemble mean by linear regression. I will discuss reasons for this, which are closely linked to the specific characteristics of seasonal multi-model forecasts. I explore possible directions for improvements, for example using informative priors on the post-processing parameters, and jointly modelling forecasts and observations.

Predicting lymphatic filariasis transmission and elimination dynamics using a multi-model ensemble framework.

PubMed

Smith, Morgan E; Singh, Brajendra K; Irvine, Michael A; Stolk, Wilma A; Subramanian, Swaminathan; Hollingsworth, T Déirdre; Michael, Edwin

2017-03-01

Mathematical models of parasite transmission provide powerful tools for assessing the impacts of interventions. Owing to complexity and uncertainty, no single model may capture all features of transmission and elimination dynamics. Multi-model ensemble modelling offers a framework to help overcome biases of single models. We report on the development of a first multi-model ensemble of three lymphatic filariasis (LF) models (EPIFIL, LYMFASIM, and TRANSFIL), and evaluate its predictive performance in comparison with that of the constituents using calibration and validation data from three case study sites, one each from the three major LF endemic regions: Africa, Southeast Asia and Papua New Guinea (PNG). We assessed the performance of the respective models for predicting the outcomes of annual MDA strategies for various baseline scenarios thought to exemplify the current endemic conditions in the three regions. The results show that the constructed multi-model ensemble outperformed the single models when evaluated across all sites. Single models that best fitted calibration data tended to do less well in simulating the out-of-sample, or validation, intervention data. Scenario modelling results demonstrate that the multi-model ensemble is able to compensate for variance between single models in order to produce more plausible predictions of intervention impacts. Our results highlight the value of an ensemble approach to modelling parasite control dynamics. However, its optimal use will require further methodological improvements as well as consideration of the organizational mechanisms required to ensure that modelling results and data are shared effectively between all stakeholders. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Trends in winter circulation over the British Isles and central Europe in twenty-first century projections by 25 CMIP5 GCMs

NASA Astrophysics Data System (ADS)

Stryhal, Jan; Huth, Radan

2018-03-01

Winter midlatitude atmospheric circulation has been extensively studied for its tight link to surface weather, and automated circulation classifications have often been used to this end. Here, eight such classifications are applied to daily sea level pressure patterns simulated by an ensemble of CMIP5 GCMs twenty-first century projections for the British Isles and central Europe in order to robustly estimate future changes in frequency, persistence, and strength of synoptic-scale circulation there. All methods are able to identify present-day biases of models reported before, such as an overestimated occurrence of zonal flow and underestimation of anticyclonic conditions and easterly advection, although the strength of these biases varies among the methods. In future, models show that the zonal flow will become more frequent while the strength of the mean flow is not projected to change. Over the British Isles, the models that better simulate the latitude of zonal flow over the historical period indicate a slight equatorward shift of westerlies in their projections, while the poleward expansion of circulation—expected in future at global scale—is apparent in those models that have large errors. Over central Europe, some classifications indicate an increase in persistence and especially in frequency of anticyclonic types, which is, however, shown to be rather an artifact of some methods than a real feature. On the other hand, the easterly flow is robustly projected to become markedly weaker in central Europe, which we hypothesize might be an important factor contributing to the projected decrease of cold extremes there.

ENSO-Related Precipitation and Its Statistical Relationship with the Walker Circulation Trend in CMIP5 AMIP Models

DOE PAGES

Yim, Bo; Yeh, Sang -Wook; Sohn, Byung -Ju

2016-01-29

Observational evidence shows that the Walker circulation (WC) in the tropical Pacific has strengthened in recent decades. In this study, we examine the WC trend for 1979–2005 and its relationship with the precipitation associated with the El Niño Southern Oscillation (ENSO) using the sea surface temperature (SST)-constrained Atmospheric Model Intercomparison Project (AMIP) simulations of the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. All of the 29 models show a strengthening of the WC trend in response to an increase in the SST zonal gradient along the equator. Despite the same SST-constrained AMIP simulations, however, a large diversity ismore » found among the CMIP5 climate models in the magnitude of the WC trend. The relationship between the WC trend and precipitation anomalies (PRCPAs) associated with ENSO (ENSO-related PRCPAs) shows that the longitudinal position of the ENSO-related PRCPAs in the western tropical Pacific is closely related to the magnitude of the WC trend. Specifically, it is found that the strengthening of the WC trend is large (small) in the CMIP5 AMIP simulations in which the ENSO-related PRCPAs are located relatively westward (eastward) in the western tropical Pacific. Furthermore, the zonal shift of the ENSO-related precipitation in the western tropical Pacific, which is associated with the climatological mean precipitation in the tropical Pacific, could play an important role in modifying the WC trend in the CMIP5 climate models.« less

Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions

USGS Publications Warehouse

Viney, N.R.; Bormann, H.; Breuer, L.; Bronstert, A.; Croke, B.F.W.; Frede, H.; Graff, T.; Hubrechts, L.; Huisman, J.A.; Jakeman, A.J.; Kite, G.W.; Lanini, J.; Leavesley, G.; Lettenmaier, D.P.; Lindstrom, G.; Seibert, J.; Sivapalan, M.; Willems, P.

2009-01-01

This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in multi-model ensembles. The reasons behind these observations may relate to the effects of the weighting schemes, non-stationarity of the climate series and possible cross-correlations between models. Crown Copyright ?? 2008.

Evaluation of a new satellite-based precipitation dataset for climate studies in the Xiang River basin, Southern China

NASA Astrophysics Data System (ADS)

Zhu, Q.; Xu, Y. P.; Hsu, K. L.

2017-12-01

A new satellite-based precipitation dataset, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) with long-term time series dating back to 1983 can be one valuable dataset for climate studies. This study investigates the feasibility of using PERSIANN-CDR as a reference dataset for climate studies. Sixteen CMIP5 models are evaluated over the Xiang River basin, southern China, by comparing their performance on precipitation projection and streamflow simulation, particularly on extreme precipitation and streamflow events. The results show PERSIANN-CDR is a valuable dataset for climate studies, even on extreme precipitation events. The precipitation estimates and their extreme events from CMIP5 models are improved significantly compared with rain gauge observations after bias-correction by the PERSIANN-CDR precipitation estimates. Given streamflows simulated with raw and bias-corrected precipitation estimates from 16 CMIP5 models, 10 out of 16 are improved after bias-correction. The impact of bias-correction on extreme events for streamflow simulations are unstable, with eight out of 16 models can be clearly claimed they are improved after the bias-correction. Concerning the performance of raw CMIP5 models on precipitation, IPSL-CM5A-MR excels the other CMIP5 models, while MRI-CGCM3 outperforms on extreme events with its better performance on six extreme precipitation metrics. Case studies also show that raw CCSM4, CESM1-CAM5, and MRI-CGCM3 outperform other models on streamflow simulation, while MIROC5-ESM-CHEM, MIROC5-ESM and IPSL-CM5A-MR behaves better than the other models after bias-correction.

The Canadian seasonal forecast and the APCC exchange.

NASA Astrophysics Data System (ADS)

Archambault, B.; Fontecilla, J.; Kharin, V.; Bourgouin, P.; Ashok, K.; Lee, D.

2009-05-01

In this talk, we will first describe the Canadian seasonal forecast system. This system uses a 4 model ensemble approach with each of these models generating a 10 members ensemble. Multi-model issues related to this system will be describes. Secondly, we will describe an international multi-system initiative. The Asia-Pacific Economic Cooperation (APEC) is a forum for 21 Pacific Rim countries or regions including Canada. The APEC Climate Center (APCC) provides seasonal forecasts to their regional climate centers with a Multi Model Ensemble (MME) approach. The APCC MME is based on 13 ensemble prediction systems from different institutions including MSC(Canada), NCEP(USA), COLA(USA), KMA(Korea), JMA(Japan), BOM(Australia) and others. In this presentation, we will describe the basics of this international cooperation.

Towards Systematic Benchmarking of Climate Model Performance

NASA Astrophysics Data System (ADS)

Gleckler, P. J.

2014-12-01

The process by which climate models are evaluated has evolved substantially over the past decade, with the Coupled Model Intercomparison Project (CMIP) serving as a centralizing activity for coordinating model experimentation and enabling research. Scientists with a broad spectrum of expertise have contributed to the CMIP model evaluation process, resulting in many hundreds of publications that have served as a key resource for the IPCC process. For several reasons, efforts are now underway to further systematize some aspects of the model evaluation process. First, some model evaluation can now be considered routine and should not require "re-inventing the wheel" or a journal publication simply to update results with newer models. Second, the benefit of CMIP research to model development has not been optimal because the publication of results generally takes several years and is usually not reproducible for benchmarking newer model versions. And third, there are now hundreds of model versions and many thousands of simulations, but there is no community-based mechanism for routinely monitoring model performance changes. An important change in the design of CMIP6 can help address these limitations. CMIP6 will include a small set standardized experiments as an ongoing exercise (CMIP "DECK": ongoing Diagnostic, Evaluation and Characterization of Klima), so that modeling groups can submit them at any time and not be overly constrained by deadlines. In this presentation, efforts to establish routine benchmarking of existing and future CMIP simulations will be described. To date, some benchmarking tools have been made available to all CMIP modeling groups to enable them to readily compare with CMIP5 simulations during the model development process. A natural extension of this effort is to make results from all CMIP simulations widely available, including the results from newer models as soon as the simulations become available for research. Making the results from routine performance tests readily accessible will help advance a more transparent model evaluation process.

The response of tropical cyclone activity to tropospheric aerosols, greenhouse gases and volcanic eruptions

NASA Astrophysics Data System (ADS)

Camargo, S. J.; Sobel, A. H.; Polvani, L. M.; Emanuel, K.; Previdi, M. J.

2017-12-01

Previous work has shown that aerosol cooling reduces tropical cyclone (TC) potential intensity (PI) more strongly than greenhouse gas warming increases it. This has the consequence that PI shows only small increases in simulations of the historical period despite considerable global warming over that period. We use CMIP5 models, as well as offline radiative kernels, to better understand this result. The outsize effect of aerosol forcing is a consequence of the fact that tropospheric aerosols act in the shortwave while greenhouse gases act in the longwave. Shortwave forcing has a greater impact on PI than does longwave, because of the differences in the response of the surface energy budget to the direct, temperature-independent component of the forcing. Shortwave forcing mainly drives the climate system in the surface, while greenhouse gases do so at the top of the atmosphere, so that net longwave flux associated with a temperature change can be small, especially at high temperature. Our kernel results also indicate that the temperature-dependent longwave feedback component is also greater by approximately a factor of two for the shortwave than the longwave forcing. Recent papers using observations and proxy reconstructions suggested a reduction of frequency, duration and intensity of Atlantic TCs in the years following volcanic eruptions. Observations show no significant reduction of TC activity in the first season after three large volcanic eruptions in the 20th Century, with the exception of the North Atlantic. The response to these volcanic eruptions cannot be separated from the coinciding El Niño events either in observations or in reanalysis. Both the NCAR Large Ensemble and CMIP5 models show a strong reduction in the PI following large volcanic eruptions. But, given that the models response to volcanic aerosols is known to be too strong, when a bias correction is considered, the PI signal after the volcanic eruptions becomes much smaller. Furthermore, there is no statistically significant reduction in TC activity for either the explicit synthetic downscaled CMIP5 storms following the volcanic eruptions. Therefore, there is little evidence of a global reduction of TC activity from direct volcanic aerosols.

Climate change projections of boreal summer precipitation over tropical America by using statistical downscaling from CMIP5 models

NASA Astrophysics Data System (ADS)

Palomino-Lemus, Reiner; Córdoba-Machado, Samir; Raquel Gámiz-Fortis, Sonia; Castro-Díez, Yolanda; Jesús Esteban-Parra, María

2017-12-01

Climate change projections for the last 30 years of the 21st century, for boreal summer precipitation in tropical America, have been made by developing a statistical downscaling (SD) model applied to the SLP outputs of 20 GCMs of CMIP5, for present climate (1970-2000), and for future (2071-2100) under the RCP2.6, RCP4.5 and RCP8.5 scenarios. For present climate, many SD GCMs faithfully reproduce the precipitation field in many regions of the study area. For future climate, as the radiative forcing increases, the projected changes intensify and the regions affected expand, with higher coherence between models. The zone between central and southeastern Brazil registered the most pronounced precipitation changes by a large number of SD models, even for the RCP2.6. Except for this region in Brazil, in general, the changes in rainfall range from moderate (± 25%) to intense (from ±70% to ±100%) as the radiative forcing increases from the RCP2.6-RCP8.5. For this latter scenario, all SD models present significant precipitation changes for more than 50% of the area, in some cases reaching 75% of area with significant changes. For the ensemble mean, the results show three extensive regions with significant changes under the three scenarios, the most highlighted changes being for the RCP8.5: a northwest-southeast band that extends from northern Mexico to eastern Brazil, crossing through northern Colombia, along with the regions in the south of the study area, with generally moderate precipitation increases; and a band that extends from eastern Ecuador to southeastern Brazil, with major decreasing changes. This pattern of change could be related with a possible strengthening in frequency in terms of La Niña events for the end of the century.

Impact of natural variability on the perception of climate change for the upcoming decades: Analysis of the CanESM2-LE and CESM-LE large ensembles

NASA Astrophysics Data System (ADS)

Rondeau-Genesse, G.; Braun, M.; Chaumont, D.

2017-12-01

The pace of climate change can have a direct impact on the efforts required to adapt. However, for relatively short time scales, this pace can be masked by natural variability (NV). In some cases, this variability might cause, for a few decades, climate change to exceed what would be expected from the greenhouse gas (GHG) emissions alone or, to the contrary, it might cause slowdowns or even hiatuses. This phenomenon is difficult to explore using ensembles such as CMIP5, which are composed of multiple climatological models and thus combine both NV and inter-model differences. This study analyses CanESM2-LE and CESM-LE, two state-of-the-art large ensembles (LE) comprised of multiple realizations from a single climatological model and a single GHG emission scenario. We explore the relationship between NV and climate change over the next few decades in Canada and the United States. Temperature indices, namely the mean annual temperature and the 3-day maximum and minimum temperatures are assessed. Results indicate that under the RCP8.5, temperatures within most of the individual large ensemble members will increase in a roughly linear manner between 2021 and 2060. Nevertheless, in some regions such as parts of Canada and Alaska, there is a 20 to 35% probability that the temperature increase will slow down between 2021 and 2040. Such a slowdown in warming temperatures would provide some leeway for adaptation projects, but this phenomenon is caused by NV alone and, as such, is only temporary. Indeed, members of the large ensembles where a slowdown of warming is found during the 2021-2040 period are two to five times more likely to experience a period of very fast warming in the following decades. The opposite scenario, where the changes expected by 2050 would occur early because of NV, remains fairly uncommon for the mean annual temperature. For the extreme temperature indices however, this early warming still occurs in 5 to 20% of the large ensemble members. As such, while our results indicate that the dominant pattern in Canada and the United States is a fairly linear warming, the chances for other patterns is non negligible for the upcoming decades. This reinforces the need for constant, uninterrupted efforts towards climate change adaptation.

Examination of multi-model ensemble seasonal prediction methods using a simple climate system

NASA Astrophysics Data System (ADS)

Kang, In-Sik; Yoo, Jin Ho

2006-02-01

A simple climate model was designed as a proxy for the real climate system, and a number of prediction models were generated by slightly perturbing the physical parameters of the simple model. A set of long (240 years) historical hindcast predictions were performed with various prediction models, which are used to examine various issues of multi-model ensemble seasonal prediction, such as the best ways of blending multi-models and the selection of models. Based on these results, we suggest a feasible way of maximizing the benefit of using multi models in seasonal prediction. In particular, three types of multi-model ensemble prediction systems, i.e., the simple composite, superensemble, and the composite after statistically correcting individual predictions (corrected composite), are examined and compared to each other. The superensemble has more of an overfitting problem than the others, especially for the case of small training samples and/or weak external forcing, and the corrected composite produces the best prediction skill among the multi-model systems.

Causes of the 2011-14 California Drought

NASA Technical Reports Server (NTRS)

Seager, Richard; Hoerling, Martin; Schubert, Siegfried; Wang, Hailan; Lyon, Bradfield; Kumar, Arun; Nakamura, Jennifer; Henderson, Naomi

2015-01-01

The causes of the California drought during November-April winters of 2011/12-2013/14 are analyzed using observations and ensemble simulations with seven atmosphere models forced by observed SSTs. Historically, dry California winters are most commonly associated with a ridge off the west coast but no obvious SST forcing. Wet winters are most commonly associated with a trough off the west coast and an El Nino event. These attributes of dry and wet winters are captured by many of the seven models. According to the models, SST forcing can explain up to a third of California winter precipitation variance. SST forcing was key to sustaining a high pressure ridge over the west coast and suppressing precipitation during the three winters. In 2011/12 this was a response to a La Nina event, whereas in 2012/13 and 2013/14 it appears related to a warm west-cool east tropical Pacific SST pattern. All models contain a mode of variability linking such tropical Pacific SST anomalies to a wave train with a ridge off the North American west coast. This mode explains less variance than ENSO and Pacific decadal variability, and its importance in 2012/13 and 2013/14 was unusual. The models from phase 5 of CMIP (CMIP5) project rising greenhouse gases to cause changes in California all-winter precipitation that are very small compared to recent drought anomalies. However, a long-term warming trend likely contributed to surface moisture deficits during the drought. As such, the precipitation deficit during the drought was dominated by natural variability, a conclusion framed by discussion of differences between observed and modeled tropical SST trends.

Pauci ex tanto numero: reducing redundancy in multi-model ensembles

NASA Astrophysics Data System (ADS)

Solazzo, E.; Riccio, A.; Kioutsioukis, I.; Galmarini, S.

2013-02-01

We explicitly address the fundamental issue of member diversity in multi-model ensembles. To date no attempts in this direction are documented within the air quality (AQ) community, although the extensive use of ensembles in this field. Common biases and redundancy are the two issues directly deriving from lack of independence, undermining the significance of a multi-model ensemble, and are the subject of this study. Shared biases among models will determine a biased ensemble, making therefore essential the errors of the ensemble members to be independent so that bias can cancel out. Redundancy derives from having too large a portion of common variance among the members of the ensemble, producing overconfidence in the predictions and underestimation of the uncertainty. The two issues of common biases and redundancy are analysed in detail using the AQMEII ensemble of AQ model results for four air pollutants in two European regions. We show that models share large portions of bias and variance, extending well beyond those induced by common inputs. We make use of several techniques to further show that subsets of models can explain the same amount of variance as the full ensemble with the advantage of being poorly correlated. Selecting the members for generating skilful, non-redundant ensembles from such subsets proved, however, non-trivial. We propose and discuss various methods of member selection and rate the ensemble performance they produce. In most cases, the full ensemble is outscored by the reduced ones. We conclude that, although independence of outputs may not always guarantee enhancement of scores (but this depends upon the skill being investigated) we discourage selecting the members of the ensemble simply on the basis of scores, that is, independence and skills need to be considered disjointly.

Assessing the contribution of different factors in RegCM4.3 regional climate model projections using the Factor Separation method over the Med-CORDEX domain

NASA Astrophysics Data System (ADS)

Zsolt Torma, Csaba; Giorgi, Filippo

2014-05-01

A set of regional climate model (RCM) simulations applying dynamical downscaling of global climate model (GCM) simulations over the Mediterranean domain specified by the international initiative Coordinated Regional Downscaling Experiment (CORDEX) were completed with the Regional Climate Model RegCM, version RegCM4.3. Two GCMs were selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble to provide the driving fields for the RegCM: HadGEM2-ES (HadGEM) and MPI-ESM-MR (MPI). The simulations consist of an ensemble including multiple physics configurations and different "Reference Concentration Pathways" (RCP4.5 and RCP8.5). In total 15 simulations were carried out with 7 model physics configurations with varying convection and land surface schemes. The horizontal grid spacing of the RCM simulations is 50 km and the simulated period in all cases is 1970-2100 (1970-2099 in case of HadGEM driven simulations). This ensemble includes a combination of experiments in which different model components are changed individually and in combination, and thus lends itself optimally to the application of the Factor Separation (FS) method. This study applies the FS method to investigate the contributions of different factors, along with their synergy, on a set of regional climate model (RCM) projections for the Mediterranean region. The FS method is applied to 6 projections for the period 1970-2100 performed with the regional model RegCM4.3 over the Med-CORDEX domain. Two different sets of factors are intercompared, namely the driving global climate model (HadGEM and MPI) boundary conditions against two model physics settings (convection scheme and irrigation). We find that both the GCM driving conditions and the model physics provide important contributions, depending on the variable analyzed (surface air temperature and precipitation), season (winter vs. summer) and time horizon into the future, while the synergy term mostly tends to counterbalance the contributions of the individual factors. We demonstrate the usefulness of the FS method to assess different sources of uncertainty in RCM-based regional climate projections.

Herding cats? A multi-model perspective on tropospheric ozone

NASA Astrophysics Data System (ADS)

Young, P. J.

2015-12-01

Various global multi-model studies have investigated tropospheric ozone changes over multi-decadal timescales. Several robust features emerge, which - for instance - allows the IPCC to associate high confidence in the radiative forcing associated with ozone increases between 1750 and the present day. However, such quantities hide the spread in results between different models, particularly when looking at seasonal and regional scales, and including for comparisons with observations. What can we learn about our scientific understanding from the model spread? What can we learn about models from the model spread? And can we make recommendations for deficient or missing processes if we wish to use our models for environmental prediction? Of course, these questions also have to be asked in the context of what we want the model(s) to do (air quality, climate, stratospheric ozone depletion etc.). This poster will report ongoing work in my group which draws on results from multi-model experiments conducted in support of the most recent IPCC report (CMIP5 and ACCMIP), with an eye to the expected outcomes from the ongoing Chemistry-Climate Model Initiative (CCMI) model simulations.

Regional variability of the frequency distribution of daily precipitation and the synoptic characteristics of heavy precipitation events in present and future climate simulations

NASA Astrophysics Data System (ADS)

DeAngelis, Anthony M.

Changes in the characteristics of daily precipitation in response to global warming may have serious impacts on human life and property. An analysis of precipitation in climate models is performed to evaluate how well the models simulate the present climate and how precipitation may change in the future. Models participating in phase 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) have substantial biases in their simulation of heavy precipitation intensity over parts of North America during the 20th century. Despite these biases, the large-scale atmospheric circulation accompanying heavy precipitation is either simulated realistically or the strength of the circulation is overestimated. The biases are not related to the large-scale flow in a simple way, pointing toward the importance of other model deficiencies, such as coarse horizontal resolution and convective parameterizations, for the accurate simulation of intense precipitation. Although the models may not sufficiently simulate the intensity of precipitation, their realistic portrayal of the large-scale circulation suggests that projections of future precipitation may be reliable. In the CMIP5 ensemble, the distribution of daily precipitation is projected to undergo substantial changes in response to future atmospheric warming. The regional distribution of these changes was investigated, revealing that dry days and days with heavy-extreme precipitation are projected to increase at the expense of light-moderate precipitation over much of the middle and low latitudes. Such projections have serious implications for future impacts from flood and drought events. In other places, changes in the daily precipitation distribution are characterized by a shift toward either wetter or drier conditions in the future, with heavy-extreme precipitation projected to increase in all but the driest subtropical subsidence regions. Further analysis shows that increases in heavy precipitation in midlatitudes are largely explained by thermodynamics, including increases in atmospheric water vapor. However, in low latitudes and northern high latitudes, changes in vertical velocity accompanying heavy precipitation are also important. The strength of the large-scale atmospheric circulation is projected to change in accordance with vertical velocity in many places, though the circulation patterns, and therefore physical mechanisms that generate heavy precipitation, may remain the same.

Probabilistic Evaluation of Competing Climate Models

NASA Astrophysics Data System (ADS)

Braverman, A. J.; Chatterjee, S.; Heyman, M.; Cressie, N.

2017-12-01

A standard paradigm for assessing the quality of climate model simulations is to compare what these models produce for past and present time periods, to observations of the past and present. Many of these comparisons are based on simple summary statistics called metrics. Here, we propose an alternative: evaluation of competing climate models through probabilities derived from tests of the hypothesis that climate-model-simulated and observed time sequences share common climate-scale signals. The probabilities are based on the behavior of summary statistics of climate model output and observational data, over ensembles of pseudo-realizations. These are obtained by partitioning the original time sequences into signal and noise components, and using a parametric bootstrap to create pseudo-realizations of the noise sequences. The statistics we choose come from working in the space of decorrelated and dimension-reduced wavelet coefficients. We compare monthly sequences of CMIP5 model output of average global near-surface temperature anomalies to similar sequences obtained from the well-known HadCRUT4 data set, as an illustration.

Multi-model assessment of air pollution-related premature mortality in Europe and U.S.: Domestic vs. foreign contributions

EPA Science Inventory

The impact of air pollution on premature mortality in Europe and the United States (U.S.) for the year 2010 is modelled by a multi-model ensemble of regional models in the framework of the AQMEII3 project. The gridded surface concentrations of O3, CO, SO2 and PM2.5 from each mode...

Uncertainty Analysis of Downscaled CMIP5 Precipitation Data for Louisiana, USA

NASA Astrophysics Data System (ADS)

Sumi, S. J.; Tamanna, M.; Chivoiu, B.; Habib, E. H.

2014-12-01

The downscaled CMIP3 and CMIP5 Climate and Hydrology Projections dataset contains fine spatial resolution translations of climate projections over the contiguous United States developed using two downscaling techniques (monthly Bias Correction Spatial Disaggregation (BCSD) and daily Bias Correction Constructed Analogs (BCCA)). The objective of this study is to assess the uncertainty of the CMIP5 downscaled general circulation models (GCM). We performed an analysis of the daily, monthly, seasonal and annual variability of precipitation downloaded from the Downscaled CMIP3 and CMIP5 Climate and Hydrology Projections website for the state of Louisiana, USA at 0.125° x 0.125° resolution. A data set of daily gridded observations of precipitation of a rectangular boundary covering Louisiana is used to assess the validity of 21 downscaled GCMs for the 1950-1999 period. The following statistics are computed using the CMIP5 observed dataset with respect to the 21 models: the correlation coefficient, the bias, the normalized bias, the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean square error (RMSE). A measure of variability simulated by each model is computed as the ratio of its standard deviation, in both space and time, to the corresponding standard deviation of the observation. The correlation and MAPE statistics are also computed for each of the nine climate divisions of Louisiana. Some of the patterns that we observed are: 1) Average annual precipitation rate shows similar spatial distribution for all the models within a range of 3.27 to 4.75 mm/day from Northwest to Southeast. 2) Standard deviation of summer (JJA) precipitation (mm/day) for the models maintains lower value than the observation whereas they have similar spatial patterns and range of values in winter (NDJ). 3) Correlation coefficients of annual precipitation of models against observation have a range of -0.48 to 0.36 with variable spatial distribution by model. 4) Most of the models show negative correlation coefficients in summer and positive in winter. 5) MAE shows similar spatial distribution for all the models within a range of 5.20 to 7.43 mm/day from Northwest to Southeast of Louisiana. 6) Highest values of correlation coefficients are found at seasonal scale within a range of 0.36 to 0.46.

Ensembles vs. information theory: supporting science under uncertainty

NASA Astrophysics Data System (ADS)

Nearing, Grey S.; Gupta, Hoshin V.

2018-05-01

Multi-model ensembles are one of the most common ways to deal with epistemic uncertainty in hydrology. This is a problem because there is no known way to sample models such that the resulting ensemble admits a measure that has any systematic (i.e., asymptotic, bounded, or consistent) relationship with uncertainty. Multi-model ensembles are effectively sensitivity analyses and cannot - even partially - quantify uncertainty. One consequence of this is that multi-model approaches cannot support a consistent scientific method - in particular, multi-model approaches yield unbounded errors in inference. In contrast, information theory supports a coherent hypothesis test that is robust to (i.e., bounded under) arbitrary epistemic uncertainty. This paper may be understood as advocating a procedure for hypothesis testing that does not require quantifying uncertainty, but is coherent and reliable (i.e., bounded) in the presence of arbitrary (unknown and unknowable) uncertainty. We conclude by offering some suggestions about how this proposed philosophy of science suggests new ways to conceptualize and construct simulation models of complex, dynamical systems.

Mid-21st century projections of hydroclimate in Western Himalayas and Satluj River basin

NASA Astrophysics Data System (ADS)

Tiwari, Sarita; Kar, Sarat C.; Bhatla, R.

2018-02-01

The Himalayan climate system is sensitive to global warming and climate change. Regional hydrology and the downstream water flow in the rivers of Himalayan origin may change due to variations in snow and glacier melt in the region. This study examines the mid-21st century climate projections over western Himalayas from the Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models under Representative Concentration Pathways (RCP) scenarios (RCP4.5 and RCP8.5). All the global climate models used in the present analysis indicate that the study region would be warmer by mid-century. The temperature trends from all the models studied here are statistically significant at 95% confidence interval. Multi-model ensemble spreads show that there are large differences among the models in their projections of future climate with spread in temperature ranging from about 1.5 °C to 5 °C over various areas of western Himalayas in all the seasons. Spread in precipitation projections lies between 0.3 and 1 mm/day in all the seasons. Major shift in the timing of evaporation maxima and minima is noticed. The GFDL_ESM2G model products have been downscaled to Satluj River basin using the weather research and forecast (WRF) model and impact of climate change on streamflow has been studied. The reduction of precipitation during JJAS is expected to be > 3-6 mm/day in RCP8.5 as compared to present climate. It is expected that precipitation amount shall increase over Satluj basin in future (mid-21st century) The soil and water assessment tool (SWAT) model has been used to simulate the Satluj streamflow for the present and future climate using GFDL_ESM2G precipitation and temperature data as well as the WRF model downscaled data. The computations using the global model data show that total annual discharge from Satluj will be less in future than that in present climate, especially in peak discharge season (JJAS). The SWAT model with downscaled output indicates that during winter and spring, more discharge shall occur in future (RCP8.5) in Satluj River.

Midlatitude Summer Drying: An Underestimated Threat in CMIP5 Models?

NASA Astrophysics Data System (ADS)

Douville, H.; Plazzotta, M.

2017-10-01

Early assessments of the hydrological impacts of global warming suggested both an intensification of the global water cycle and an expansion of dry areas. Yet these alarming conclusions were challenged by a number of latter studies emphasizing the lack of evidence in observations and historical simulations, as well as the large uncertainties in climate projections from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Here several aridity indices and a two-tier attribution strategy are used to demonstrate that a summer midlatitude drying has recently emerged over the northern continents, which is mainly attributable to anthropogenic climate change. This emerging signal is shown to be the harbinger of a long-term drying in the CMIP5 projections. Linear trends in the observed aridity indices can therefore be used as observational constraints and suggest that the projected midlatitude summer drying was underestimated by most CMIP5 models. Mitigating global warming therefore remains a priority to avoid dangerous impacts on global water and food security.

Dominant Drivers of GCMs Errors in the Simulation of South Asian Summer Monsoon

NASA Astrophysics Data System (ADS)

Ashfaq, Moetasim

2017-04-01

Accurate simulation of the South Asian summer monsoon (SAM) is a longstanding unresolved problem in climate modeling science. There has not been a benchmark effort to decipher the origin of undesired yet virtually invariable unsuccessfulness of general circulation models (GCMs) over this region. This study analyzes a large ensemble of CMIP5 GCMs to demonstrate that most of the simulation errors in the summer season and their driving mechanisms are systematic and of similar nature across the GCMs, with biases in meridional differential heating playing a critical role in determining the timing of monsoon onset over land, the magnitude of seasonal precipitation distribution and the trajectories of monsoon depressions. Errors in the pre-monsoon heat low over the lower latitudes and atmospheric latent heating over the slopes of Himalayas and Karakoram Range induce significant errors in the atmospheric circulations and meridional differential heating. Lack of timely precipitation over land further exacerbates such errors by limiting local moisture recycling and latent heating aloft from convection. Most of the summer monsoon errors and their sources are reproducible in the land-atmosphere configuration of a GCM when it is configured at horizontal grid spacing comparable to the CMIP5 GCMs. While an increase in resolution overcomes many modeling challenges, coarse resolution is not necessarily the primary driver in the exhibition of errors over South Asia. These results highlight the importance of previously less well known pre-monsoon mechanisms that critically influence the strength of SAM in the GCMs and highlight the importance of land-atmosphere interactions in the development and maintenance of SAM.

Pauci ex tanto numero: reduce redundancy in multi-model ensembles

NASA Astrophysics Data System (ADS)

Solazzo, E.; Riccio, A.; Kioutsioukis, I.; Galmarini, S.

2013-08-01

We explicitly address the fundamental issue of member diversity in multi-model ensembles. To date, no attempts in this direction have been documented within the air quality (AQ) community despite the extensive use of ensembles in this field. Common biases and redundancy are the two issues directly deriving from lack of independence, undermining the significance of a multi-model ensemble, and are the subject of this study. Shared, dependant biases among models do not cancel out but will instead determine a biased ensemble. Redundancy derives from having too large a portion of common variance among the members of the ensemble, producing overconfidence in the predictions and underestimation of the uncertainty. The two issues of common biases and redundancy are analysed in detail using the AQMEII ensemble of AQ model results for four air pollutants in two European regions. We show that models share large portions of bias and variance, extending well beyond those induced by common inputs. We make use of several techniques to further show that subsets of models can explain the same amount of variance as the full ensemble with the advantage of being poorly correlated. Selecting the members for generating skilful, non-redundant ensembles from such subsets proved, however, non-trivial. We propose and discuss various methods of member selection and rate the ensemble performance they produce. In most cases, the full ensemble is outscored by the reduced ones. We conclude that, although independence of outputs may not always guarantee enhancement of scores (but this depends upon the skill being investigated), we discourage selecting the members of the ensemble simply on the basis of scores; that is, independence and skills need to be considered disjointly.

Contribution of Temperature to Chilean Droughts Using Ensemble Climate Projections

NASA Astrophysics Data System (ADS)

Zambrano-Bigiarini, M.; Alfieri, L.; Naumann, G.; Garreaud, R. D.

2017-12-01

Precipitation deficit is traditionally considered as the main driver of drought events, however the evolution of drought conditions is also influenced by other variables such as temperature, wind speed and evapotranspiration. In view of global warming, the effect of rising temperatures may lead to increased socio-economic drought impacts, particularly in vulnerable developing countries. In this work, we used two drought indices to analyze the impacts of precipitation and temperature on the frequency, severity and duration of Chilean droughts (25°S-56°S) during the XXI century, using multi-model climate projections consistent with the high-end RCP 8.5 scenario. An ensemble of seven global CMIP5 simulations were used to drive the Earth System Model EC-EARTH3-HR v3.1 over the 1976-2100 period, in order to increase the spatial resolution from the original grid to 0.35°. The Standardized Precipitation Index (SPI) was used to describe the impact of precipitation on drought conditions, while the Standardized Precipitation-Evapotranspiration Index (SPEI) was used to assess the effect of temperature -throughout changes in potential evapotranspiration- on drought characteristics at different time scales. Drought indices along with duration, severity and frequency of drought events were computed for a 30-year baseline period (1976-2005) and then compared to three 30-year periods representing short, medium and long-term scenarios (2011-2040, 2041-2070 and 2071-2100). Indices obtained from climate simulations during the baseline period were compared against the corresponding values derived from ground observations. Results obtained with SPI-12 reveal a progressive decrease in precipitation in Chile, which is consistent through all climate models, though each of them shows a different spatial pattern. Simulations based on SPEI-12 show that the expected increase in evaporative demand (driven by the temperature increase) for the region is likely to exacerbate the severity and duration of drought events. Findings of this work are an important support for timely preparation of drought adaptation and mitigation plans to improve water management strategies and resilience during the XXI century.

Continental Asymmetry in Climate-Induced Tropical Drought: Driving Mechanisms and Ecosystem Response

NASA Astrophysics Data System (ADS)

Randerson, J. T.; Swann, A. L. S.; Koven, C. D.; Hoffman, F. M.; Chen, Y.

2015-12-01

Current theory does not adequately explain diverging patterns of future drought stress predicted by Earth system models (ESMs) across tropical South America, Africa, and equatorial Asia. By 2100 for the Representative Concentration Pathway 8.5 (RCP8.5) many models predict significant decreases in precipitation across northeastern South America and Central America. In contrast, most models predict increasing levels of precipitation across tropical Africa and equatorial Asia. Using the Community Earth System Model v1.0 with RCP8.5 simulations to 2300, we found that this longitudinal precipitation asymmetry intensified over time and as a consequence, terrestrial carbon losses from the neotropics were considerably higher than those in Africa and Asia. Carbon losses in some areas of the Amazon in a fully coupled simulation exceeded 15 kg C per m2 by 2300, relative to estimates from a biogeochemically-forced simulation in which atmospheric carbon dioxide and other greenhouse gases did not influence the atmospheric radiation budget. Variations in the amount of neotropical drying varied considerably among CMIP5 ESMs, and we used several types of analysis to identify driving mechanisms and to reduce uncertainties associated with these projections. CMIP5 models in general underestimated North Atlantic sea surface temperatures and the strength of the Atlantic meridional overturning circulation (AMOC). Models that more accurately simulated North Atlantic SSTs during the historical era had smaller mean precipitation biases and predicted greater neotropical forest drying than other models. This suggests that future drought stress in northern South America and Central America may be larger than estimates derived from the multi-model mean. Analysis of idealized radiatively coupled, biogeochemically coupled and fully coupled CMIP5 model simulations indicated that the direct effects of atmospheric carbon dioxide on plant physiology also was an important factor driving asymmetric precipitation change across the tropics, and had a similar pattern as changes induced solely from greenhouse gas effects on atmospheric radiation. We conclude by discussing the implications of the continental drought asymmetry for the vulnerability of tropical forests to fire, agriculture, and tree mortality.

An Ensemble Approach to Understanding the ENSO Response to Climate Change

NASA Astrophysics Data System (ADS)

Stevenson, S.; Capotondi, A.; Fasullo, J.; Otto-Bliesner, B. L.

2017-12-01

The dynamics of the El Nino/Southern Oscillation (ENSO) are known to be sensitive to changes in background climate conditions, as well as atmosphere/ocean feedbacks. However, the degree to which shifts in ENSO characteristics can be robustly attributed to external climate forcings remains unknown. Efforts to assess these changes in a multi-model framework are subject to uncertainties due to both differing model physics and internal ENSO variability. New community ensembles created at the National Center for Atmospheric Research and the NOAA Geophysical Fluid Dynamics Laboratory are ideally suited to addressing this problem, providing many realizations of the climate of the 850-2100 period with a combination of both natural and anthropogenic climate forcing factors. Here we analyze the impacts of external forcing on El Nino and La Nina evolution using four sets of simulations: the CESM Last Millennium Ensemble (CESM-LME), which covers the 850-2005 period and provides long-term context for forced responses; the Large Ensemble (CESM-LE), which includes 20th century and 21st century (RCP8.5) projections; the Medium Ensemble (CESM-ME), which is composed of 21st century RCP4.5 projections; and a large ensemble with the GFDL ESM2M, which includes 20th century and RCP8.5 projections. In the CESM, ENSO variance increases slightly over the 20th century in all ensembles, with the effects becoming much larger during the 21st. The slower increase in variance over the 20th century is shown to arise from compensating influences from greenhouse gas (GHG) and anthropogenic aerosol emissions, which give way to GHG-dominated effects by 2100. However, the 21st century variance increase is not robust: CESM and the ESM2M differ drastically in their ENSO projections. The mechanisms for these inter-model differences are discussed, as are the implications for the design of future multi-model ENSO projection experiments.

Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)

NASA Astrophysics Data System (ADS)

Arritt, R. W.

2008-12-01

The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Can regional climate models provide additional useful information from global seasonal forecasts? MRED will use a suite of regional climate models to downscale seasonal forecasts produced by the new National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus will be on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the potential usefulness of higher resolution, especially for near-surface fields influenced by high resolution orography. Each regional model will cover the conterminous US (CONUS) at approximately 32 km resolution, and will perform an ensemble of 15 runs for each year 1982-2003 for the forecast period 1 December - 30 April. MRED will compare individual regional and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs), as well as wind, humidity, radiation, turbulent heat fluxes, which are important for more advanced coupled macro-scale hydrologic models. Metrics of ensemble spread will also be evaluated. Extensive analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will eventually define a strategy for more skillful and useful regional seasonal climate forecasts.

Internal Interdecadal Variability in CMIP5 Control Simulations

NASA Astrophysics Data System (ADS)

Cheung, A. H.; Mann, M. E.; Frankcombe, L. M.; England, M. H.; Steinman, B. A.; Miller, S. K.

2015-12-01

Here we make use of control simulations from the CMIP5 models to quantify the amplitude of the interdecadal internal variability component in Atlantic, Pacific, and Northern Hemisphere mean surface temperature. We compare against estimates derived from observations using a semi-empirical approach wherein the forced component as estimated using CMIP5 historical simulations is removed to yield an estimate of the residual, internal variability. While the observational estimates are largely consistent with those derived from the control simulations for both basins and the Northern Hemisphere, they lie in the upper range of the model distributions, suggesting the possibility of differences between the amplitudes of observed and modeled variability. We comment on some possible reasons for the disparity.

The North American Multi-Model Ensemble (NMME): Phase-1 Seasonal to Interannual Prediction, Phase-2 Toward Developing Intra-Seasonal Prediction

NASA Technical Reports Server (NTRS)

Kirtman, Ben P.; Min, Dughong; Infanti, Johnna M.; Kinter, James L., III; Paolino, Daniel A.; Zhang, Qin; vandenDool, Huug; Saha, Suranjana; Mendez, Malaquias Pena; Becker, Emily;

IPSL-CM5A2. An Earth System Model designed to run long simulations for past and future climates.

NASA Astrophysics Data System (ADS)

Sepulchre, Pierre; Caubel, Arnaud; Marti, Olivier; Hourdin, Frédéric; Dufresne, Jean-Louis; Boucher, Olivier

2017-04-01

The IPSL-CM5A model was developed and released in 2013 "to study the long-term response of the climate system to natural and anthropogenic forcings as part of the 5th Phase of the Coupled Model Intercomparison Project (CMIP5)" [Dufresne et al., 2013]. Although this model also has been used for numerous paleoclimate studies, a major limitation was its computation time, which averaged 10 model-years / day on 32 cores of the Curie supercomputer (on TGCC computing center, France). Such performances were compatible with the experimental designs of intercomparison projects (e.g. CMIP, PMIP) but became limiting for modelling activities involving several multi-millenial experiments, which are typical for Quaternary or "deeptime" paleoclimate studies, in which a fully-equilibrated deep-ocean is mandatory. Here we present the Earth-System model IPSL-CM5A2. Based on IPSL-CM5A, technical developments have been performed both on separate components and on the coupling system in order to speed up the whole coupled model. These developments include the integration of hybrid parallelization MPI-OpenMP in LMDz atmospheric component, the use of a new input-ouput library to perform parallel asynchronous input/output by using computing cores as "IO servers", the use of a parallel coupling library between the ocean and the atmospheric components. Running on 304 cores, the model can now simulate 55 years per day, opening new gates towards multi-millenial simulations. Apart from obtaining better computing performances, one aim of setting up IPSL-CM5A2 was also to overcome the cold bias depicted in global surface air temperature (t2m) in IPSL-CM5A. We present the tuning strategy to overcome this bias as well as the main characteristics (including biases) of the pre-industrial climate simulated by IPSL-CM5A2. Lastly, we shortly present paleoclimate simulations run with this model, for the Holocene and for deeper timescales in the Cenozoic, for which the particular continental configuration was overcome by a new design of the ocean tripolar grid.

Multi-RCM ensemble downscaling of global seasonal forecasts (MRED)

NASA Astrophysics Data System (ADS)

Arritt, R.

2009-04-01

Regional climate models (RCMs) have long been used to downscale global climate simulations. In contrast the ability of RCMs to downscale seasonal climate forecasts has received little attention. The Multi-RCM Ensemble Downscaling (MRED) project was recently initiated to address the question, Does dynamical downscaling using RCMs provide additional useful information for seasonal forecasts made by global models? MRED is using a suite of RCMs to downscale seasonal forecasts produced by the National Centers for Environmental Prediction (NCEP) Climate Forecast System (CFS) seasonal forecast system and the NASA GEOS5 system. The initial focus is on wintertime forecasts in order to evaluate topographic forcing, snowmelt, and the usefulness of higher resolution for near-surface fields influenced by high resolution orography. Each RCM covers the conterminous U.S. at approximately 32 km resolution, comparable to the scale of the North American Regional Reanalysis (NARR) which will be used to evaluate the models. The forecast ensemble for each RCM is comprised of 15 members over a period of 22+ years (from 1982 to 2003+) for the forecast period 1 December - 30 April. Each RCM will create a 15-member lagged ensemble by starting on different dates in the preceding November. This results in a 120-member ensemble for each projection (8 RCMs by 15 members per RCM). The RCMs will be continually updated at their lateral boundaries using 6-hourly output from CFS or GEOS5. Hydrometeorological output will be produced in a standard netCDF-based format for a common analysis grid, which simplifies both model intercomparison and the generation of ensembles. MRED will compare individual RCM and global forecasts as well as ensemble mean precipitation and temperature forecasts, which are currently being used to drive macroscale land surface models (LSMs). Metrics of ensemble spread will also be evaluated. Extensive process-oriented analysis will be performed to link improvements in downscaled forecast skill to regional forcings and physical mechanisms. Our overarching goal is to determine what additional skill can be provided by a community ensemble of high resolution regional models, which we believe will define a strategy for more skillful and useful regional seasonal climate forecasts.

Mid-Century Warming in the Los Angeles Region and its Uncertainty using Dynamical and Statistical Downscaling

NASA Astrophysics Data System (ADS)

Sun, F.; Hall, A. D.; Walton, D.; Capps, S. B.; Qu, X.; Huang, H. J.; Berg, N.; Jousse, A.; Schwartz, M.; Nakamura, M.; Cerezo-Mota, R.

2012-12-01

Using a combination of dynamical and statistical downscaling techniques, we projected mid-21st century warming in the Los Angeles region at 2-km resolution. To account for uncertainty associated with the trajectory of future greenhouse gas emissions, we examined projections for both "business-as-usual" (RCP8.5) and "mitigation" (RCP2.6) emissions scenarios from the Fifth Coupled Model Intercomparison Project (CMIP5). To account for the considerable uncertainty associated with choice of global climate model, we downscaled results for all available global climate models in CMIP5. For the business-as-usual scenario, we find that by the mid-21st century, the most likely warming is roughly 2.6°C averaged over the region's land areas, with a 95% confidence that the warming lies between 0.9 and 4.2°C. The high resolution of the projections reveals a pronounced spatial pattern in the warming: High elevations and inland areas separated from the coast by at least one mountain complex warm 20 to 50% more than the areas near the coast or within the Los Angeles basin. This warming pattern is especially apparent in summertime. The summertime warming contrast between the inland and coastal zones has a large effect on the most likely expected number of extremely hot days per year. Coastal locations and areas within the Los Angeles basin see roughly two to three times the number of extremely hot days, while high elevations and inland areas typically experience approximately three to five times the number of extremely hot days. Under the mitigation emissions scenario, the most likely warming and increase in heat extremes are somewhat smaller. However, the majority of the warming seen in the business-as-usual scenario still occurs at all locations in the most likely case under the mitigation scenario, and heat extremes still increase significantly. This warming study is the first part of a series studies of our project. More climate change impacts on the Santa Ana wind, rainfall, snowfall and snowmelt, cloud and surface hydrology are forthcoming and could be found in www.atmos.ucla.edu/csrl.he ensemble-mean, annual-mean surface air temperature change and its uncertainty from the available CMIP5 GCMs under the RCP8.5 (left) and RCP2.6 (right) emissions scenarios, unit: °C.

Quantitative assessment of AOD from 17 CMIP5 models based on satellite-derived AOD over India

DOE Office of Scientific and Technical Information (OSTI.GOV)

Misra, Amit; Kanawade, Vijay P.; Tripathi, Sachchida Nand

Aerosol optical depth (AOD) values from 17 CMIP5 models are compared with Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) derived AODs over India. The objective is to identify the cases of successful AOD simulation by CMIP5 models, considering satellite-derived AOD as a benchmark. Six years of AOD data (2000–2005) from MISR and MODIS are processed to create quality-assured gridded AOD maps over India, which are compared with corresponding maps of 17 CMIP5 models at the same grid resolution. Intercomparison of model and satellite data shows that model-AOD is better correlated with MISR-derived AOD than MODIS. The correlation between model-AOD andmore » MISR-AOD is used to segregate the models into three categories identifying their performance in simulating the AOD over India. Maps of correlation between model-AOD and MISR-/MODIS-AOD are generated to provide quantitative information about the intercomparison. The two sets of data are examined for different seasons and years to examine the seasonal and interannual variation in the correlation coefficients. In conclusion, latitudinal and longitudinal variations in AOD as simulated by models are also examined and compared with corresponding variations observed by satellites.« less

Quantitative assessment of AOD from 17 CMIP5 models based on satellite-derived AOD over India

DOE PAGES

Misra, Amit; Kanawade, Vijay P.; Tripathi, Sachchida Nand

2016-08-03

Aerosol optical depth (AOD) values from 17 CMIP5 models are compared with Moderate Resolution Imaging Spectroradiometer (MODIS) and Multiangle Imaging Spectroradiometer (MISR) derived AODs over India. The objective is to identify the cases of successful AOD simulation by CMIP5 models, considering satellite-derived AOD as a benchmark. Six years of AOD data (2000–2005) from MISR and MODIS are processed to create quality-assured gridded AOD maps over India, which are compared with corresponding maps of 17 CMIP5 models at the same grid resolution. Intercomparison of model and satellite data shows that model-AOD is better correlated with MISR-derived AOD than MODIS. The correlation between model-AOD andmore » MISR-AOD is used to segregate the models into three categories identifying their performance in simulating the AOD over India. Maps of correlation between model-AOD and MISR-/MODIS-AOD are generated to provide quantitative information about the intercomparison. The two sets of data are examined for different seasons and years to examine the seasonal and interannual variation in the correlation coefficients. In conclusion, latitudinal and longitudinal variations in AOD as simulated by models are also examined and compared with corresponding variations observed by satellites.« less

Snow water equivalent in the Alps as seen by gridded data sets, CMIP5 and CORDEX climate models

NASA Astrophysics Data System (ADS)

Terzago, Silvia; von Hardenberg, Jost; Palazzi, Elisa; Provenzale, Antonello

2017-07-01

The estimate of the current and future conditions of snow resources in mountain areas would require reliable, kilometre-resolution, regional-observation-based gridded data sets and climate models capable of properly representing snow processes and snow-climate interactions. At the moment, the development of such tools is hampered by the sparseness of station-based reference observations. In past decades passive microwave remote sensing and reanalysis products have mainly been used to infer information on the snow water equivalent distribution. However, the investigation has usually been limited to flat terrains as the reliability of these products in mountain areas is poorly characterized.This work considers the available snow water equivalent data sets from remote sensing and from reanalyses for the greater Alpine region (GAR), and explores their ability to provide a coherent view of the snow water equivalent distribution and climatology in this area. Further we analyse the simulations from the latest-generation regional and global climate models (RCMs, GCMs), participating in the Coordinated Regional Climate Downscaling Experiment over the European domain (EURO-CORDEX) and in the Fifth Coupled Model Intercomparison Project (CMIP5) respectively. We evaluate their reliability in reproducing the main drivers of snow processes - near-surface air temperature and precipitation - against the observational data set EOBS, and compare the snow water equivalent climatology with the remote sensing and reanalysis data sets previously considered. We critically discuss the model limitations in the historical period and we explore their potential in providing reliable future projections.The results of the analysis show that the time-averaged spatial distribution of snow water equivalent and the amplitude of its annual cycle are reproduced quite differently by the different remote sensing and reanalysis data sets, which in fact exhibit a large spread around the ensemble mean. We find that GCMs at spatial resolutions equal to or finer than 1.25° longitude are in closer agreement with the ensemble mean of satellite and reanalysis products in terms of root mean square error and standard deviation than lower-resolution GCMs. The set of regional climate models from the EURO-CORDEX ensemble provides estimates of snow water equivalent at 0.11° resolution that are locally much larger than those indicated by the gridded data sets, and only in a few cases are these differences smoothed out when snow water equivalent is spatially averaged over the entire Alpine domain. ERA-Interim-driven RCM simulations show an annual snow cycle that is comparable in amplitude to those provided by the reference data sets, while GCM-driven RCMs present a large positive bias. RCMs and higher-resolution GCM simulations are used to provide an estimate of the snow reduction expected by the mid-21st century (RCP 8.5 scenario) compared to the historical climatology, with the main purpose of highlighting the limits of our current knowledge and the need for developing more reliable snow simulations.

The UK Earth System Model project

NASA Astrophysics Data System (ADS)

Tang, Yongming

2016-04-01

In this talk we will describe the development and current status of the UK Earth System Model (UKESM). This project is a NERC/Met Office collaboration and has two objectives; to develop and apply a world-leading Earth System Model, and to grow a community of UK Earth System Model scientists. We are building numerical models that include all the key components of the global climate system, and contain the important process interactions between global biogeochemistry, atmospheric chemistry and the physical climate system. UKESM will be used to make key CMIP6 simulations as well as long-time (e.g. millennium) simulations, large ensemble experiments and investigating a range of future carbon emission scenarios.

Grand European and Asian-Pacific multi-model seasonal forecasts: maximization of skill and of potential economical value to end-users

NASA Astrophysics Data System (ADS)

Alessandri, Andrea; Felice, Matteo De; Catalano, Franco; Lee, June-Yi; Wang, Bin; Lee, Doo Young; Yoo, Jin-Ho; Weisheimer, Antije

2018-04-01

Multi-model ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single-model ensembles. Previous works suggested that the potential benefit that can be expected by using a MME amplifies with the increase of the independence of the contributing Seasonal Prediction Systems. In this work we combine the two MME Seasonal Prediction Systems (SPSs) independently developed by the European (ENSEMBLES) and by the Asian-Pacific (APCC/CliPAS) communities. To this aim, all the possible multi-model combinations obtained by putting together the 5 models from ENSEMBLES and the 11 models from APCC/CliPAS have been evaluated. The grand ENSEMBLES-APCC/CliPAS MME enhances significantly the skill in predicting 2m temperature and precipitation compared to previous estimates from the contributing MMEs. Our results show that, in general, the better combinations of SPSs are obtained by mixing ENSEMBLES and APCC/CliPAS models and that only a limited number of SPSs is required to obtain the maximum performance. The number and selection of models that perform better is usually different depending on the region/phenomenon under consideration so that all models are useful in some cases. It is shown that the incremental performance contribution tends to be higher when adding one model from ENSEMBLES to APCC/CliPAS MMEs and vice versa, confirming that the benefit of using MMEs amplifies with the increase of the independence the contributing models. To verify the above results for a real world application, the Grand ENSEMBLES-APCC/CliPAS MME is used to predict retrospective energy demand over Italy as provided by TERNA (Italian Transmission System Operator) for the period 1990-2007. The results demonstrate the useful application of MME seasonal predictions for energy demand forecasting over Italy. It is shown a significant enhancement of the potential economic value of forecasting energy demand when using the better combinations from the Grand MME by comparison to the maximum value obtained from the better combinations of each of the two contributing MMEs. The above results demonstrate for the first time the potential of the Grand MME to significantly contribute in obtaining useful predictions at the seasonal time-scale.

Variability of hydrological extreme events in East Asia and their dynamical control: a comparison between observations and two high-resolution global climate models

NASA Astrophysics Data System (ADS)

Freychet, N.; Duchez, A.; Wu, C.-H.; Chen, C.-A.; Hsu, H.-H.; Hirschi, J.; Forryan, A.; Sinha, B.; New, A. L.; Graham, T.; Andrews, M. B.; Tu, C.-Y.; Lin, S.-J.

2017-02-01

This work investigates the variability of extreme weather events (drought spells, DS15, and daily heavy rainfall, PR99) over East Asia. It particularly focuses on the large scale atmospheric circulation associated with high levels of the occurrence of these extreme events. Two observational datasets (APHRODITE and PERSIANN) are compared with two high-resolution global climate models (HiRAM and HadGEM3-GC2) and an ensemble of other lower resolution climate models from CMIP5. We first evaluate the performance of the high resolution models. They both exhibit good skill in reproducing extreme events, especially when compared with CMIP5 results. Significant differences exist between the two observational datasets, highlighting the difficulty of having a clear estimate of extreme events. The link between the variability of the extremes and the large scale circulation is investigated, on monthly and interannual timescales, using composite and correlation analyses. Both extreme indices DS15 and PR99 are significantly linked to the low level wind intensity over East Asia, i.e. the monsoon circulation. It is also found that DS15 events are strongly linked to the surface temperature over the Siberian region and to the land-sea pressure contrast, while PR99 events are linked to the sea surface temperature anomalies over the West North Pacific. These results illustrate the importance of the monsoon circulation on extremes over East Asia. The dependencies on of the surface temperature over the continent and the sea surface temperature raise the question as to what extent they could affect the occurrence of extremes over tropical regions in future projections.

Scale-dependent performances of CMIP5 earth system models in simulating terrestrial vegetation carbon

NASA Astrophysics Data System (ADS)

Jiang, L.; Luo, Y.; Yan, Y.; Hararuk, O.

2013-12-01

Mitigation of global changes will depend on reliable projection for the future situation. As the major tools to predict future climate, Earth System Models (ESMs) used in Coupled Model Intercomparison Project Phase 5 (CMIP5) for the IPCC Fifth Assessment Report have incorporated carbon cycle components, which account for the important fluxes of carbon between the ocean, atmosphere, and terrestrial biosphere carbon reservoirs; and therefore are expected to provide more detailed and more certain projections. However, ESMs are never perfect; and evaluating the ESMs can help us to identify uncertainties in prediction and give the priorities for model development. In this study, we benchmarked carbon in live vegetation in the terrestrial ecosystems simulated by 19 ESMs models from CMIP5 with an observationally estimated data set of global carbon vegetation pool 'Olson's Major World Ecosystem Complexes Ranked by Carbon in Live Vegetation: An Updated Database Using the GLC2000 Land Cover Product' by Gibbs (2006). Our aim is to evaluate the ability of ESMs to reproduce the global vegetation carbon pool at different scales and what are the possible causes for the bias. We found that the performance CMIP5 ESMs is very scale-dependent. While CESM1-BGC, CESM1-CAM5, CESM1-FASTCHEM and CESM1-WACCM, and NorESM1-M and NorESM1-ME (they share the same model structure) have very similar global sums with the observation data but they usually perform poorly at grid cell and biome scale. In contrast, MIROC-ESM and MIROC-ESM-CHEM simulate the best on at grid cell and biome scale but have larger differences in global sums than others. Our results will help improve CMIP5 ESMs for more reliable prediction.

Most robust estimate of the Transient Climate Response yet?

NASA Astrophysics Data System (ADS)

Haustein, Karsten; Venema, Victor; Schurer, Andrew

2017-04-01

Estimates of the Transient Climate Response often lack a coherent hemispheric or otherwise spatio-temporal representation. In the light of recent work that highlights the importance of inhomogeneous forcing considerations (Shindell et al 2014; Marvel et al 2015) and tas/tos-related inaccuracies (Richardson et al. 2016), here we present results from a well-tested two-box response model that takes these effects carefully into account. All external forcing data are updated based on latest emission estimates as well as recent TSI and volcanic AOD estimates. So are observed GMST data which include data for the entire year of 2016. Hence we also provide one of the first TCR estimates taking the latest El Nino into account. We demonstrate that short-term climate variability is not going to change the TCR estimate beyond very minor fluctuations. The method is therefore shown to be robust within surprisingly small uncertainty estimates. Using PMIP3 and an extended ensemble of HadCM3 simulations (Euro500; Schurer et al. 2014) GCM simulations for the pre-industrial period, we test the fast and slow response time constants that are tailored for observational data (Ripdal 2012). We also test the hemispheric response as well as the response over land and ocean separately. The TCR/ECS ratio is taken from a selected sub-set of CMIP5 simulations. The selection criteria is the best spatiotemporal match over 4 different time periods between 1860 and 2010. We will argue that this procedure should also be standard procedure to estimate ECS from observations, rather than relying on OHC estimates only. Finally, the demonstrate that PMIP3-type simulations that are initialised at least a century before 1850 (as is the standard initialisation for CMIP5-type simulations) are to be preferred. Remaining long-term radiative imbalance due to strong volcanic eruptions (e.g. Gleckler et al. 2006) tend to make CMIP5-type simulations slightly more sensitive to forcing, which leads to detectable stronger warming up until recent day.

Evaluating the strength of the land$-$atmosphere moisture feedback in Earth system models using satellite observations

DOE PAGES

Levine, Paul A.; Randerson, James T.; Swenson, Sean C.; ...

2016-12-09

The relationship between terrestrial water storage (TWS) and atmospheric processes has important implications for predictability of climatic extremes and projection of future climate change. In places where moisture availability limits evapotranspiration (ET), variability in TWS has the potential to influence surface energy fluxes and atmospheric conditions. Where atmospheric conditions, in turn, influence moisture availability, a full feedback loop exists. Here we developed a novel approach for measuring the strength of both components of this feedback loop, i.e., the forcing of the atmosphere by variability in TWS and the response of TWS to atmospheric variability, using satellite observations of TWS, precipitation,more » solar radiation, and vapor pressure deficit during 2002–2014. Our approach defines metrics to quantify the relationship between TWS anomalies and climate globally on a seasonal to interannual timescale. Metrics derived from the satellite data were used to evaluate the strength of the feedback loop in 38 members of the Community Earth System Model (CESM) Large Ensemble (LENS) and in six models that contributed simulations to phase 5 of the Coupled Model Intercomparison Project (CMIP5). We found that both forcing and response limbs of the feedback loop in LENS were stronger than in the satellite observations in tropical and temperate regions. Feedbacks in the selected CMIP5 models were not as strong as those found in LENS, but were still generally stronger than those estimated from the satellite measurements. Consistent with previous studies conducted across different spatial and temporal scales, our analysis suggests that models may overestimate the strength of the feedbacks between the land surface and the atmosphere. Lastly, we describe several possible mechanisms that may contribute to this bias, and discuss pathways through which models may overestimate ET or overestimate the sensitivity of ET to TWS.« less

Using prior information to separate the temperature response to greenhouse gas forcing from that of aerosols - Estimating the transient climate response

NASA Astrophysics Data System (ADS)

Schurer, Andrew; Hegerl, Gabriele

2016-04-01

The evaluation of the transient climate response (TCR) is of critical importance to policy makers as it can be used to calculate a simple estimate of the expected warming given predicted greenhouse gas emissions. Previous studies using optimal detection techniques have been able to estimate a TCR value from the historic record using simulations from some of the models which took part in the Coupled Model Intercomparison Project Phase 5 (CMIP5) but have found that others give unconstrained results. At least partly this is due to degeneracy between the greenhouse gas and aerosol signals which makes separation of the temperature response to these forcings problematic. Here we re-visit this important topic by using an adapted optimal detection analysis within a Bayesian framework. We account for observational uncertainty by the use of an ensemble of instrumental observations, and model uncertainty by combining the results from several different models. This framework allows the use of prior information which is found to help separate the response to the different forcings leading to a more constrained estimate of TCR.

Tropical teleconnections via the ocean and atmosphere induced by Southern Ocean deep convective events

NASA Astrophysics Data System (ADS)

Marinov, I.; Cabre, A.; Gunn, A.; Gnanadesikan, A.

2016-12-01

The current generation (CMIP5) of Earth System Models (ESMs) shows a huge variability in their ability to represent Southern Ocean (SO) deep-ocean convection and Antarctic Bottom Water, with a preference for open-sea convection in the Weddell and Ross gyres. A long control simulation in a coarse 3o resolution ESM (the GFDL CM2Mc model) shows a highly regular multi-decadal oscillation between periods of SO open sea convection and non-convective periods. This process also happens naturally, with different frequencies and durations of convection across most CMIP5 models under preindustrial forcing (deLavergne et al, 2014). Here we assess the impact of SO deep convection and resulting sea surface temperature (SST) anomalies on the tropical atmosphere and ocean via teleconnections, with a focus on interannual to multi-decadal timescales. We combine analysis of our low-resolution coupled model with inter-model analysis across historical CMIP5 simulations. SST cooling south of 60S during non-convective decades triggers a stronger, northward shifted SH Hadley cell, which results in intensified northward cross-equatorial moist heat transport and a poleward shift in the ITCZ. Resulting correlations between the cross-equatorial atmospheric heat transport and ITCZ location are in good agreement with recent theories (e.g. Frierson et al. 2013; Donohoe et al. 2014). Lagged correlations between a SO convective index and cross-equatorial heat transports (in the atmosphere and ocean), as well as various tropical (and ENSO) climate indices are analyzed. In the ocean realm, we find that non-convective decades result in weaker AABW formation and weaker ACC but stronger Antarctic Intermediate Water (AAIW) formation, likely as a result of stronger SO westerlies (more positive SAM). The signals of AABW and AAIW are seen in the tropics on short timescales of years to decades in the temperature, heat storage and heat transport anomalies and also in deep and intermediate ocean oxygen. Most of the current ESMs with frequent deep-sea convection events in the control state predict a permanent shut down of this convection under climate change in the 21st century. We propose that the preindustrial convective state of the Southern Ocean and its evolution under climate warming will have implications for the SO-tropical teleconnections.

ENSO Frequency Asymmetry and the Pacific Decadal Oscillation in Observations and 19 CMIP5 Models

NASA Astrophysics Data System (ADS)

Lin, Renping; Zheng, Fei; Dong, Xiao

2018-05-01

Using observational data and the pre-industrial simulations of 19 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), the El Niño (EN) and La Niña (LN) events in positive and negative Pacific Decadal Oscillation (PDO) phases are examined. In the observational data, with EN (LN) events the positive (negative) SST anomaly in the equatorial eastern Pacific is much stronger in positive (negative) PDO phases than in negative (positive) phases. Meanwhile, the models cannot reasonably reproduce this difference. Besides, the modulation of ENSO frequency asymmetry by the PDO is explored. Results show that, in the observational data, EN is 300% more (58% less) frequent than LN in positive (negative) PDO phases, which is significant at the 99% confidence level using the Monte Carlo test. Most of the CMIP5 models exhibit results that are consistent with the observational data.

Regionally Varying Assessments of Tropical Width in Reanalyses and CMIP5 Models Using a Tropopause Break Metric

NASA Astrophysics Data System (ADS)

Homeyer, C. R.; Martin, E. R.; McKinzie, R.; McCarthy, K.

2017-12-01

The boundary between the tropics and the extratropics in each hemisphere is not fixed in space or time. Variations in the north-south width of the tropics are directly connected to changes in weather and climate. These fluctuations have been shown to impact tropical biodiversity, the spread of vector borne diseases, atmospheric chemistry, and additional natural and human sectors. However, there is no unanimous definition of the tropical boundary. This has led to a disagreement on the magnitude of changes in the tropical width during the past 30 years and a lack of understanding concerning its spatial and temporal variability. This study identifies the variability of the tropical width in modern reanalyses (ERA-Interim, JRA-55, CFSR, MERRA, and MERRA-2) and CMIP5 models (all models with available 6-hourly output) using a novel analysis metric: the tropopause "break" (i.e., the sharp discontinuity in tropopause altitude between the tropics and extratropics). Similarities and differences are found amongst the reanalyses, with some degree of tropical narrowing in the Eastern Pacific between 1981 and 2010. Historical simulations from the CMIP5 models agree well with the tropopause break latitudes depicted by the reanalyses, with considerable differences in estimated trends over the relatively short overlapping time period of the datasets. For future projections under the RCP8.5 scenario from 2006 to 2100, CMIP5 models generally show statistically significant increases in tropical width (at the 99% level) throughout each hemisphere, with regional variability of 1-2 degrees in poleward latitude trends. The impact of CMIP5 model grid resolution and other factors on the results of the tropopause break analysis will be discussed.

The seasonal cycle of pCO2 and CO2 fluxes in the Southern Ocean: diagnosing anomalies in CMIP5 Earth system models

NASA Astrophysics Data System (ADS)

Precious Mongwe, N.; Vichi, Marcello; Monteiro, Pedro M. S.

2018-05-01

The Southern Ocean forms an important component of the Earth system as a major sink of CO2 and heat. Recent studies based on the Coupled Model Intercomparison Project version 5 (CMIP5) Earth system models (ESMs) show that CMIP5 models disagree on the phasing of the seasonal cycle of the CO2 flux (FCO2) and compare poorly with available observation products for the Southern Ocean. Because the seasonal cycle is the dominant mode of CO2 variability in the Southern Ocean, its simulation is a rigorous test for models and their long-term projections. Here we examine the competing roles of temperature and dissolved inorganic carbon (DIC) as drivers of the seasonal cycle of pCO2 in the Southern Ocean to explain the mechanistic basis for the seasonal biases in CMIP5 models. We find that despite significant differences in the spatial characteristics of the mean annual fluxes, the intra-model homogeneity in the seasonal cycle of FCO2 is greater than observational products. FCO2 biases in CMIP5 models can be grouped into two main categories, i.e., group-SST and group-DIC. Group-SST models show an exaggeration of the seasonal rates of change of sea surface temperature (SST) in autumn and spring during the cooling and warming peaks. These higher-than-observed rates of change of SST tip the control of the seasonal cycle of pCO2 and FCO2 towards SST and result in a divergence between the observed and modeled seasonal cycles, particularly in the Sub-Antarctic Zone. While almost all analyzed models (9 out of 10) show these SST-driven biases, 3 out of 10 (namely NorESM1-ME, HadGEM-ES and MPI-ESM, collectively the group-DIC models) compensate for the solubility bias because of their overly exaggerated primary production, such that biologically driven DIC changes mainly regulate the seasonal cycle of FCO2.

Anthropogenic sulfate aerosol and the southward shift of tropical precipitation in the late 20th century

NASA Astrophysics Data System (ADS)

Hwang, Yen-Ting; Frierson, Dargan M. W.; Kang, Sarah M.

2013-06-01

In this paper, we demonstrate a global scale southward shift of the tropical rain belt during the latter half of the 20th century in observations and global climate models (GCMs). In rain gauge data, the southward shift maximizes in the 1980s and is associated with signals in Africa, Asia, and South America. A southward shift exists at a similar time in nearly all CMIP3 and CMIP5 historical simulations, and occurs on both land and ocean, although in most models the shifts are significantly less than in observations. Utilizing a theoretical framework based on atmospheric energetics, we perform an attribution of the zonal mean southward shift of precipitation across a large suite of CMIP3 and CMIP5 GCMs. Our results suggest that anthropogenic aerosol cooling of the Northern Hemisphere is the primary cause of the consistent southward shift across GCMs, although other processes affecting the atmospheric energy budget also contribute to the model-to-model spread.

Assimilating the Future for Better Forecasts and Earlier Warnings

NASA Astrophysics Data System (ADS)

Du, H.; Wheatcroft, E.; Smith, L. A.

2016-12-01

Multi-model ensembles have become popular tools to account for some of the uncertainty due to model inadequacy in weather and climate simulation-based predictions. The current multi-model forecasts focus on combining single model ensemble forecasts by means of statistical post-processing. Assuming each model is developed independently or with different primary target variables, each is likely to contain different dynamical strengths and weaknesses. Using statistical post-processing, such information is only carried by the simulations under a single model ensemble: no advantage is taken to influence simulations under the other models. A novel methodology, named Multi-model Cross Pollination in Time, is proposed for multi-model ensemble scheme with the aim of integrating the dynamical information regarding the future from each individual model operationally. The proposed approach generates model states in time via applying data assimilation scheme(s) to yield truly "multi-model trajectories". It is demonstrated to outperform traditional statistical post-processing in the 40-dimensional Lorenz96 flow. Data assimilation approaches are originally designed to improve state estimation from the past to the current time. The aim of this talk is to introduce a framework that uses data assimilation to improve model forecasts at future time (not to argue for any one particular data assimilation scheme). Illustration of applying data assimilation "in the future" to provide early warning of future high-impact events is also presented.

Next-Generation Climate Modeling Science Challenges for Simulation, Workflow and Analysis Systems

NASA Astrophysics Data System (ADS)

Koch, D. M.; Anantharaj, V. G.; Bader, D. C.; Krishnan, H.; Leung, L. R.; Ringler, T.; Taylor, M.; Wehner, M. F.; Williams, D. N.

2016-12-01

We will present two examples of current and future high-resolution climate-modeling research that are challenging existing simulation run-time I/O, model-data movement, storage and publishing, and analysis. In each case, we will consider lessons learned as current workflow systems are broken by these large-data science challenges, as well as strategies to repair or rebuild the systems. First we consider the science and workflow challenges to be posed by the CMIP6 multi-model HighResMIP, involving around a dozen modeling groups performing quarter-degree simulations, in 3-member ensembles for 100 years, with high-frequency (1-6 hourly) diagnostics, which is expected to generate over 4PB of data. An example of science derived from these experiments will be to study how resolution affects the ability of models to capture extreme-events such as hurricanes or atmospheric rivers. Expected methods to transfer (using parallel Globus) and analyze (using parallel "TECA" software tools) HighResMIP data for such feature-tracking by the DOE CASCADE project will be presented. A second example will be from the Accelerated Climate Modeling for Energy (ACME) project, which is currently addressing challenges involving multiple century-scale coupled high resolution (quarter-degree) climate simulations on DOE Leadership Class computers. ACME is anticipating production of over 5PB of data during the next 2 years of simulations, in order to investigate the drivers of water cycle changes, sea-level-rise, and carbon cycle evolution. The ACME workflow, from simulation to data transfer, storage, analysis and publication will be presented. Current and planned methods to accelerate the workflow, including implementing run-time diagnostics, and implementing server-side analysis to avoid moving large datasets will be presented.

Changes of reference evapotranspiration in the Haihe River Basin: Present observations and future projection from climatic variables through multi-model ensemble

NASA Astrophysics Data System (ADS)

Xing, Wanqiu; Wang, Weiguang; Shao, Quanxi; Peng, Shizhang; Yu, Zhongbo; Yong, Bin; Taylor, John

2014-04-01

As the most excellent indicator for hydrological cycle and a central link to water-balance calculations, the reference evapotranspiration (ET0) is of increasing importance in assessing the potential impacts of climate change on hydrology and water resources systems since the climate change has been becoming more pronounced. In this study, we conduct an investigation on the spatial and temporal changes in ET0 of the Haihe River Basin in present and future stages. The ET0 in the past five decades (1961-2010) are calculated by the Penman-Monteith method with historical climatic variables in 40 sites while the ET0 estimation for the future period of 2011-2099 is based on the related climatic variables projected by Coupled General Circulation Model (CGCM) multimodel ensemble projections in Phase 3 of the Coupled Model Intercomparison Project (CMIP3) using the Bayesian Model Average (BMA) approach. Results can be summarized for the present and future as follows. (1) No coherent spatial patterns in ET0 changes are seen in the whole basin. Half of the stations distributed mainly in the eastern and southeastern plain regions present significant negative trends, while only 3 stations in the western mountainous and plateau basin show significant positive trends. Radiation is mainly responsible for the ET0 change in the southern and eastern basin, whereas relative humidity and wind speed are the leading factors in the eastern coastal and north parts. (2) BMA ensemble method is competent to produce lower bias in comparison with other common methods in this basin. Future spatiotemporal ET0 pattern analysis by means of the BMA method based on the ensembles of four CGCMs suggested that although the spatial patterns under three scenarios are different in the forthcoming two decades, generally increasing trends can be found in the 21st century, which is mainly attributed to the significant increasing temperature. In addition, the implication of future ET0 change in agriculture and local water resources is discussed as an extension of this work. The results can provide beneficial reference and comprehensive information to understand the impact of climate change on the future water balance and improve the regional strategy for water resource and eco-environment management in the Haihe River Basin.

Observed and simulated changes in Antarctic sea ice and sea level pressure: anthropogenic or natural variability? (Invited)

NASA Astrophysics Data System (ADS)

Hobbs, W. R.

2013-12-01

Statistically-significant changes in Antarctic sea ice cover and the overlying atmosphere have been observed over the last 30 years, but there is an open question of whether these changes are due to multi-decadal natural variability or an anthropogenically-forced response. A number of recent papers have shown that the slight increase in total sea ice cover is within the bounds of internal variability exhibited by coupled climate models in the CMIP5 suite. Modelled changes for the same time period generally show a decrease, but again with a magnitude that is within internal variability. However, in contrast to the Arctic, sea ice tends in the Antarctic are spatially highly heterogeneous, and consideration of the total ice cover may mask important regional signals. In this work, a robust ';fingerprinting' approach is used to show that the observed spatial pattern of sea ice trends is in fact outside simulated natural variability in west Antarctic, and furthermore that the CMIP5 models consistently show decreased ice cover in the Ross and Weddell Seas, sectors which in fact have an observed increase in cover. As a first step towards understanding the disagreement between models and observations, modelled sea level pressure trends are analysed using and optimal fingerprinting approach, to identify whether atmospheric deficiencies in the models can explain the model-observation discrepancy.

Disentangling the uncertainty of hydrologic drought characteristics in a multi-model century-long experiment in continental river basins

NASA Astrophysics Data System (ADS)

Samaniego, Luis; Kumar, Rohini; Pechlivanidis, Illias; Breuer, Lutz; Wortmann, Michel; Vetter, Tobias; Flörke, Martina; Chamorro, Alejandro; Schäfer, David; Shah, Harsh; Zeng, Xiaofan

2016-04-01

The quantification of the predictive uncertainty in hydrologic models and their attribution to its main sources is of particular interest in climate change studies. In recent years, a number of studies have been aimed at assessing the ability of hydrologic models (HMs) to reproduce extreme hydrologic events. Disentangling the overall uncertainty of streamflow -including its derived low-flow characteristics- into individual contributions, stemming from forcings and model structure, has also been studied. Based on recent literature, it can be stated that there is a controversy with respect to which source is the largest (e.g., Teng, et al. 2012, Bosshard et al. 2013, Prudhomme et al. 2014). Very little has also been done to estimate the relative impact of the parametric uncertainty of the HMs with respect to overall uncertainty of low-flow characteristics. The ISI-MIP2 project provides a unique opportunity to understand the propagation of forcing and model structure uncertainties into century-long time series of drought characteristics. This project defines a consistent framework to deal with compatible initial conditions for the HMs and a set of standardized historical and future forcings. Moreover, the ensemble of hydrologic model predictions varies across a broad range of climate scenarios and regions. To achieve this goal, we use six preconditioned hydrologic models (HYPE or HBV, mHM, SWIM, VIC, and WaterGAP3) set up in seven large continental river basins: Amazon, Blue Nile, Ganges, Niger, Mississippi, Rhine, Yellow. These models are forced with bias-corrected outputs of five CMIP5 general circulation models (GCM) under four extreme representative concentration pathway (RCP) scenarios (i.e. 2.6, 4.5, 6.0, and 8.5 Wm-2) for the period 1971-2099. Simulated streamflow is transformed into a monthly runoff index (RI) to analyze the attribution of the GCM and HM uncertainty into drought magnitude and duration over time. Uncertainty contributions are investigated during periods: 1) 2006-2035, 2) 2036-2065 and 3) 2070-2099. Results presented in Samaniego et al. 2015 (submitted) indicate that GCM uncertainty mostly dominates over HM uncertainty for predictions of runoff drought characteristics, irrespective of the selected RCP and region. For the mHM model, in particular, GCM uncertainty always dominates over parametric uncertainty. In general, the overall uncertainty increases with time. The larger the radiative forcing of the RCP, the larger the uncertainty in drought characteristics, however, the propagation of the GCM uncertainty onto a drought characteristic depends largely upon the hydro-climatic regime. While our study emphasizes the need for multi-model ensembles for the assessment of future drought projections, the agreement between GCM forcings is still weak to draw conclusive recommendations. References: L. Samaniego, R. Kumar, I. G. Pechlivanidis, L. Breuer, M. Wortmann, T. Vetter, M. Flörke, A. Chamorro, D. Schäfer, H. Shah, X. Zeng: Propagation of forcing and model uncertainty into hydrological drought characteristics in a multi-model century-long experiment in continental river basins. Submitted to Climatic Change on Dec 2015. Bosshard, et al. 2013. doi:10.1029/2011WR011533. Prudhomme et al. 2014, doi:10.1073/pnas.1222473110. Teng, et al. 2012, doi:10.1175/JHM-D-11-058.1.

Control vocabulary software designed for CMIP6

NASA Astrophysics Data System (ADS)

Nadeau, D.; Taylor, K. E.; Williams, D. N.; Ames, S.

2016-12-01

The Coupled Model Intercomparison Project Phase 6 (CMIP6) coordinates a number of intercomparison activities and includes many more experiments than its predecessor, CMIP5. In order to organize and facilitate use of the complex collection of expected CMIP6 model output, a standard set of descriptive information has been defined, which must be stored along with the data. This standard information enables automated machine interpretation of the contents of all model output files. The standard metadata is stored in compliance with the Climate and Forecast (CF) standard, which ensures that it can be interpreted and visualized by many standard software packages. Additional attributes (not standardized by CF) are required by CMIP6 to enhance identification of models and experiments, and to provide additional information critical for interpreting the model results. To ensure that CMIP6 data complies with the standards, a python program called "PrePARE" (Pre-Publication Attribute Reviewer for the ESGF) has been developed to check the model output prior to its publication and release for analysis. If, for example, a required attribute is missing or incorrect (e.g., not included in the reference CMIP6 controlled vocabularies), then PrePare will prevent publication. In some circumstances, missing attributes can be created or incorrect attributes can be replaced automatically by PrePARE, and the program will warn users about the changes that have been made. PrePARE provides a final check on model output assuring adherence to a baseline conformity across the output from all CMIP6 models which will facilitate analysis by climate scientists. PrePARE is flexible and can be easily modified for use by similar projects that have a well-defined set of metadata and controlled vocabularies.

Using ensemble models to identify and apportion heavy metal pollution sources in agricultural soils on a local scale.

PubMed

Wang, Qi; Xie, Zhiyi; Li, Fangbai

2015-11-01

This study aims to identify and apportion multi-source and multi-phase heavy metal pollution from natural and anthropogenic inputs using ensemble models that include stochastic gradient boosting (SGB) and random forest (RF) in agricultural soils on the local scale. The heavy metal pollution sources were quantitatively assessed, and the results illustrated the suitability of the ensemble models for the assessment of multi-source and multi-phase heavy metal pollution in agricultural soils on the local scale. The results of SGB and RF consistently demonstrated that anthropogenic sources contributed the most to the concentrations of Pb and Cd in agricultural soils in the study region and that SGB performed better than RF. Copyright © 2015 Elsevier Ltd. All rights reserved.

Recent intensification of the Walker Circulation and the role of natural sea surface temperature variability

NASA Astrophysics Data System (ADS)

Zhao, X.; Allen, R.

2017-12-01

In a warming world, the tropical atmospheric overturning circulation-including the Walker Circulation-is expected to weaken due to thermodynamic constraints. Tropical precipitation increases at a slower rate than water vapor-which increases according to Clausius Clapeyron scaling, assuming constant relative humidity-so the tropical overturning circulation slows down. This is supported by both observations and model simulations, which show a slowdown of the Walker Circulation over the 20th century. Model projections suggest a further weakening of the Walker Circulation in the 21st century. However, over the last several decades (1979-2014), multiple observations reveal a robust strengthening of the Walker Circulation. Although coupled CMIP5 simulations are unable to reproduce this strengthening, AMIP simulations-which feature the observed evolution of SSTs-are generally able to reproduce it. Assuming the ensemble mean sea surface temperatures (SSTs) from historical CMIP5 simulations accurately represent the externally forced SST response, the observed SSTs can be decomposed into a forced and an unforced component. CAM5 AMIP-type simulations driven by the unforced component of observed SSTs reproduce the observed strengthening of the Walker Circulation. Corresponding simulations driven by the forced component of observed SSTs yield a weaker Walker Circulation. These results are consistent with the zonal tropical SST gradient and the Bjerknes feedback. The unforced component of SSTs yield an increased SST gradient over tropical Pacific (a La Nina like pattern) and strengthening of the tropical trade winds, which constitute the lower branch of the Walker Circulation. The forced component of SSTs yields a zonally uniform tropical Pacific SST warming and a marginal weakening of the Walker Circulation. Our results suggest significant modulation of the tropical Walker Circulation by natural SST variability over the last several decades.

NASA Center for Climate Simulation (NCCS) Presentation

NASA Technical Reports Server (NTRS)

Webster, William P.

2012-01-01

The NASA Center for Climate Simulation (NCCS) offers integrated supercomputing, visualization, and data interaction technologies to enhance NASA's weather and climate prediction capabilities. It serves hundreds of users at NASA Goddard Space Flight Center, as well as other NASA centers, laboratories, and universities across the US. Over the past year, NCCS has continued expanding its data-centric computing environment to meet the increasingly data-intensive challenges of climate science. We doubled our Discover supercomputer's peak performance to more than 800 teraflops by adding 7,680 Intel Xeon Sandy Bridge processor-cores and most recently 240 Intel Xeon Phi Many Integrated Core (MIG) co-processors. A supercomputing-class analysis system named Dali gives users rapid access to their data on Discover and high-performance software including the Ultra-scale Visualization Climate Data Analysis Tools (UV-CDAT), with interfaces from user desktops and a 17- by 6-foot visualization wall. NCCS also is exploring highly efficient climate data services and management with a new MapReduce/Hadoop cluster while augmenting its data distribution to the science community. Using NCCS resources, NASA completed its modeling contributions to the Intergovernmental Panel on Climate Change (IPCG) Fifth Assessment Report this summer as part of the ongoing Coupled Modellntercomparison Project Phase 5 (CMIP5). Ensembles of simulations run on Discover reached back to the year 1000 to test model accuracy and projected climate change through the year 2300 based on four different scenarios of greenhouse gases, aerosols, and land use. The data resulting from several thousand IPCC/CMIP5 simulations, as well as a variety of other simulation, reanalysis, and observationdatasets, are available to scientists and decision makers through an enhanced NCCS Earth System Grid Federation Gateway. Worldwide downloads have totaled over 110 terabytes of data.

DOE Office of Scientific and Technical Information (OSTI.GOV)

LaRow, Timothy

The SSTs used in our study come from the Community Climate System Model version 4 (CCSM4) (Gent et al 2011) and from the Canadian Centre for Climate Modeling and Analysis (CanESM2) (Chylek et al20ll) climate models from the fifth Coupled Model Intercomparison Project (CMIP5) (Taylor et al2012). We've examined the tropical cyclones using both the historical simulation that employs volcanic and aerosol forcing as well as the representative concentration pathway 4.5 (RCP4.5). In addition, we've compared the present day North Atlantic tropical cyclone metrics from a previous study (LaRow, 2013) to these climate change experiments. The experimental setup is shownmore » in Table 1. We considered the CMIP5 experiment number '3.2 historical' (Taylor et al,201l), which provides simulations of the recent past (1850-2005). The second set of CMIP5 SSTs is the RCp4.5 experiment where the radiative forcing stabilizes at 45W m-2 after 2100 (experiment number 4.1 in Taylor etal2}ll).« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yim, Bo; Yeh, Sang -Wook; Sohn, Byung -Ju

Observational evidence shows that the Walker circulation (WC) in the tropical Pacific has strengthened in recent decades. In this study, we examine the WC trend for 1979–2005 and its relationship with the precipitation associated with the El Niño Southern Oscillation (ENSO) using the sea surface temperature (SST)-constrained Atmospheric Model Intercomparison Project (AMIP) simulations of the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. All of the 29 models show a strengthening of the WC trend in response to an increase in the SST zonal gradient along the equator. Despite the same SST-constrained AMIP simulations, however, a large diversity ismore » found among the CMIP5 climate models in the magnitude of the WC trend. The relationship between the WC trend and precipitation anomalies (PRCPAs) associated with ENSO (ENSO-related PRCPAs) shows that the longitudinal position of the ENSO-related PRCPAs in the western tropical Pacific is closely related to the magnitude of the WC trend. Specifically, it is found that the strengthening of the WC trend is large (small) in the CMIP5 AMIP simulations in which the ENSO-related PRCPAs are located relatively westward (eastward) in the western tropical Pacific. Furthermore, the zonal shift of the ENSO-related precipitation in the western tropical Pacific, which is associated with the climatological mean precipitation in the tropical Pacific, could play an important role in modifying the WC trend in the CMIP5 climate models.« less

Climate projections and extremes in dynamically downscaled CMIP5 model outputs over the Bengal delta: a quartile based bias-correction approach with new gridded data

NASA Astrophysics Data System (ADS)

Hasan, M. Alfi; Islam, A. K. M. Saiful; Akanda, Ali Shafqat

2017-11-01

In the era of global warning, the insight of future climate and their changing extremes is critical for climate-vulnerable regions of the world. In this study, we have conducted a robust assessment of Regional Climate Model (RCM) results in a monsoon-dominated region within the new Coupled Model Intercomparison Project Phase 5 (CMIP5) and the latest Representative Concentration Pathways (RCP) scenarios. We have applied an advanced bias correction approach to five RCM simulations in order to project future climate and associated extremes over Bangladesh, a critically climate-vulnerable country with a complex monsoon system. We have also generated a new gridded product that performed better in capturing observed climatic extremes than existing products. The bias-correction approach provided a notable improvement in capturing the precipitation extremes as well as mean climate. The majority of projected multi-model RCMs indicate an increase of rainfall, where one model shows contrary results during the 2080s (2071-2100) era. The multi-model mean shows that nighttime temperatures will increase much faster than daytime temperatures and the average annual temperatures are projected to be as hot as present-day summer temperatures. The expected increase of precipitation and temperature over the hilly areas are higher compared to other parts of the country. Overall, the projected extremities of future rainfall are more variable than temperature. According to the majority of the models, the number of the heavy rainy days will increase in future years. The severity of summer-day temperatures will be alarming, especially over hilly regions, where winters are relatively warm. The projected rise of both precipitation and temperature extremes over the intense rainfall-prone northeastern region of the country creates a possibility of devastating flash floods with harmful impacts on agriculture. Moreover, the effect of bias-correction, as presented in probable changes of both bias-corrected and uncorrected extremes, can be considered in future policy making.

Data citation in climate sciences: Improvements in CMIP6 compared to CMIP5

NASA Astrophysics Data System (ADS)

Stockhause, M.; Lautenschlager, M.

2017-12-01

Within CMIP5 (Coupled Model Intercomparison Project Phase 5) the citation of the data was not possible prior its long-term archival in the IPCC Data Distribution Centre (DDC). The Reference Data Archive for AR5 (Assessment Report 5) was built up after the submission deadline for part 1 of the AR5. This was too late for many scientific articles. But even the AR5 data in the IPCC DDC is rarely cited in literature in spite of annual download volumes between one and three PBytes. On the other hand, the request for a citation possibility for the evolving CMIP6 data prior to long-term archival came from the CMIP6 data providers. The additional provision of data citations for the project input4MIPs (input data for CMIP6) could raise the scientists' awareness of the discrepancy between the readiness to cite data and the desire to be cited and get credit. The CMIP6 Citation Service is a pragmatic approach built on existing services and services under development, such as ESGF (Earth System Grid Federation) as data infrastructure component, DataCite as DOI registration agency, and Scholix services for tracking data usage information. Other principles followed to overcome barriers of data citation are: Collect data and literature references in the data citation metadata to enable data-data and data-literature interlinking. Visibility of data citation information in the ESGF data portals (low barrier to access data citation information) Provide data usage information in literature for the data providers, data node managers and their funders (requested by some ESGF data node managers) The CMIP6 Citation Service is an implementation only of the credit part of the RDA WGDC recommendation for the citation of dynamic data. The second part, the identification of the data subset underlying an article, is planned for CMIP7 as a data cart approach comprising multiple pre-defined CMIP6 DataCite DOIs. Additional policies on the long-term data availability are required. References: M. Stockhause and M. Lautenschlager (2017). CMIP6 Data Citation of Evolving Data. Data Science Journal. 16, p.30. doi:10.5334/dsj-2017-030. https://doi.org/10.5334/dsj-2017-030 . http://cmip6cite.wdc-climate.de

Impact of state updating and multi-parametric ensemble for streamflow hindcasting in European river basins

NASA Astrophysics Data System (ADS)

Noh, S. J.; Rakovec, O.; Kumar, R.; Samaniego, L. E.

2015-12-01

Accurate and reliable streamflow prediction is essential to mitigate social and economic damage coming from water-related disasters such as flood and drought. Sequential data assimilation (DA) may facilitate improved streamflow prediction using real-time observations to correct internal model states. In conventional DA methods such as state updating, parametric uncertainty is often ignored mainly due to practical limitations of methodology to specify modeling uncertainty with limited ensemble members. However, if parametric uncertainty related with routing and runoff components is not incorporated properly, predictive uncertainty by model ensemble may be insufficient to capture dynamics of observations, which may deteriorate predictability. Recently, a multi-scale parameter regionalization (MPR) method was proposed to make hydrologic predictions at different scales using a same set of model parameters without losing much of the model performance. The MPR method incorporated within the mesoscale hydrologic model (mHM, http://www.ufz.de/mhm) could effectively represent and control uncertainty of high-dimensional parameters in a distributed model using global parameters. In this study, we evaluate impacts of streamflow data assimilation over European river basins. Especially, a multi-parametric ensemble approach is tested to consider the effects of parametric uncertainty in DA. Because augmentation of parameters is not required within an assimilation window, the approach could be more stable with limited ensemble members and have potential for operational uses. To consider the response times and non-Gaussian characteristics of internal hydrologic processes, lagged particle filtering is utilized. The presentation will be focused on gains and limitations of streamflow data assimilation and multi-parametric ensemble method over large-scale basins.

Landfast ice thickness in the Canadian Arctic Archipelago from observations and models

NASA Astrophysics Data System (ADS)

Howell, Stephen E. L.; Laliberté, Frédéric; Kwok, Ron; Derksen, Chris; King, Joshua

2016-07-01

Observed and modelled landfast ice thickness variability and trends spanning more than 5 decades within the Canadian Arctic Archipelago (CAA) are summarized. The observed sites (Cambridge Bay, Resolute, Eureka and Alert) represent some of the Arctic's longest records of landfast ice thickness. Observed end-of-winter (maximum) trends of landfast ice thickness (1957-2014) were statistically significant at Cambridge Bay (-4.31 ± 1.4 cm decade-1), Eureka (-4.65 ± 1.7 cm decade-1) and Alert (-4.44 ± 1.6 cm -1) but not at Resolute. Over the 50+-year record, the ice thinned by ˜ 0.24-0.26 m at Cambridge Bay, Eureka and Alert with essentially negligible change at Resolute. Although statistically significant warming in spring and fall was present at all sites, only low correlations between temperature and maximum ice thickness were present; snow depth was found to be more strongly associated with the negative ice thickness trends. Comparison with multi-model simulations from Coupled Model Intercomparison project phase 5 (CMIP5), Ocean Reanalysis Intercomparison (ORA-IP) and Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) show that although a subset of current generation models have a "reasonable" climatological representation of landfast ice thickness and distribution within the CAA, trends are unrealistic and far exceed observations by up to 2 orders of magnitude. ORA-IP models were found to have positive correlations between temperature and ice thickness over the CAA, a feature that is inconsistent with both observations and coupled models from CMIP5.

Downscaling RCP8.5 daily temperatures and precipitation in Ontario using localized ensemble optimal interpolation (EnOI) and bias correction

NASA Astrophysics Data System (ADS)

Deng, Ziwang; Liu, Jinliang; Qiu, Xin; Zhou, Xiaolan; Zhu, Huaiping

2017-10-01

A novel method for daily temperature and precipitation downscaling is proposed in this study which combines the Ensemble Optimal Interpolation (EnOI) and bias correction techniques. For downscaling temperature, the day to day seasonal cycle of high resolution temperature of the NCEP climate forecast system reanalysis (CFSR) is used as background state. An enlarged ensemble of daily temperature anomaly relative to this seasonal cycle and information from global climate models (GCMs) are used to construct a gain matrix for each calendar day. Consequently, the relationship between large and local-scale processes represented by the gain matrix will change accordingly. The gain matrix contains information of realistic spatial correlation of temperature between different CFSR grid points, between CFSR grid points and GCM grid points, and between different GCM grid points. Therefore, this downscaling method keeps spatial consistency and reflects the interaction between local geographic and atmospheric conditions. Maximum and minimum temperatures are downscaled using the same method. For precipitation, because of the non-Gaussianity issue, a logarithmic transformation is used to daily total precipitation prior to conducting downscaling. Cross validation and independent data validation are used to evaluate this algorithm. Finally, data from a 29-member ensemble of phase 5 of the Coupled Model Intercomparison Project (CMIP5) GCMs are downscaled to CFSR grid points in Ontario for the period from 1981 to 2100. The results show that this method is capable of generating high resolution details without changing large scale characteristics. It results in much lower absolute errors in local scale details at most grid points than simple spatial downscaling methods. Biases in the downscaled data inherited from GCMs are corrected with a linear method for temperatures and distribution mapping for precipitation. The downscaled ensemble projects significant warming with amplitudes of 3.9 and 6.5 °C for 2050s and 2080s relative to 1990s in Ontario, respectively; Cooling degree days and hot days will significantly increase over southern Ontario and heating degree days and cold days will significantly decrease in northern Ontario. Annual total precipitation will increase over Ontario and heavy precipitation events will increase as well. These results are consistent with conclusions in many other studies in the literature.

Decadal simulation and comprehensive evaluation of CESM/CAM5.1 with advanced chemistry, aerosol microphysics, and aerosol-cloud interactions

NASA Astrophysics Data System (ADS)

He, Jian; Zhang, Yang; Glotfelty, Tim; He, Ruoying; Bennartz, Ralf; Rausch, John; Sartelet, Karine

2015-03-01

Earth system models have been used for climate predictions in recent years due to their capabilities to include biogeochemical cycles, human impacts, as well as coupled and interactive representations of Earth system components (e.g., atmosphere, ocean, land, and sea ice). In this work, the Community Earth System Model (CESM) with advanced chemistry and aerosol treatments, referred to as CESM-NCSU, is applied for decadal (2001-2010) global climate predictions. A comprehensive evaluation is performed focusing on the atmospheric component—the Community Atmosphere Model version 5.1 (CAM5.1) by comparing simulation results with observations/reanalysis data and CESM ensemble simulations from the Coupled Model Intercomparison Project phase 5 (CMIP5). The improved model can predict most meteorological and radiative variables relatively well with normalized mean biases (NMBs) of -14.1 to -9.7% and 0.7-10.8%, respectively, although temperature at 2 m (T2) is slightly underpredicted. Cloud variables such as cloud fraction (CF) and precipitating water vapor (PWV) are well predicted, with NMBs of -10.5 to 0.4%, whereas cloud condensation nuclei (CCN), cloud liquid water path (LWP), and cloud optical thickness (COT) are moderately-to-largely underpredicted, with NMBs of -82.2 to -31.2%, and cloud droplet number concentration (CDNC) is overpredictd by 26.7%. These biases indicate the limitations and uncertainties associated with cloud microphysics (e.g., resolved clouds and subgrid-scale cumulus clouds). Chemical concentrations over the continental U.S. (CONUS) (e.g., SO42-, Cl-, OC, and PM2.5) are reasonably well predicted with NMBs of -12.8 to -1.18%. Concentrations of SO2, SO42-, and PM10 are also reasonably well predicted over Europe with NMBs of -20.8 to -5.2%, so are predictions of SO2 concentrations over the East Asia with an NMB of -18.2%, and the tropospheric ozone residual (TOR) over the globe with an NMB of -3.5%. Most meteorological and radiative variables predicted by CESM-NCSU agree well overall with those predicted by CESM-CMIP5. The performance of LWP and AOD predicted by CESM-NCSU is better than that of CESM-CMIP5 in terms of model bias and correlation coefficients. Large biases for some chemical predictions can be attributed to uncertainties in the emissions of precursor gases (e.g., SO2, NH3, and NOx) and primary aerosols (black carbon and primary organic matter) as well as uncertainties in formulations of some model components (e.g., online dust and sea-salt emissions, secondary organic aerosol formation, and cloud microphysics). Comparisons of CESM simulation with baseline emissions and 20% of anthropogenic emissions from the baseline emissions indicate that anthropogenic gas and aerosol species can decrease downwelling shortwave radiation (FSDS) by 4.7 W m-2 (or by 2.9%) and increase SWCF by 3.2 W m-2 (or by 3.1%) in the global mean.

Indirect aerosol effect increases CMIP5 models projected Arctic warming

DOE PAGES

Chylek, Petr; Vogelsang, Timothy J.; Klett, James D.; ...

2016-02-20

Phase 5 of the Coupled Model Intercomparison Project (CMIP5) climate models’ projections of the 2014–2100 Arctic warming under radiative forcing from representative concentration pathway 4.5 (RCP4.5) vary from 0.9° to 6.7°C. Climate models with or without a full indirect aerosol effect are both equally successful in reproducing the observed (1900–2014) Arctic warming and its trends. However, the 2014–2100 Arctic warming and the warming trends projected by models that include a full indirect aerosol effect (denoted here as AA models) are significantly higher (mean projected Arctic warming is about 1.5°C higher) than those projected by models without a full indirect aerosolmore » effect (denoted here as NAA models). The suggestion is that, within models including full indirect aerosol effects, those projecting stronger future changes are not necessarily distinguishable historically because any stronger past warming may have been partially offset by stronger historical aerosol cooling. In conclusion, the CMIP5 models that include a full indirect aerosol effect follow an inverse radiative forcing to equilibrium climate sensitivity relationship, while models without it do not.« less

Indirect aerosol effect increases CMIP5 models projected Arctic warming

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chylek, Petr; Vogelsang, Timothy J.; Klett, James D.

Phase 5 of the Coupled Model Intercomparison Project (CMIP5) climate models’ projections of the 2014–2100 Arctic warming under radiative forcing from representative concentration pathway 4.5 (RCP4.5) vary from 0.9° to 6.7°C. Climate models with or without a full indirect aerosol effect are both equally successful in reproducing the observed (1900–2014) Arctic warming and its trends. However, the 2014–2100 Arctic warming and the warming trends projected by models that include a full indirect aerosol effect (denoted here as AA models) are significantly higher (mean projected Arctic warming is about 1.5°C higher) than those projected by models without a full indirect aerosolmore » effect (denoted here as NAA models). The suggestion is that, within models including full indirect aerosol effects, those projecting stronger future changes are not necessarily distinguishable historically because any stronger past warming may have been partially offset by stronger historical aerosol cooling. In conclusion, the CMIP5 models that include a full indirect aerosol effect follow an inverse radiative forcing to equilibrium climate sensitivity relationship, while models without it do not.« less

Assessment and economic valuation of air pollution impacts on human health over Europe and the United States as calculated by a multi-model ensemble in the framework of AQMEII3

EPA Science Inventory

The impact of air pollution on human health and the associated external costs in Europe and the United States (US) for the year 2010 are modeled by a multi-model ensemble of regional models in the frame of the third phase of the Air Quality Modelling Evaluation International Init...

Evaluation of Multi-Model Ensemble System for Seasonal and Monthly Prediction

NASA Astrophysics Data System (ADS)

Zhang, Q.; Van den Dool, H. M.

2013-12-01

Since August 2011, the realtime seasonal forecasts of U.S. National Multi-Model Ensemble (NMME) have been made on 8th of each month by NCEP Climate Prediction Center (CPC). During the first year, the participating models were NCEP/CFSv1&2, GFDL/CM2.2, NCAR/U.Miami/COLA/CCSM3, NASA/GEOS5, IRI/ ECHAM-a & ECHAM-f for the realtime NMME forecast. The Canadian Meteorological Center CanCM3 and CM4 replaced the CFSv1 and IRI's models in the second year. The NMME team at CPC collects three variables, including precipitation, 2-meter temperature and sea surface temperature from each modeling center on a 1x1 global grid, removes systematic errors, makes the grand ensemble mean with equal weight for each model and constructs a probability forecast with equal weight for each member. The team then provides the NMME forecast to the operational CPC forecaster responsible for the seasonal and monthly outlook each month. Verification of the seasonal and monthly prediction from NMME is conducted by calculating the anomaly correlation (AC) from the 30-year hindcasts (1982-2011) of individual model and NMME ensemble. The motivation of this study is to provide skill benchmarks for future improvements of the NMME seasonal and monthly prediction system. The experimental (Phase I) stage of the project already supplies routine guidance to users of the NMME forecasts.

Intersections of downscaling, the ethics of climate services, and regional research grand challenges.

NASA Astrophysics Data System (ADS)

Hewitson, B.; Jack, C. D.; Gutowski, W. J., Jr.

2014-12-01

Possibly the leading complication for users of climate information for policy and adaptation is the confusing mix of contrasting data sets that offer widely differing (and often times fundamentally contradictory) indications of the magnitude and direction of past and future regional climate change. In this light, the most pressing scientific-societal challenge is the need to find new ways to understand the sources of conflicting messages from multi-model, multi-method and multi-scale disparities, to develop and implement new analytical methodologies to address this difficulty and so to advance the interpretation and communication of robust climate information to decision makers. Compounding this challenge is the growth of climate services which, in view of the confusing mix of climate change messages, raises serious concerns as to the ethics of communication and dissemination of regional climate change data.The Working Group on Regional Climate (WGRC) of the World Climate Research Program (WCRP) oversees the CORDEX downscaling program which offers a systematic approach to compare the CMIP5 GCMs alongside RCMs and Empirical-statistical (ESD) downscaling within a common experimental design, and which facilitates the evaluation and assessment of the relative information content and sources of error. Using results from the CORDEX RCM and ESD evaluation experiment, and set against the regional messages from the CMIP5 GCMs, we examine the differing messages that arise from each data source. These are then considered in terms of the implications of consequence if each data source were to be independently adopted in a real world use-case scenario. This is then cast in the context of the emerging developments on the distillation dilemma - where the pressing need is for multi-method integration - and how this relates to the WCRP regional research grand challenges.

Forest-stressing climate factors on the US West Coast as simulated by CMIP5

NASA Astrophysics Data System (ADS)

Rupp, D. E.; Buotte, P.; Hicke, J. A.; Law, B. E.; Mote, P.; Sharp, D.; Zhenlin, Y.

2013-12-01

The rate of forest mortality has increased significantly in western North America since the 1970s. Causes include insect attacks, fire, and soil water deficit, all of which are interdependent. We first identify climate factors that stress forests by reducing photosynthesis and hydraulic conductance, and by promoting bark beetle infestation and wildfire. Examples of such factors may be two consecutive years of extreme summer precipitation deficit, or prolonged vapor pressure deficit exceeding some threshold. Second, we quantify the frequency and magnitude of these climate factors in 20th and 21st century climates, as simulated by global climate models (GCMs) in Coupled Model Intercomparison Project phase 5 (CMIP5), of Washington, Oregon, and California in the western US. Both ';raw' (i.e., original spatial resolution) and statistically downscaled simulations are considered, the latter generated using the Multivariate Adaptive Constructed Analogs (MACA) method. CMIP5 models that most faithfully reproduce the observed historical statistics of these climate factors are identified. Furthermore, significant changes in the statistics between the 20th and 21st centuries are reported. A subsequent task will be to use a selected subset of MACA-downscaled CMIP5 simulations to force the Community Land Model, version 4.5 (CLM 4.5). CLM 4.5 will be modified to better simulate forest mortality and to couple CLM with an economic model. The ultimate goal of this study is to understand the interactions and the feedbacks by which the market and the forest ecosystem influence each other.

Evaluating CMIP5 Simulations of Historical Continental Climate with Koeppen Bioclimatic Metrics

NASA Astrophysics Data System (ADS)

Phillips, T. J.; Bonfils, C.

2013-12-01

The classic Koeppen bioclimatic classification scheme associates generic vegetation types (e.g. grassland, tundra, broadleaf or evergreen forests, etc.) with regional climate zones defined by their annual cycles of continental temperature (T) and precipitation (P), considered together. The locations or areas of Koeppen vegetation types derived from observational data thus can provide concise metrical standards for simultaneously evaluating climate simulations of T and P in naturally defined regions. The CMIP5 models' collective ability to correctly represent two variables that are critically important for living organisms at regional scales is therefore central to this evaluation. For this study, 14 Koeppen vegetation types are derived from annual-cycle climatologies of T and P in some 3 dozen CMIP5 simulations of the 1980-1999 period. Metrics for evaluating the ability of the CMIP5 models to simulate the correct locations and areas of each vegetation type, as well as measures of overall model performance, also are developed. It is found that the CMIP5 models are generally most deficient in simulating: 1) climates of drier Koeppen zones (e.g. desert, savanna, grassland, steppe vegetation types) located in the southwestern U.S. and Mexico, eastern Europe, southern Africa, and central Australia; 2) climates of regions such as central Asia and western South America where topography plays a key role. Details of regional T or P biases in selected simulations that exemplify general model performance problems also will be presented. Acknowledgments: This work was funded by the U.S. Department of Energy Office of Science and was performed at the Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Map of Koeppen vegetation types derived from observed T and P.

Evaluation of CMIP5 twentieth century rainfall simulation over the equatorial East Africa

NASA Astrophysics Data System (ADS)

Ongoma, Victor; Chen, Haishan; Gao, Chujie

2018-02-01

This study assesses the performance of 22 Coupled Model Intercomparison Project Phase 5 (CMIP5) historical simulations of rainfall over East Africa (EA) against reanalyzed datasets during 1951-2005. The datasets were sourced from Global Precipitation Climatology Centre (GPCC) and Climate Research Unit (CRU). The metrics used to rank CMIP5 Global Circulation Models (GCMs) based on their performance in reproducing the observed rainfall include correlation coefficient, standard deviation, bias, percentage bias, root mean square error, and trend. Performances of individual models vary widely. The overall performance of the models over EA is generally low. The models reproduce the observed bimodal rainfall over EA. However, majority of them overestimate and underestimate the October-December (OND) and March-May (MAM) rainfall, respectively. The monthly (inter-annual) correlation between model and reanalyzed is high (low). More than a third of the models show a positive bias of the annual rainfall. High standard deviation in rainfall is recorded in the Lake Victoria Basin, central Kenya, and eastern Tanzania. A number of models reproduce the spatial standard deviation of rainfall during MAM season as compared to OND. The top eight models that produce rainfall over EA relatively well are as follows: CanESM2, CESM1-CAM5, CMCC-CESM, CNRM-CM5, CSIRO-Mk3-6-0, EC-EARTH, INMCM4, and MICROC5. Although these results form a fairly good basis for selection of GCMs for carrying out climate projections and downscaling over EA, it is evident that there is still need for critical improvement in rainfall-related processes in the models assessed. Therefore, climate users are advised to use the projections of rainfall from CMIP5 models over EA cautiously when making decisions on adaptation to or mitigation of climate change.

Scenarios Based on Shared Socioeconomic Pathway Assumptions

NASA Astrophysics Data System (ADS)

Edmonds, J.

2013-12-01

A set of new scenarios is being developed by the international scientific community as part of a larger program that was articulated in Moss, et al. (2009), published in Nature. A long series of meetings including climate researchers drawn from the climate modeling, impacts, adaptation and vulnerability (IAV) and integrated assessment modeling (IAM) communities have led to the development of a set of five Shared Socioeconomic Pathways (SSPs), which define the state of human and natural societies at a macro scale over the course of the 21st century without regard to climate mitigation or change. SSPs were designed to explore a range of possible futures consistent with greater or lesser challenges to mitigation and challenges to adaptation. They include a narrative storyline and a set of quantified measures--e.g. demographic and economic profiles--that define the high-level state of society as it evolves over the 21st century under the assumption of no significant climate feedback. SSPs can be used to develop quantitative scenarios of human Earth systems using IAMs. IAMs produce information about greenhouse gas emissions, energy systems, the economy, agriculture and land use. Each set of SSPs will have a different human Earth system realization for each IAM. Five groups from the IAM community have begun to explore the implications of SSP assumptions for emissions, energy, economy, agriculture and land use. We report the quantitative results of initial experiments from those groups. A major goal of the Moss, et al. strategy was to enable the use of CMIP5 climate model ensemble products for IAV research. CMIP5 climate scenarios used four Representative Concentration Pathway (RCP) scenarios, defined in terms of radiative forcing in the year 2100: 2.6, 4.5, 6.0, and 8.5 Wm-2. There is no reason to believe that the SSPs will generate year 2100 levels of radiative forcing that correspond to the four RCP levels, though it is important that at least one SSP produce a scenario with at least 8.5 Wm-2. To address this problem each SSP scenario can be treated as a reference scenario, to which emissions mitigation policies can be applied to create a set of RCP replications. These RCP replications have the underlying SSP socio-economic assumptions in addition to policy assumptions and radiative forcing levels consistent with the CMIP5 products. We report quantitative results of initial experiments from the five participating groups.

Documenting Climate Models and Simulations: the ES-DOC Ecosystem in Support of CMIP

NASA Astrophysics Data System (ADS)

Pascoe, C. L.; Guilyardi, E.

2017-12-01

The results of climate models are of increasing and widespread importance. No longer is climate model output of sole interest to climate scientists and researchers in the climate change impacts and adaptation fields. Now non-specialists such as government officials, policy-makers, and the general public, all have an increasing need to access climate model output and understand its implications. For this host of users, accurate and complete metadata (i.e., information about how and why the data were produced) is required to document the climate modeling results. Here we describe the ES-DOC community-govern project to collect and make available documentation of climate models and their simulations for the internationally coordinated modeling activity CMIP6 (Coupled Model Intercomparison Project, Phase 6). An overview of the underlying standards, key properties and features, the evolution from CMIP5, the underlying tools and workflows as well as what modelling groups should expect and how they should engage with the documentation of their contribution to CMIP6 is also presented.

Historical simulations and climate change projections over India by NCAR CCSM4: CMIP5 vs. NEX-GDDP

NASA Astrophysics Data System (ADS)

Sahany, Sandeep; Mishra, Saroj Kanta; Salunke, Popat

2018-03-01

A new bias-corrected statistically downscaled product, namely, the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), has recently been developed by NASA to help the scientific community in climate change impact studies at local to regional scale. In this work, the product is validated over India and its added value as compared to its CMIP5 counterpart for the NCAR CCSM4 model is analyzed, followed by climate change projections under the RCP8.5 global warming scenario using the two datasets for the variables daily maximum 2-m air temperature (Tmax), daily minimum 2-m air temperature (Tmin), and rainfall. It is found that, overall, the CCSM4-NEX-GDDP significantly reduces many of the biases in CCSM4-CMIP5 for the historical simulations; however, some biases such as the significant overestimation in the frequency of occurrence in the lower tail of the Tmax and Tmin still remain. In regard to rainfall, an important value addition in CCSM4-NEX-GDDP is the alleviation of the significant underestimation of rainfall extremes found in CCSM4-CMIP5. The projected Tmax from CCSM4-NEX-GDDP are in general higher than that projected by CCSM4-CMIP5, suggesting that the risks of heat waves and very hot days could be higher than that projected by the latter. CCSM4-NEX-GDDP projects the frequency of occurrence of the upper extreme values of historical Tmax to increase by a factor of 100 towards the end of century (as opposed to a factor of 10 increase projected by CCSM4-CMIP5). In regard to rainfall, both CCSM4-CMIP5 and CCSM4-NEX-GDDP project an increase in annual rainfall over India under the RCP8.5 global warming scenario progressively from the near term through the far term. However, CCSM4-NEX-GDDP consistently projects a higher magnitude of increase and over a larger area as compared to that projected by CCSM4-CMIP5. Projected daily rainfall distributions from CCSM4-CMIP5 and CCSM4-NEX-GDDP suggest the occurrence of events that have no historical precedents. Worth noting is that the extreme daily rainfall values projected by CCSM4-NEX-GDDP are two to three times larger than that projected by CCSM4-CMIP5.

Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5

NASA Astrophysics Data System (ADS)

Dufresne, J.-L.; Foujols, M.-A.; Denvil, S.; Caubel, A.; Marti, O.; Aumont, O.; Balkanski, Y.; Bekki, S.; Bellenger, H.; Benshila, R.; Bony, S.; Bopp, L.; Braconnot, P.; Brockmann, P.; Cadule, P.; Cheruy, F.; Codron, F.; Cozic, A.; Cugnet, D.; de Noblet, N.; Duvel, J.-P.; Ethé, C.; Fairhead, L.; Fichefet, T.; Flavoni, S.; Friedlingstein, P.; Grandpeix, J.-Y.; Guez, L.; Guilyardi, E.; Hauglustaine, D.; Hourdin, F.; Idelkadi, A.; Ghattas, J.; Joussaume, S.; Kageyama, M.; Krinner, G.; Labetoulle, S.; Lahellec, A.; Lefebvre, M.-P.; Lefevre, F.; Levy, C.; Li, Z. X.; Lloyd, J.; Lott, F.; Madec, G.; Mancip, M.; Marchand, M.; Masson, S.; Meurdesoif, Y.; Mignot, J.; Musat, I.; Parouty, S.; Polcher, J.; Rio, C.; Schulz, M.; Swingedouw, D.; Szopa, S.; Talandier, C.; Terray, P.; Viovy, N.; Vuichard, N.

2013-05-01

We present the global general circulation model IPSL-CM5 developed to study the long-term response of the climate system to natural and anthropogenic forcings as part of the 5th Phase of the Coupled Model Intercomparison Project (CMIP5). This model includes an interactive carbon cycle, a representation of tropospheric and stratospheric chemistry, and a comprehensive representation of aerosols. As it represents the principal dynamical, physical, and bio-geochemical processes relevant to the climate system, it may be referred to as an Earth System Model. However, the IPSL-CM5 model may be used in a multitude of configurations associated with different boundary conditions and with a range of complexities in terms of processes and interactions. This paper presents an overview of the different model components and explains how they were coupled and used to simulate historical climate changes over the past 150 years and different scenarios of future climate change. A single version of the IPSL-CM5 model (IPSL-CM5A-LR) was used to provide climate projections associated with different socio-economic scenarios, including the different Representative Concentration Pathways considered by CMIP5 and several scenarios from the Special Report on Emission Scenarios considered by CMIP3. Results suggest that the magnitude of global warming projections primarily depends on the socio-economic scenario considered, that there is potential for an aggressive mitigation policy to limit global warming to about two degrees, and that the behavior of some components of the climate system such as the Arctic sea ice and the Atlantic Meridional Overturning Circulation may change drastically by the end of the twenty-first century in the case of a no climate policy scenario. Although the magnitude of regional temperature and precipitation changes depends fairly linearly on the magnitude of the projected global warming (and thus on the scenario considered), the geographical pattern of these changes is strikingly similar for the different scenarios. The representation of atmospheric physical processes in the model is shown to strongly influence the simulated climate variability and both the magnitude and pattern of the projected climate changes.

Multi-Model Combination techniques for Hydrological Forecasting: Application to Distributed Model Intercomparison Project Results

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ajami, N K; Duan, Q; Gao, X

2005-04-11

This paper examines several multi-model combination techniques: the Simple Multi-model Average (SMA), the Multi-Model Super Ensemble (MMSE), Modified Multi-Model Super Ensemble (M3SE) and the Weighted Average Method (WAM). These model combination techniques were evaluated using the results from the Distributed Model Intercomparison Project (DMIP), an international project sponsored by the National Weather Service (NWS) Office of Hydrologic Development (OHD). All of the multi-model combination results were obtained using uncalibrated DMIP model outputs and were compared against the best uncalibrated as well as the best calibrated individual model results. The purpose of this study is to understand how different combination techniquesmore » affect the skill levels of the multi-model predictions. This study revealed that the multi-model predictions obtained from uncalibrated single model predictions are generally better than any single member model predictions, even the best calibrated single model predictions. Furthermore, more sophisticated multi-model combination techniques that incorporated bias correction steps work better than simple multi-model average predictions or multi-model predictions without bias correction.« less

Obs4MIPS: Satellite Observations for Model Evaluation

NASA Astrophysics Data System (ADS)

Ferraro, R.; Waliser, D. E.; Gleckler, P. J.

2017-12-01

This poster will review the current status of the obs4MIPs project, whose purpose is to provide a limited collection of well-established and documented datasets for comparison with Earth system models (https://www.earthsystemcog.org/projects/obs4mips/). These datasets have been reformatted to correspond with the CMIP5 model output requirements, and include technical documentation specifically targeted for their use in model output evaluation. The project holdings now exceed 120 datasets with observations that directly correspond to CMIP5 model output variables, with new additions in response to the CMIP6 experiments. With the growth in climate model output data volume, it is increasing more difficult to bring the model output and the observations together to do evaluations. The positioning of the obs4MIPs datasets within the Earth System Grid Federation (ESGF) allows for the use of currently available and planned online tools within the ESGF to perform analysis using model output and observational datasets without necessarily downloading everything to a local workstation. This past year, obs4MIPs has updated its submission guidelines to closely align with changes in the CMIP6 experiments, and is implementing additional indicators and ancillary data to allow users to more easily determine the efficacy of an obs4MIPs dataset for specific evaluation purposes. This poster will present the new guidelines and indicators, and update the list of current obs4MIPs holdings and their connection to the ESGF evaluation and analysis tools currently available, and being developed for the CMIP6 experiments.

Expansion of the Lyme Disease Vector Ixodes scapularis in Canada inferred from CMIP5 Climate Projections

NASA Astrophysics Data System (ADS)

McPherson, Michelle Yvonne; García-García, Almudena; José Cuesta-Valero, Francisco; Beltrami, Hugo; Hansen-Ketchum, Patti; MacDougall, Donna; Hume Ogden, Nicholas

2017-04-01

A number of studies have assessed possible climate change impacts on the Lyme disease vector, Ixodes scapularis. However, most have used surface air temperature from only one climate model simulation and/or one emission scenario, representing only one possible climate future. We quantified effects of different Representative Concentration Pathway (RCP) and climate model outputs on the projected future changes in the basic reproduction number (R0) of I. scapularis to explore uncertainties in future R0 estimates. We used surface air temperature generated by a complete set of General Circulation Models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) to hindcast historical and forecast future effects of climate change on the R0 of I. scapularis. As in previous studies, R0 of I. scapularis increased with a warming climate under future projected climate. Increases in the multi-model mean R0 values showed significant changes over time under all RCP scenarios, however; only the estimated R0 mean values between RCP6.0 and RCP8.5 showed statistically significant differences. Our results highlight the potential for climate change to have an effect on future Lyme disease risk in Canada even if the Paris Agreement's goal to keep global warming below 2°C is achieved, although mitigation reducing emissions from RCP8.5 levels to those of RCP6.0 or less would be expected to slow tick invasion after the 2030s. On-going planning is needed to inform and guide adaptation in light of the projected range of possible futures.

The role of historical forcings in simulating the observed Atlantic Multidecadal Oscillation

NASA Astrophysics Data System (ADS)

Goes, L. M.; Cane, M. A.; Bellomo, K.; Clement, A. C.

2016-12-01

The variation in basin-wide North Atlantic sea surface temperatures (SST), known as the Atlantic multidecadal oscillation (AMO), affects climate throughout the Northern Hemisphere and tropics, yet the forcing mechanisms are not fully understood. Here, we analyze the AMO in the Coupled Model Intercomparison Project phase 5 (CMIP5) Pre-industrial (PI) and Historical (HIST) simulations to determine the role of historical climate forcings in producing the observed 20th century shifts in the AMO (OBS, 1865-2005). We evaluate whether the agreement between models and observations is better with historical forcings or without forcing - i.e. due to processes internal to the climate system, such as the Atlantic Meridional Overturning Circulation (AMOC). To do this we draw 141-year samples from 38 CMIP5 PI runs and compare the correlation between the PI and HIST AMO to the observed AMO. We find that in the majority of models (24 out of 38), it is very unlikely (less than 10% chance) that the unforced simulations produce agreement with observations that are as high as the forced simulations. We also compare the amplitude of the simulated AMO and find that 87% of models produce multi-decadal variance in the AMO with historical forcings that is very likely higher than without forcing, but most models underestimate the variance of the observed AMO. This indicates that over the 20th century external rather than internal forcing was crucial in setting the pace, phase and amplitude of the AMO.

Assessing the hydrological response from an ensemble of CMIP5 climate projections in the transition zone of the Atlantic region (Bay of Biscay)

NASA Astrophysics Data System (ADS)

Meaurio, Maite; Zabaleta, Ane; Boithias, Laurie; Epelde, Ane Miren; Sauvage, Sabine; Sánchez-Pérez, Jose-Miguel; Srinivasan, Raghavan; Antiguedad, Iñaki

2017-05-01

The climate changes projected for the 21st century will have consequences on the hydrological response of catchments. These changes, and their consequences, are most uncertain in the transition zones. The study area, in the Bay of Biscay, is located in the transition zone of the European Atlantic region, where hydrological impact of climate change was scarcely studied. In order to address this scarcity, the hydrological impacts of climate change on river discharge were assessed. To do so, a hydrological modelling was carried out considering 16 climate scenarios that include 5 General Circulation Models (GCM) from the 5th report of the Coupled Model Intercomparison Project (CMIP5), 2 statistical downscaling methods and 2 Representative Concentration Pathways. Projections for future discharge (2011-2100) were divided into three 30-year horizons (2030s, 2060s and 2090s) and a comparison was made between these time horizons and the baseline (1961-2000). The results show that the downscaling method used resulted in a higher source of uncertainty than GCM itself. In addition, the uncertainties inherent to the methods used at all the levels do not affect the results equally along the year. In spite of those uncertainties, general trends for the 2090s predict seasonal discharge decreases by around -17% in autumn, -16% in spring, -11% in winter and -7% in summer. These results are in line with those predicted for the Atlantic region (France and the Iberian Peninsula). Trends for extreme flows were also analysed: the most significant show an increase in the duration (days) of low flows. From an environmental point of view, and considering the need to meet the objectives established by the Water Framework Directive (WFD), this will be a major challenge for the future planning on water management.

Assessing Future Hydrological Changes in the Tana River Basin, Kenya

NASA Astrophysics Data System (ADS)

Jenkins, Rhosanna

2017-04-01

Changes in precipitation will be one of the most significant factors in determining the overall impact of global climate change but are also one of the most uncertain and difficult to project. The reliability of global climate models (GCMs) for predicting changes in rainfall is particularly concerning for East Africa. This research focuses on Kenya's Tana River Basin and aims to project the impacts of climate change upon the hydrology in order to inform national climate change adaptation plans. The Tana basin has been identified as crucial for Kenya's development, with increased irrigated agriculture and additional dams planned. The area is also important for biodiversity and contains already-threatened ecosystems and endemic species. Kenya is already a water-scarce country and demand for water is expected to increase in the future as the country develops. Therefore, examining changes to precipitation with climate change is vital. The WaterWorld Policy Support System (http://www.policysupport.org/waterworld), a physically-based hydrological model, has been used to determine annual and monthly changes in hydrology. WaterWorld utilises the WorldClim (Hijmans et al., 2005) climate projections for the latest generation of climate models from the Coupled Model Intercomparison Project, phase 5 (CMIP5) to characterise the temperature and precipitation changes. In order to better understand the high uncertainties in projections of climate change, the full range of latest emissions scenarios (the representative concentration pathways or RCPs) were used to force the WaterWorld model. The WorldClim baseline values were evaluated by comparing them to observations and were found to correctly represent the annual cycle of precipitation. In addition, the CRU TS3.22 data (Harris et al., 2014) have also been examined and provide a valuable comparison to the WorldClim dataset. These simulations encompass a broad range of climate projections, but show a general trend towards increased precipitation in the Tana River Basin. Overall, the multi-model ensemble mean for all RCPs suggests that there will be increases in precipitation by the 2050s, with the annual basin-average rainfall increasing between 112% (RCP2.6) and 149% (RCP8.5). As the precipitation in East Africa is highly seasonal, examining monthly changes is also important. Drying is projected in some months, whereas wetter conditions are projected in others. Average precipitation changes do not vary greatly between the RCPs, but there are large discrepancies between individual GCMs, with some models even disagreeing on the sign of precipitation change (i.e. positive or negative relative to the baseline). Between-model differences in the magnitude of precipitation change are also substantial. This large variation in anomalies of projected precipitation demonstrates the uncertainty in CMIP5 GCM outputs for the area and has important implications for water resources management and policy. Robust management decisions will need to be made in the face of this considerable uncertainty. Policies that allow for adaptability and a wide range of possible future outcomes are paramount.

Underestimated interannual variability of East Asian summer rainfall under climate change

NASA Astrophysics Data System (ADS)

Ren, Yongjian; Song, Lianchun; Xiao, Ying; Du, Liangmin

2018-02-01

This study evaluates the performance of climate models in simulating the climatological mean and interannual variability of East Asian summer rainfall (EASR) using Coupled Model Intercomparison Project Phase 5 (CMIP5). Compared to the observation, the interannual variability of EASR during 1979-2005 is underestimated by the CMIP5 with a range of 0.86 16.08%. Based on bias correction of CMIP5 simulations with historical data, the reliability of future projections will be enhanced. The corrected EASR under representative concentration pathways (RCPs) 4.5 and 8.5 increases by 5.6 and 7.5% during 2081-2100 relative to the baseline of 1986-2005, respectively. After correction, the areas with both negative and positive anomalies decrease, which are mainly located in the South China Sea and central China, and southern China and west of the Philippines, separately. In comparison to the baseline, the interannual variability of EASR increases by 20.8% under RCP4.5 but 26.2% under RCP8.5 in 2006-2100, which is underestimated by 10.7 and 11.1% under both RCPs in the original CMIP5 simulation. Compared with the mean precipitation, the interannual variability of EASR is notably larger under global warming. Thus, the probabilities of floods and droughts may increase in the future.

Modeling Impacts of Climate and Land Use Change on Ecosystem Processes to Quantify Exposure to Climate Change in Two Landscape Conservation Cooperatives

NASA Astrophysics Data System (ADS)

Quackenbush, A.

2015-12-01

Urban land cover and associated impervious surface area is expected to increase by as much as 50% over the next few decades across substantial portions of the United States. In combination with urban expansion, increases in temperature and changes in precipitation are expected to impact ecosystems through changes in productivity, disturbance and hydrological properties. In this study, we use the NASA Terrestrial Observation and Prediction System Biogeochemical Cycle (TOPS-BGC) model to explore the combined impacts of urbanization and climate change on hydrologic dynamics (snowmelt, runoff, and evapotranspiration) and vegetation carbon uptake (gross productivity). The model is driven using land cover predictions from the Spatially Explicit Regional Growth Model (SERGoM) to quantify projected changes in impervious surface area, and climate projections from the 30 arc-second NASA Earth Exchange Downscaled Climate Projection (NEX-DCP30) dataset derived from the CMIP5 climate scenarios. We present the modeling approach and an analysis of the ecosystem impacts projected to occur in the US, with an emphasis on protected areas in the Great Northern and Appalachian Landscape Conservation Cooperatives (LCC). Under the ensemble average of the CMIP5 models and land cover change scenarios for both representative concentration pathways (RCPs) 4.5 and 8.5, both LCCs are predicted to experience increases in maximum and minimum temperatures as well as annual average precipitation. In the Great Northern LCC, this is projected to lead to increased annual runoff, especially under RCP 8.5. Earlier melt of the winter snow pack and increased evapotranspiration, however, reduces summer streamflow and soil water content, leading to a net reduction in vegetation productivity across much of the Great Northern LCC, with stronger trends occurring under RCP 8.5. Increased runoff is also projected to occur in the Appalachian LCC under both RCP 4.5 and 8.5. However, under RCP 4.5, the model predicts that the warmer wetter conditions will lead to increases in vegetation productivity across much of the Appalachian LCC, while under RCP 8.5, the effects of increased precipitation are not enough to keep up with increases in evapotranspiration, leading to projected reductions in vegetation productivity for this LCC by the end of this century.

Changes in extremely hot days under stabilized 1.5 and 2.0 °C global warming scenarios as simulated by the HAPPI multi-model ensemble

NASA Astrophysics Data System (ADS)

Wehner, Michael; Stone, Dáithí; Mitchell, Dann; Shiogama, Hideo; Fischer, Erich; Graff, Lise S.; Kharin, Viatcheslav V.; Lierhammer, Ludwig; Sanderson, Benjamin; Krishnan, Harinarayan

2018-03-01

The half a degree additional warming, prognosis and projected impacts (HAPPI) experimental protocol provides a multi-model database to compare the effects of stabilizing anthropogenic global warming of 1.5 °C over preindustrial levels to 2.0 °C over these levels. The HAPPI experiment is based upon large ensembles of global atmospheric models forced by sea surface temperature and sea ice concentrations plausible for these stabilization levels. This paper examines changes in extremes of high temperatures averaged over three consecutive days. Changes in this measure of extreme temperature are also compared to changes in hot season temperatures. We find that over land this measure of extreme high temperature increases from about 0.5 to 1.5 °C over present-day values in the 1.5 °C stabilization scenario, depending on location and model. We further find an additional 0.25 to 1.0 °C increase in extreme high temperatures over land in the 2.0 °C stabilization scenario. Results from the HAPPI models are consistent with similar results from the one available fully coupled climate model. However, a complicating factor in interpreting extreme temperature changes across the HAPPI models is their diversity of aerosol forcing changes.

Assessment of spatiotemporal variations in the fluvial wash-load component in the 21st century with regard to GCM climate change scenarios.

PubMed

Mouri, Goro

2015-11-15

For stream water, in which a relationship exists between wash-load concentration and discharge, an estimate of fine-sediment delivery may be obtained from a traditional fluvial wash-load rating curve. Here, we demonstrate that the remaining wash-load material load can be estimated from a traditional empirical principle on a nationwide scale. The traditional technique was applied to stream water for the whole of Japan. Four typical GCMs were selected from the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble to provide the driving fields for the following regional climate models to assess the wash-load component based on rating curves: the Model for Interdisciplinary Research on Climate (MIROC), the Meteorological Research Institute Atmospheric General Circulation Model (MRI-GCM), the Hadley Centre Global Environment Model (HadGEM) and the Geophysical Fluid Dynamics Laboratory (GFDL) climate model. The simulations consisted of an ensemble, including multiple physics configurations and different Representative Concentration Pathways (RCP2.6, RCP4.5 and RCP8.5), which was used to produce monthly datasets for the whole country of Japan. The impacts of future climate changes on fluvial wash load in Japanese stream water were based on the balance of changes in hydrological factors. The annual and seasonal variations of the fluvial wash load were assessed from the result of the ensemble analysis in consideration of the Greenhouse Gas (GHG) emission scenarios. The determined results for the amount of wash load increase range from approximately 20 to 110% in the 2040s, especially along part of the Pacific Ocean and the Sea of Japan regions. In the 2090s, the amount of wash load is projected to increase by more than 50% over the whole of Japan. The assessment indicates that seasonal variation is particularly important because the rainy and typhoon seasons, which include extreme events, are the dominant seasons. Because fluvial wash-load-component turbidity appears to vary exponentially, this phenomenon has an impact on the management of social capital, such as drinking water services. Prediction of the impacts of future climate change on fluvial wash-load sediment is crucial for effective environmental planning and the management of social capital to adapt to the next century. We demonstrate that simulations comprise an ensemble of factors, including multiple physical configurations, associated with the wash-load component for the whole of Japan. Copyright © 2015 Elsevier B.V. All rights reserved.

Uncertainty in projected point precipitation extremes for hydrological impact analysis of climate change

NASA Astrophysics Data System (ADS)

Van Uytven, Els; Willems, Patrick

2017-04-01

Current trends in the hydro-meteorological variables indicate the potential impact of climate change on hydrological extremes. Therefore, they trigger an increased importance climate adaptation strategies in water management. The impact of climate change on hydro-meteorological and hydrological extremes is, however, highly uncertain. This is due to uncertainties introduced by the climate models, the internal variability inherent to the climate system, the greenhouse gas scenarios and the statistical downscaling methods. In view of the need to define sustainable climate adaptation strategies, there is a need to assess these uncertainties. This is commonly done by means of ensemble approaches. Because more and more climate models and statistical downscaling methods become available, there is a need to facilitate the climate impact and uncertainty analysis. A Climate Perturbation Tool has been developed for that purpose, which combines a set of statistical downscaling methods including weather typing, weather generator, transfer function and advanced perturbation based approaches. By use of an interactive interface, climate impact modelers can apply these statistical downscaling methods in a semi-automatic way to an ensemble of climate model runs. The tool is applicable to any region, but has been demonstrated so far to cases in Belgium, Suriname, Vietnam and Bangladesh. Time series representing future local-scale precipitation, temperature and potential evapotranspiration (PET) conditions were obtained, starting from time series of historical observations. Uncertainties on the future meteorological conditions are represented in two different ways: through an ensemble of time series, and a reduced set of synthetic scenarios. The both aim to span the full uncertainty range as assessed from the ensemble of climate model runs and downscaling methods. For Belgium, for instance, use was made of 100-year time series of 10-minutes precipitation observations and daily temperature and PET observations at Uccle and a large ensemble of 160 global climate model runs (CMIP5). They cover all four representative concentration pathway based greenhouse gas scenarios. While evaluating the downscaled meteorological series, particular attention was given to the performance of extreme value metrics (e.g. for precipitation, by means of intensity-duration-frequency statistics). Moreover, the total uncertainty was decomposed in the fractional uncertainties for each of the uncertainty sources considered. Research assessing the additional uncertainty due to parameter and structural uncertainties of the hydrological impact model is ongoing.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tribbia, Joseph

NCAR brought the latest version of the Community Earth System Model (version 1, CESM1) into the mix of models in the NMME effort. This new version uses our newest atmospheric model CAM5 and produces a coupled climate and ENSO that are generally as good or better than those of the Community Climate System Model version 4 (CCSM4). Compared to CCSM4, the new coupled model has a superior climate response with respect to low clouds in both the subtropical stratus regimes and the Arctic. However, CESM1 has been run to date using a prognostic aerosol model that more than doubles itsmore » computational cost. We are currently evaluating a version of the new model using prescribed aerosols and expect it will be ready for integrations in summer 2012. Because of this NCAR has not been able to complete the hindcast integrations using the NCAR loosely-coupled ensemble Kalman filter assimilation method nor has it contributed to the current (Stage I) NMME operational utilization. The expectation is that this model will be included in the NMME in late 2012 or early 2013. The initialization method will utilize the Ensemble Kalman Filter Assimilation methods developed at NCAR using the Data Assimilation Research Testbed (DART) in conjunction with Jeff Anderson’s team in CISL. This methodology has been used in our decadal prediction contributions to CMIP5. During the course of this project, NCAR has setup and performed all the needed hindcast and forecast simulations and provide the requested fields to our collaborators. In addition, NCAR researchers have participated fully in research themes (i) and (ii). Specifically, i) we have begun to evaluate and optimize our system in hindcast mode, focusing on the optimal number of ensemble members, methodologies to recalibrate individual dynamical models, and accessing our forecasts across multiple time scales, i.e., beyond two weeks, and ii) we have begun investigation of the role of different ocean initial conditions in seasonal forecasts. The completion of the calibration hindcasts for Seasonal to Interannual (SI) predictions and the maintenance of the data archive associated with the NCAR portion of this effort has been the responsibility of the Project Scientist I (Alicia Karspeck) that was partially supported on this project.« less

Evaluating groundwater recharge variations under climate change in an endorheic basin of the Andean plateau

NASA Astrophysics Data System (ADS)

Blin, N.; Hausner, M. B.; Suarez, F. I.

2017-12-01

In arid and semi-arid regions, where surface water and precipitations are scarce, groundwater is the main source of drinking water that sustains human and natural ecosystems. Therefore, it is very important to consider the potential impacts of climate change that threaten the availability of this resource. The purpose of this study is to investigate the variations caused by climate change on the recharge of the regional groundwater aquifer at the Huasco salt flat, located in the Chilean Andean plateau. The Huasco salt flat basin has ecosystems sustained by wetlands that depend on the groundwater levels of this aquifer. Due to this reason, the Chilean government has declared this zone as protected. Hence, the assurance of the future availability of the groundwater resource becomes extremely important. The sustainable management of this resource requires reasonable estimates of recharge and evapotranspiration, which are highly dependent on the characteristics and processes occurring in the vadose zone, i.e., topography, soil type and land use, and their temporal and spatial variations are significant in arid regions. With this aim, a three-dimensional groundwater model, implemented in SWAT-MODFLOW, was developed to couple the saturated system with the vadose zone. The model was calibrated and validated using historic data. General circulation models (GCMs) were used as scenarios inputs of recharge to the groundwater model. Future simulations were run by applying an offset to the historic air temperatures and to the precipitation. These offsets were determined using a delta hybrid approach based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) multi-model ensemble archive. The obtained results were downscaled to the 0.125º latitude x 0.125º longitude grid cell containing the basin of the Huasco salt flat. The hybrid approach considered the 10th, 50th and 90th percentiles of the projected temperature and precipitation output as three scenarios of climate change used to evaluate recharge variation.

Importance of vegetation distribution for future carbon balance

NASA Astrophysics Data System (ADS)

Ahlström, A.; Xia, J.; Arneth, A.; Luo, Y.; Smith, B.

2015-12-01

Projections of future terrestrial carbon uptake vary greatly between simulations. Net primary production (NPP), wild fires, vegetation dynamics (including biome shifts) and soil decomposition constitute the main processes governing the response of the terrestrial carbon cycle in a changing climate. While primary production and soil respiration are relatively well studied and implemented in all global ecosystem models used to project the future land sink of CO2, vegetation dynamics are less studied and not always represented in global models. Here we used a detailed second generation dynamic global vegetation model with advanced representation of vegetation growth and mortality and the associated turnover and proven skill in predicting vegetation distribution and succession. We apply an emulator that describes the carbon flows and pools exactly as in simulations with the full model. The emulator simulates ecosystem dynamics in response to 13 different climate or Earth system model simulations from the CMIP5 ensemble under RCP8.5 radiative forcing at year 2085. We exchanged carbon cycle processes between these 13 simulations and investigate the changes predicted by the emulator. This method allowed us to partition the entire ensemble carbon uptake uncertainty into individual processes. We found that NPP, vegetation dynamics (including biome shifts, wild fires and mortality) and soil decomposition rates explained 49%, 17% and 33% respectively of uncertainties in modeled global C-uptake. Uncertainty due to vegetation dynamics was further partitioned into stand-clearing disturbances (16%), wild fires (0%), stand dynamics (7%), reproduction (10%) and biome shifts (67%) globally. We conclude that while NPP and soil decomposition rates jointly account for 83% of future climate induced C-uptake uncertainties, vegetation turnover and structure, dominated by shifts in vegetation distribution, represent a significant fraction globally and regionally (tropical forests: 40%), strongly motivating their representation and analysis in future C-cycle studies.

Transient Calibration of a Variably-Saturated Groundwater Flow Model By Iterative Ensemble Smoothering: Synthetic Case and Application to the Flow Induced During Shaft Excavation and Operation of the Bure Underground Research Laboratory

NASA Astrophysics Data System (ADS)

Lam, D. T.; Kerrou, J.; Benabderrahmane, H.; Perrochet, P.

2017-12-01

The calibration of groundwater flow models in transient state can be motivated by the expected improved characterization of the aquifer hydraulic properties, especially when supported by a rich transient dataset. In the prospect of setting up a calibration strategy for a variably-saturated transient groundwater flow model of the area around the ANDRA's Bure Underground Research Laboratory, we wish to take advantage of the long hydraulic head and flowrate time series collected near and at the access shafts in order to help inform the model hydraulic parameters. A promising inverse approach for such high-dimensional nonlinear model, and which applicability has been illustrated more extensively in other scientific fields, could be an iterative ensemble smoother algorithm initially developed for a reservoir engineering problem. Furthermore, the ensemble-based stochastic framework will allow to address to some extent the uncertainty of the calibration for a subsequent analysis of a flow process dependent prediction. By assimilating the available data in one single step, this method iteratively updates each member of an initial ensemble of stochastic realizations of parameters until the minimization of an objective function. However, as it is well known for ensemble-based Kalman methods, this correction computed from approximations of covariance matrices is most efficient when the ensemble realizations are multi-Gaussian. As shown by the comparison of the updated ensemble mean obtained for our simplified synthetic model of 2D vertical flow by using either multi-Gaussian or multipoint simulations of parameters, the ensemble smoother fails to preserve the initial connectivity of the facies and the parameter bimodal distribution. Given the geological structures depicted by the multi-layered geological model built for the real case, our goal is to find how to still best leverage the performance of the ensemble smoother while using an initial ensemble of conditional multi-Gaussian simulations or multipoint simulations as conceptually consistent as possible. Performance of the algorithm including additional steps to help mitigate the effects of non-Gaussian patterns, such as Gaussian anamorphosis, or resampling of facies from the training image using updated local probability constraints will be assessed.

Evaluating the Relevance, Reliability, and Applicability of CMIP5 Climate Projections for Water Resources and Environmental Planning

NASA Astrophysics Data System (ADS)

Brekke, L. D.; Scott, J.; Ferguson, I. M.; Arnold, J.; Raff, D. A.; Webb, R. S.

2012-12-01

Water managers need to understand the applicability of climate projection information available for decision-support at the scale of their applications. Applicability depends on information reliability and relevance. This need to understand applicability stems from expectations that entities rationalize adaptation investments or decisions to delay investment. It is also occurring at a time when new global climate projections are being released through the World Climate Research Programme Coupled Model Intercomparison Project phase 5 (CMIP5), which introduces new information opportunities and interpretation challenges. This project involves an interagency collaboration to evaluate the applicability of CMIP5 projections for use in water and environmental resources planning. The overarching goal is to develop and demonstrate a framework that involves dual evaluations of relevance and reliability informing an ultimate discussion and judgment of applicability, which is expected to vary with decision-making context. The framework is being developed and demonstrated within the context of reservoir systems management in California's Sacramento and San Joaquin River basins. The relevance evaluation focuses on identifying the climate variables and statistical measures relevant to long-term management questions, which may depend on satisfying multiple objectives. Past studies' results are being considered in this evaluation, along with new results from system sensitivity analyses conducted through this effort. The reliability evaluation focuses on the CMIP5 climate models' ability to simulate past conditions relative to observed references. The evaluation is being conducted across the global domain using a large menu of climate variables and statistical measures, leveraging lessons learned from similar evaluations of CMIP3 climate models. The global focus addresses a broader project goal of producing a web resource that can serve reliability information to applicability discussions around the world, with evaluation results being served through a web-portal similar to that developed by NOAA/CIRES to serve CMIP3 information on future climate extremes (http://www.esrl.noaa.gov/psd/ipcc/extremes/). The framework concludes with an applicability discussion informed by relevance and reliability results. The goal is to observe the discussion process and identify features, choice points, and challenges that might be summarized and shared with other resource management groups facing applicability questions. This presentation will discuss the project framework and preliminary results. In addition to considering CMIP5 21st century projection information, the framework is being developed to support evaluation of CMIP5 decadal predictability experiment simulations and reconcile those simulations with 21st century projections. The presentation will also discuss implications of considering the applicability of bias-corrected and downscaled information within this framework.

a Process-Based Drought Early Warning Indicator for Supporting State Drought Mitigation Decision

NASA Astrophysics Data System (ADS)

Fu, R.; Fernando, D. N.; Pu, B.

2014-12-01

Drought prone states such as Texas requires creditable and actionable drought early warning ranging from seasonal to multi-decadal scales. Such information cannot be simply extracted from the available climate prediction and projections because of their large uncertainties at regional scales and unclear connections to the needs of the decision makers. In particular, current dynamic seasonal predictions and climate projections, such as those produced by the NOAA national multi-models ensemble experiment (NMME) and the IPCC AR5 (CMIP5) models, are much more reliable for winter and spring than for the summer season for the US Southern Plains. They also show little connection between the droughts in winter/spring and those in summer, in contrast to the observed dry memory from spring to summer over that region. To mitigate the weakness of dynamic prediction/projections, we have identified three key processes behind the spring-to-summer dry memory through observational studies. Based on these key processes and related fields, we have developed a multivariate principle component statistical model to provide a probabilistic summer drought early warning indicator, using the observed or predicted climate conditions in winter and spring on seasonal scale and climate projection for the mid-21stcentury. The summer drought early warning indicator is constructed in a similar way to the NOAA probabilistic predictions that are familiar to water resource managers. The indicator skill is assessed using the standard NOAA climate prediction assessment tools, i.e., the two alternative forced choice (2AFC) and the Receiver Operating Characteristic (ROC). Comparison with long-term observations suggest that this summer drought early warning indicator is able to capture nearly all the strong summer droughts and outperform the dynamic prediction in this regard over the US Southern Plains. This early warning indicator has been used by the state water agency in May 2014 in briefing the state drought preparedness council and will be provided to stake holders through the website of the Texas state water planning agency. We will also present the results of our ongoing work on using NASA satellite based soil moisture and vegetation stress measurements to further improve the reliability of the summer drought early warning indicator.

Investigating the climate and carbon cycle impacts of CMIP6 Land Use and Land Cover Change in the Community Earth System Model (CESM2)

NASA Astrophysics Data System (ADS)

Lawrence, P.; Lawrence, D. M.; O'Neill, B. C.; Hurtt, G. C.

2017-12-01

For the next round of CMIP6 climate simulations there are new historical and SSP - RCP land use and land cover change (LULCC) data sets that have been compiled through the Land Use Model Intercomparison Project (LUMIP). The new time series data include new functionality following lessons learned through CMIP5 project and include new developments in the Community Land Model (CLM5) that will be used in all the CESM2 simulations of CMIP6. These changes include representing explicit crop modeling and better forest representation through the extended to 12 land units of the Global Land Model (GLM). To include this new information in CESM2 and CLM5 simulations new transient land surface data sets have been generated for the historical period 1850 - 2015 and for preliminary SSP - RCP paired future scenarios. The new data sets use updated MODIS Land Cover, Vegetation Continuous Fields, Leaf Area Index and Albedo to describe Primary and Secondary, Forested and Non Forested land units, as well as Rangelands and Pasture. Current day crop distributions are taken from the MIRCA2000 crop data set as done with the CLM 4.5 crop model and used to guide historical and future crop distributions. Preliminary "land only" simulations with CLM5 have been performed for the historical period and for the SSP1-RCP2.6 and SSP3-RCP7 land use and land cover change time series data. Equivalent no land use and land cover change simulations have been run for these periods under the same meteorological forcing data. The "land only" simulations use GSWP3 historical atmospheric forcing data from 1850 to 2010 and then time increasing RCP 8.5 atmospheric CO2 and climate anomalies on top of the current day GSWP3 atmospheric forcing data from 2011 to 2100. The offline simulations provide a basis to evaluate the surface climate, carbon cycle and crop production impacts of changing land use and land cover for each of these periods. To further evaluate the impacts of the new CLM5 model and the CMIP6 land use data, these results are compared to the equivalent investigations performed in CMIP5 with the CLM4/CESM1 model. We find the role of land use and land cover change in a changing climate is strongly dependent on both of these.

Selecting a climate model subset to optimise key ensemble properties

NASA Astrophysics Data System (ADS)

Herger, Nadja; Abramowitz, Gab; Knutti, Reto; Angélil, Oliver; Lehmann, Karsten; Sanderson, Benjamin M.

2018-02-01

End users studying impacts and risks caused by human-induced climate change are often presented with large multi-model ensembles of climate projections whose composition and size are arbitrarily determined. An efficient and versatile method that finds a subset which maintains certain key properties from the full ensemble is needed, but very little work has been done in this area. Therefore, users typically make their own somewhat subjective subset choices and commonly use the equally weighted model mean as a best estimate. However, different climate model simulations cannot necessarily be regarded as independent estimates due to the presence of duplicated code and shared development history. Here, we present an efficient and flexible tool that makes better use of the ensemble as a whole by finding a subset with improved mean performance compared to the multi-model mean while at the same time maintaining the spread and addressing the problem of model interdependence. Out-of-sample skill and reliability are demonstrated using model-as-truth experiments. This approach is illustrated with one set of optimisation criteria but we also highlight the flexibility of cost functions, depending on the focus of different users. The technique is useful for a range of applications that, for example, minimise present-day bias to obtain an accurate ensemble mean, reduce dependence in ensemble spread, maximise future spread, ensure good performance of individual models in an ensemble, reduce the ensemble size while maintaining important ensemble characteristics, or optimise several of these at the same time. As in any calibration exercise, the final ensemble is sensitive to the metric, observational product, and pre-processing steps used.

Seasonality of Precipitation over Himalayan Watersheds in CORDEX South Asia and their Driving CMIP5 Experiments

DOE Office of Scientific and Technical Information (OSTI.GOV)

ul Hasson, Shabeh

Since the Coupled Model Intercomparison Project Phase 5 (CMIP5) experiments exhibit limited skill in reproducing the statistical properties of prevailing precipitation regimes over the major Himalayan watersheds (Indus, Ganges, Brahmaputra and Mekong), this study evaluates the anticipated added skill of their dynamically refined simulations performed under the framework of Coordinated Regional Climate Downscaling Experiments for South Asia (CX-SA). For this, the fidelity of eight CX-SA experiments against their six driving CMIP5 experiments is assessed for the historical period (1971–2005) in terms of time-dependent statistical properties (onset/retreat timings and rapid fractional accumulation—RFA) of the dominant summer monsoonal precipitation regime (MPR). Further,more » a self-defining seasonality index (SI), which is a product of precipitation and the distance of its actual distribution relative to its uniform distribution (relative entropy—RE), has been computed for MPR, westerly precipitation regime (WPR) and annual precipitation. The time evolution of precipitation, RE and SI has also been analyzed. Results suggest that CX-SA experiments simulate even higher wet biases than their driving CMIP5 experiments over all study basins, mainly due to higher wet biases simulated over the Himalayas and Tibetan Plateau. Most of the CX-SA experiments suggest unrealistic timings of the monsoon onset that are far earlier than their driving CMIP5 experiments for all basins. Generally, CX-SA experiments feature higher underestimation of RFA slope, RE and SI, distancing their driving CMIP5 experiments farther from observations. Interestingly, regardless of the diverse skill of CMIP5 experiments, their fine scale CX-SA experiments exhibit quite a similar skill when downscaled by the same regional climate model (RCM), indicating RCM’s ability to considerably alter the driving datasets. Lastly, these findings emphasize on improving the fidelity of simulated precipitation regimes over the Himalayan watersheds by exploiting the potential of RCMs in term of microphysics, resolutions and convective closures, and preferably, on resolving the crucial fine scale processes further down to their representative (meso-to-local) scales.« less

Seasonality of Precipitation over Himalayan Watersheds in CORDEX South Asia and their Driving CMIP5 Experiments

DOE PAGES

ul Hasson, Shabeh

2016-10-02

Since the Coupled Model Intercomparison Project Phase 5 (CMIP5) experiments exhibit limited skill in reproducing the statistical properties of prevailing precipitation regimes over the major Himalayan watersheds (Indus, Ganges, Brahmaputra and Mekong), this study evaluates the anticipated added skill of their dynamically refined simulations performed under the framework of Coordinated Regional Climate Downscaling Experiments for South Asia (CX-SA). For this, the fidelity of eight CX-SA experiments against their six driving CMIP5 experiments is assessed for the historical period (1971–2005) in terms of time-dependent statistical properties (onset/retreat timings and rapid fractional accumulation—RFA) of the dominant summer monsoonal precipitation regime (MPR). Further,more » a self-defining seasonality index (SI), which is a product of precipitation and the distance of its actual distribution relative to its uniform distribution (relative entropy—RE), has been computed for MPR, westerly precipitation regime (WPR) and annual precipitation. The time evolution of precipitation, RE and SI has also been analyzed. Results suggest that CX-SA experiments simulate even higher wet biases than their driving CMIP5 experiments over all study basins, mainly due to higher wet biases simulated over the Himalayas and Tibetan Plateau. Most of the CX-SA experiments suggest unrealistic timings of the monsoon onset that are far earlier than their driving CMIP5 experiments for all basins. Generally, CX-SA experiments feature higher underestimation of RFA slope, RE and SI, distancing their driving CMIP5 experiments farther from observations. Interestingly, regardless of the diverse skill of CMIP5 experiments, their fine scale CX-SA experiments exhibit quite a similar skill when downscaled by the same regional climate model (RCM), indicating RCM’s ability to considerably alter the driving datasets. Lastly, these findings emphasize on improving the fidelity of simulated precipitation regimes over the Himalayan watersheds by exploiting the potential of RCMs in term of microphysics, resolutions and convective closures, and preferably, on resolving the crucial fine scale processes further down to their representative (meso-to-local) scales.« less

Greenhouse gas scenario sensitivity and uncertainties in precipitation projections for central Belgium

NASA Astrophysics Data System (ADS)

Van Uytven, E.; Willems, P.

2018-03-01

Climate change impact assessment on meteorological variables involves large uncertainties as a result of incomplete knowledge on the future greenhouse gas concentrations and climate model physics, next to the inherent internal variability of the climate system. Given that the alteration in greenhouse gas concentrations is the driver for the change, one expects the impacts to be highly dependent on the considered greenhouse gas scenario (GHS). In this study, we denote this behavior as GHS sensitivity. Due to the climate model related uncertainties, this sensitivity is, at local scale, not always that strong as expected. This paper aims to study the GHS sensitivity and its contributing role to climate scenarios for a case study in Belgium. An ensemble of 160 CMIP5 climate model runs is considered and climate change signals are studied for precipitation accumulation, daily precipitation intensities and wet day frequencies. This was done for the different seasons of the year and the scenario periods 2011-2040, 2031-2060, 2051-2081 and 2071-2100. By means of variance decomposition, the total variance in the climate change signals was separated in the contribution of the differences in GHSs and the other model-related uncertainty sources. These contributions were found dependent on the variable and season. Following the time of emergence concept, the GHS uncertainty contribution is found dependent on the time horizon and increases over time. For the most distinct time horizon (2071-2100), the climate model uncertainty accounts for the largest uncertainty contribution. The GHS differences explain up to 18% of the total variance in the climate change signals. The results point further at the importance of the climate model ensemble design, specifically the ensemble size and the combination of climate models, whereupon climate scenarios are based. The numerical noise, introduced at scales smaller than the skillful scale, e.g. at local scale, was not considered in this study.

Observed fingerprint of a weakening Atlantic Ocean overturning circulation

NASA Astrophysics Data System (ADS)

Rahmstorf, S.; Caesar, L.; Feulner, G.; Robinson, A.; Saba, V. S.

2017-12-01

The overturning circulation of the Atlantic Ocean (AMOC) has a major impact on climate, yet its evolution over the past hundred years or so is poorly known for lack of direct measurements. We use a high-resolution global climate model to derive a characteristic spatial and seasonal fingerprint of AMOC changes and compare this to the observed linear temperature trend since 1870. Both the model and observations show a remarkably similar pattern of a cooling in the subpolar gyre region (most pronounced for the November to May season) and a warming in the Gulf Stream region which in their combination can only be explained by a reduction in the AMOC. We explain the mechanisms that link the pattern to an AMOC slowdown, and use an ensemble of CMIP5 simulations to calibrate the observed decline. This suggests a weakening of the AMOC by 3 Sv ( 16%) since the mid-20th Century. Its recent evolution is consistent with direct measurements in the RAPID project and reaches record low values in recent years.

The progressive aridification of southwest North America: The what, the why, the when and the how (Invited)

NASA Astrophysics Data System (ADS)

Seager, R.; Liu, H.; Henderson, N.; Kushnir, Y.; Ting, M.; Cook, B.; Nakamura, J.

2013-12-01

The latest generation of global coupled climate models from the Coupled Model Intercomparison Project Five (CMIP5), much anticipated after the prior CMIP3 models projected the southwest of North America to transition in the near term to a more arid climate, turned out to be a case of deja-vu all over again. While suggesting that northern California might get more midwinter precipitation, overall the CMIP5 models, like their CMIP3 precursors, project that the interior southwest of the U.S., most of Mexico, Texas and the southern Plains, will progressively transition to a more arid climate with reduced precipitation and increased potential evapotranspiration driving a reduction in soil moisture and streamflow. An about 10% reduction of Colorado River flow for the 2021-2040 period relative to the last half of the 20th Century appears a good ball park number to motivate adaptation efforts. Here we will present new detailed analyses of Reanalysis and CMIP5 model moisture budgets to determine the causes of the projected aridification. The role of moisture convergences by the mean and transient flows will be addressed as well as the dynamical causes of the shifts in atmospheric circulation that contribute to drying or locally offset it. The hydroclimate history of the West for the past few decades will also be examined for evidence that model projected aridification is in progress or cannot currently be detected amidst the large natural variability of hydroclimate in the region. But it will be cautioned that waiting for statistical significance might be as fruitful as Waiting for Godot while, during the wait, the southwest will already have transitioned into a troublingly drier climate with serious impacts on people, societies, ecosystems and agriculture.

Assessment of NASA GISS CMIP5 ModelE simulated clouds and TOA radiation budgets using satellite observations over the southern mid-latitudes

NASA Astrophysics Data System (ADS)

Stanfield, Ryan Evan

Past, current, and future climates have been simulated by the National Aeronautics and Space Administration (NASA) Goddard Institute for Space Studies (GISS) ModelE Global Circulation Model (GCM) and summarized by the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC, AR4). New simulations from the updated CMIP5 version of the NASA GISS ModelE GCM were recently released to the public community during the summer of 2011 and will be included in the upcoming IPCC AR5 ensemble of simulations. Due to the recent nature of these simulations, they have not yet been extensively validated against observations. To assess the NASA GISS-E2-R GCM, model simulated clouds and cloud properties are compared to observational cloud properties derived from the Clouds and Earth's Radiant Energy System (CERES) project using MODerate Resolution Imaging Spectroradiometer (MODIS) data for the period of March 2000 through December 2005. Over the 6-year period, the global average modeled cloud fractions are within 1% of observations. However, further study however shows large regional biases between the GCM simulations and CERES-MODIS observations. The southern mid-latitudes (SML) were chosen as a focus region due to model errors across multiple GCMs within the recent phase 5 of the Coupled Model Intercomparison Project (CMIP5). Over the SML, the GISS GCM undersimulates total cloud fraction over 20%, but oversimulates total water path by 2 g m-2. Simulated vertical cloud distributions over the SML when compared to both CERES-MODIS and CloudSat/CALIPSO observations show a drastic undersimulation of low level clouds by the GISS GCM, but higher fractions of thicker clouds. To assess the impact of GISS simulated clouds on the TOA radiation budgets, the modeled TOA radiation budgets are compared to CERES EBAF observations. Because modeled low-level cloud fraction is much lower than observed over the SML, modeled reflected shortwave (SW) flux at the TOA is 13 W m -2 lower and outgoing longwave radiation (OLR) is 3 W m-2 higher than observations. Finally, cloud radiative effects (CRE) are calculated and compared with observations to fully assess the impact of clouds on the TOA radiation budgets. The difference in clear-sky reflected SW flux between model and observation is only +4 W m-2 while the SW CRE difference is up to 17 W m-2, indicating that most of the bias in SW CRE results from the all-sky bias between the model and observation. A sizeable negative bias of 10 W m-2 in simulated clear-sky OLR has been found due to a dry bias in calculating observed clear-sky OLR and lack of upper-level water vapor at the 100-mb level in the model. The dry bias impacts CRE LW, with the model undersimulating by 13 W m-2. The CRE NET difference is only 5 W m-2 due to the cancellation of SW and LW CRE biases.

How do the methodological choices of your climate change study affect your results? A hydrologic case study across the Pacific Northwest

NASA Astrophysics Data System (ADS)

Chegwidden, O.; Nijssen, B.; Rupp, D. E.; Kao, S. C.; Clark, M. P.

2017-12-01

We describe results from a large hydrologic climate change dataset developed across the Pacific Northwestern United States and discuss how the analysis of those results can be seen as a framework for other large hydrologic ensemble investigations. This investigation will better inform future modeling efforts and large ensemble analyses across domains within and beyond the Pacific Northwest. Using outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we provide projections of hydrologic change for the domain through the end of the 21st century. The dataset is based upon permutations of four methodological choices: (1) ten global climate models (2) two representative concentration pathways (3) three meteorological downscaling methods and (4) four unique hydrologic model set-ups (three of which entail the same hydrologic model using independently calibrated parameter sets). All simulations were conducted across the Columbia River Basin and Pacific coastal drainages at a 1/16th ( 6 km) resolution and at a daily timestep. In total, the 172 distinct simulations offer an updated, comprehensive view of climate change projections through the end of the 21st century. The results consist of routed streamflow at 400 sites throughout the domain as well as distributed spatial fields of relevant hydrologic variables like snow water equivalent and soil moisture. In this presentation, we discuss the level of agreement with previous hydrologic projections for the study area and how these projections differ with specific methodological choices. By controlling for some methodological choices we can show how each choice affects key climatic change metrics. We discuss how the spread in results varies across hydroclimatic regimes. We will use this large dataset as a case study for distilling a wide range of hydroclimatological projections into useful climate change assessments.

Using synchronization in multi-model ensembles to improve prediction

NASA Astrophysics Data System (ADS)

Hiemstra, P.; Selten, F.

2012-04-01

In recent decades, many climate models have been developed to understand and predict the behavior of the Earth's climate system. Although these models are all based on the same basic physical principles, they still show different behavior. This is for example caused by the choice of how to parametrize sub-grid scale processes. One method to combine these imperfect models, is to run a multi-model ensemble. The models are given identical initial conditions and are integrated forward in time. A multi-model estimate can for example be a weighted mean of the ensemble members. We propose to go a step further, and try to obtain synchronization between the imperfect models by connecting the multi-model ensemble, and exchanging information. The combined multi-model ensemble is also known as a supermodel. The supermodel has learned from observations how to optimally exchange information between the ensemble members. In this study we focused on the density and formulation of the onnections within the supermodel. The main question was whether we could obtain syn-chronization between two climate models when connecting only a subset of their state spaces. Limiting the connected subspace has two advantages: 1) it limits the transfer of data (bytes) between the ensemble, which can be a limiting factor in large scale climate models, and 2) learning the optimal connection strategy from observations is easier. To answer the research question, we connected two identical quasi-geostrohic (QG) atmospheric models to each other, where the model have different initial conditions. The QG model is a qualitatively realistic simulation of the winter flow on the Northern hemisphere, has three layers and uses a spectral imple-mentation. We connected the models in the original spherical harmonical state space, and in linear combinations of these spherical harmonics, i.e. Empirical Orthogonal Functions (EOFs). We show that when connecting through spherical harmonics, we only need to connect 28% of the state variables to obtain synchronization. In addition, when connecting through EOFs, we can reduce this percentage even more to 12%. This reduction is caused by the more efficient description of the model state variables when using EOFs. The connected state variables center around the medium scale structures in the model. Small and large scale structures need not be connected in order to obtain synchronization. This could be related to the baroclinic instabilities in the QG model which are located in the medium scale structures of the model. The baroclinic instabilities are the main source of divergence between the two connected models.