How to reduce long-term drift in present-day and deep-time simulations?

NASA Astrophysics Data System (ADS)

Brunetti, Maura; Vérard, Christian

2018-06-01

Climate models are often affected by long-term drift that is revealed by the evolution of global variables such as the ocean temperature or the surface air temperature. This spurious trend reduces the fidelity to initial conditions and has a great influence on the equilibrium climate after long simulation times. Useful insight on the nature of the climate drift can be obtained using two global metrics, i.e. the energy imbalance at the top of the atmosphere and at the ocean surface. The former is an indicator of the limitations within a given climate model, at the level of both numerical implementation and physical parameterisations, while the latter is an indicator of the goodness of the tuning procedure. Using the MIT general circulation model, we construct different configurations with various degree of complexity (i.e. different parameterisations for the bulk cloud albedo, inclusion or not of friction heating, different bathymetry configurations) to which we apply the same tuning procedure in order to obtain control runs for fixed external forcing where the climate drift is minimised. We find that the interplay between tuning procedure and different configurations of the same climate model provides crucial information on the stability of the control runs and on the goodness of a given parameterisation. This approach is particularly relevant for constructing good-quality control runs of the geological past where huge uncertainties are found in both initial and boundary conditions. We will focus on robust results that can be generally applied to other climate models.

High-resolution dynamical downscaling of the future Alpine climate

NASA Astrophysics Data System (ADS)

Bozhinova, Denica; José Gómez-Navarro, Juan; Raible, Christoph

2017-04-01

The Alpine region and Switzerland is a challenging area for simulating and analysing Global Climate Model (GCM) results. This is mostly due to the combination of a very complex topography and the still rather coarse horizontal resolution of current GCMs, in which not all of the many-scale processes that drive the local weather and climate can be resolved. In our study, the Weather Research and Forecasting (WRF) model is used to dynamically downscale a GCM simulation to a resolution as high as 2 km x 2 km. WRF is driven by initial and boundary conditions produced with the Community Earth System Model (CESM) for the recent past (control run) and until 2100 using the RCP8.5 climate scenario (future run). The control run downscaled with WRF covers the period 1976-2005, while the future run investigates a 20-year-slice simulated for the 2080-2099. We compare the control WRF-CESM simulations to an observational product provided by MeteoSwiss and an additional WRF simulation driven by the ERA-Interim reanalysis, to estimate the bias that is introduced by the extra modelling step of our framework. Several bias-correction methods are evaluated, including a quantile mapping technique, to ameliorate the bias in the control WRF-CESM simulation. In the next step of our study these corrections are applied to our future WRF-CESM run. The resulting downscaled and bias-corrected data is analysed for the properties of precipitation and wind speed in the future climate. Our special interest focuses on the absolute quantities simulated for these meteorological variables as these are used to identify extreme events, such as wind storms and situations that can lead to floods.

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.

Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios

Treesearch

John B Kim; Erwan Monier; Brent Sohngen; G Stephen Pitts; Ray Drapek; James McFarland; Sara Ohrel; Jefferson Cole

2016-01-01

We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a...

The Parana paradox: can a model explain the decadal impacts of climate variability and land-cover change?

NASA Astrophysics Data System (ADS)

Lee, E.; Moorcroft, P. R.; Livino, A.; Briscoe, J.

2013-12-01

Since the 1970s, despite a decrease in rainfall, flow in the Parana river has increased. This paradox is explored using the Ecosystem Demography (ED) model. If there were no change in land cover, the modeled runoff decreased from the 1970s to the 2000s by 11.8% (with 1970 land cover) or 18.8% (with 2008 land cover). When the model is run holding climate constant, the decadal average of the modeled runoff increased by 24.4% (with the 1970s climate) or by 33.6% (with 2000s climate). When the model is run allowing both the actual climate and land-cover changes, the model gives an increase in the decadal average of runoff by 8.5%. This agrees well with 10.5% increase in the actual stream flow as measured at Itaipu. There are three main conclusions from this work. First, the ED model is able to explain a major, paradoxical, reality in the Parana basin. Second, it is necessary to take into account both climate and land use changes when exploring past or future changes in river flows. Third, the ED model, now coupled with a regional climate model (i.e., EDBRAMS), is a sound basis for exploring likely changes in river flows in major South American rivers.

Running climate model on a commercial cloud computing environment: A case study using Community Earth System Model (CESM) on Amazon AWS

NASA Astrophysics Data System (ADS)

Chen, Xiuhong; Huang, Xianglei; Jiao, Chaoyi; Flanner, Mark G.; Raeker, Todd; Palen, Brock

2017-01-01

The suites of numerical models used for simulating climate of our planet are usually run on dedicated high-performance computing (HPC) resources. This study investigates an alternative to the usual approach, i.e. carrying out climate model simulations on commercially available cloud computing environment. We test the performance and reliability of running the CESM (Community Earth System Model), a flagship climate model in the United States developed by the National Center for Atmospheric Research (NCAR), on Amazon Web Service (AWS) EC2, the cloud computing environment by Amazon.com, Inc. StarCluster is used to create virtual computing cluster on the AWS EC2 for the CESM simulations. The wall-clock time for one year of CESM simulation on the AWS EC2 virtual cluster is comparable to the time spent for the same simulation on a local dedicated high-performance computing cluster with InfiniBand connections. The CESM simulation can be efficiently scaled with the number of CPU cores on the AWS EC2 virtual cluster environment up to 64 cores. For the standard configuration of the CESM at a spatial resolution of 1.9° latitude by 2.5° longitude, increasing the number of cores from 16 to 64 reduces the wall-clock running time by more than 50% and the scaling is nearly linear. Beyond 64 cores, the communication latency starts to outweigh the benefit of distributed computing and the parallel speedup becomes nearly unchanged.

Sensitivity of WRF Regional Climate Simulations to Choice of Land Use Dataset

EPA Science Inventory

The goal of this study is to assess the sensitivity of regional climate simulations run with the Weather Research and Forecasting (WRF) model to the choice of datasets representing land use and land cover (LULC). Within a regional climate modeling application, an accurate repres...

OBERON: OBliquity and Energy balance Run on N-body systems

NASA Astrophysics Data System (ADS)

Forgan, Duncan H.

2016-08-01

OBERON (OBliquity and Energy balance Run on N-body systems) models the climate of Earthlike planets under the effects of an arbitrary number and arrangement of other bodies, such as stars, planets and moons. The code, written in C++, simultaneously computes N body motions using a 4th order Hermite integrator, simulates climates using a 1D latitudinal energy balance model, and evolves the orbital spin of bodies using the equations of Laskar (1986a,b).

Different types of drifts in two seasonal forecast systems and their dependence on ENSO

NASA Astrophysics Data System (ADS)

Hermanson, L.; Ren, H.-L.; Vellinga, M.; Dunstone, N. D.; Hyder, P.; Ineson, S.; Scaife, A. A.; Smith, D. M.; Thompson, V.; Tian, B.; Williams, K. D.

2017-11-01

Seasonal forecasts using coupled ocean-atmosphere climate models are increasingly employed to provide regional climate predictions. For the quality of forecasts to improve, regional biases in climate models must be diagnosed and reduced. The evolution of biases as initialized forecasts drift away from the observations is poorly understood, making it difficult to diagnose the causes of climate model biases. This study uses two seasonal forecast systems to examine drifts in sea surface temperature (SST) and precipitation, and compares them to the long-term bias in the free-running version of each model. Drifts are considered from daily to multi-annual time scales. We define three types of drift according to their relation with the long-term bias in the free-running model: asymptoting, overshooting and inverse drift. We find that precipitation almost always has an asymptoting drift. SST drifts on the other hand, vary between forecasting systems, where one often overshoots and the other often has an inverse drift. We find that some drifts evolve too slowly to have an impact on seasonal forecasts, even though they are important for climate projections. The bias found over the first few days can be very different from that in the free-running model, so although daily weather predictions can sometimes provide useful information on the causes of climate biases, this is not always the case. We also find that the magnitude of equatorial SST drifts, both in the Pacific and other ocean basins, depends on the El Niño Southern Oscillation (ENSO) phase. Averaging over all hindcast years can therefore hide the details of ENSO state dependent drifts and obscure the underlying physical causes. Our results highlight the need to consider biases across a range of timescales in order to understand their causes and develop improved climate models.

Secular trends and climate drift in coupled ocean-atmosphere general circulation models

NASA Astrophysics Data System (ADS)

Covey, Curt; Gleckler, Peter J.; Phillips, Thomas J.; Bader, David C.

2006-02-01

Coupled ocean-atmosphere general circulation models (coupled GCMs) with interactive sea ice are the primary tool for investigating possible future global warming and numerous other issues in climate science. A long-standing problem with such models is that when different components of the physical climate system are linked together, the simulated climate can drift away from observation unless constrained by ad hoc adjustments to interface fluxes. However, 11 modern coupled GCMs, including three that do not employ flux adjustments, behave much better in this respect than the older generation of models. Surface temperature trends in control run simulations (with external climate forcing such as solar brightness and atmospheric carbon dioxide held constant) are small compared with observed trends, which include 20th century climate change due to both anthropogenic and natural factors. Sea ice changes in the models are dominated by interannual variations. Deep ocean temperature and salinity trends are small enough for model control runs to extend over 1000 simulated years or more, but trends in some regions, most notably the Arctic, differ substantially among the models and may be problematic. Methods used to initialize coupled GCMs can mitigate climate drift but cannot eliminate it. Lengthy "spin-ups" of models, made possible by increasing computer power, are one reason for the improvements this paper documents.

Water availability in +2°C and +4°C worlds.

PubMed

Fung, Fai; Lopez, Ana; New, Mark

2011-01-13

While the parties to the UNFCCC agreed in the December 2009 Copenhagen Accord that a 2°C global warming over pre-industrial levels should be avoided, current commitments on greenhouse gas emissions reductions from these same parties will lead to a 50 : 50 chance of warming greater than 3.5°C. Here, we evaluate the differences in impacts and adaptation issues for water resources in worlds corresponding to the policy objective (+2°C) and possible reality (+4°C). We simulate the differences in impacts on surface run-off and water resource availability using a global hydrological model driven by ensembles of climate models with global temperature increases of 2°C and 4°C. We combine these with UN-based population growth scenarios to explore the relative importance of population change and climate change for water availability. We find that the projected changes in global surface run-off from the ensemble show an increase in spatial coherence and magnitude for a +4°C world compared with a +2°C one. In a +2°C world, population growth in most large river basins tends to override climate change as a driver of water stress, while in a +4°C world, climate change becomes more dominant, even compensating for population effects where climate change increases run-off. However, in some basins where climate change has positive effects, the seasonality of surface run-off becomes increasingly amplified in a +4°C climate.

Modeling the impacts of climate change and technical progress on the wheat yield in inland China: An autoregressive distributed lag approach.

PubMed

Zhai, Shiyan; Song, Genxin; Qin, Yaochen; Ye, Xinyue; Lee, Jay

2017-01-01

This study aims to evaluate the impacts of climate change and technical progress on the wheat yield per unit area from 1970 to 2014 in Henan, the largest agricultural province in China, using an autoregressive distributed lag approach. The bounded F-test for cointegration among the model variables yielded evidence of a long-run relationship among climate change, technical progress, and the wheat yield per unit area. In the long run, agricultural machinery and fertilizer use both had significantly positive impacts on the per unit area wheat yield. A 1% increase in the aggregate quantity of fertilizer use increased the wheat yield by 0.19%. Additionally, a 1% increase in machine use increased the wheat yield by 0.21%. In contrast, precipitation during the wheat growth period (from emergence to maturity, consisting of the period from last October to June) led to a decrease in the wheat yield per unit area. In the short run, the coefficient of the aggregate quantity of fertilizer used was negative. Land size had a significantly positive impact on the per unit area wheat yield in the short run. There was no significant short-run or long-run impact of temperature on the wheat yield per unit area in Henan Province. The results of our analysis suggest that climate change had a weak impact on the wheat yield, while technical progress played an important role in increasing the wheat yield per unit area. The results of this study have implications for national and local agriculture policies under climate change. To design well-targeted agriculture adaptation policies for the future and to reduce the adverse effects of climate change on the wheat yield, climate change and technical progress factors should be considered simultaneously. In addition, adaptive measures associated with technical progress should be given more attention.

Modeling the impacts of climate change and technical progress on the wheat yield in inland China: An autoregressive distributed lag approach

PubMed Central

Qin, Yaochen; Lee, Jay

2017-01-01

This study aims to evaluate the impacts of climate change and technical progress on the wheat yield per unit area from 1970 to 2014 in Henan, the largest agricultural province in China, using an autoregressive distributed lag approach. The bounded F-test for cointegration among the model variables yielded evidence of a long-run relationship among climate change, technical progress, and the wheat yield per unit area. In the long run, agricultural machinery and fertilizer use both had significantly positive impacts on the per unit area wheat yield. A 1% increase in the aggregate quantity of fertilizer use increased the wheat yield by 0.19%. Additionally, a 1% increase in machine use increased the wheat yield by 0.21%. In contrast, precipitation during the wheat growth period (from emergence to maturity, consisting of the period from last October to June) led to a decrease in the wheat yield per unit area. In the short run, the coefficient of the aggregate quantity of fertilizer used was negative. Land size had a significantly positive impact on the per unit area wheat yield in the short run. There was no significant short-run or long-run impact of temperature on the wheat yield per unit area in Henan Province. The results of our analysis suggest that climate change had a weak impact on the wheat yield, while technical progress played an important role in increasing the wheat yield per unit area. The results of this study have implications for national and local agriculture policies under climate change. To design well-targeted agriculture adaptation policies for the future and to reduce the adverse effects of climate change on the wheat yield, climate change and technical progress factors should be considered simultaneously. In addition, adaptive measures associated with technical progress should be given more attention. PMID:28950027

Coupling climate and hydrological models to evaluate the impact of climate change on run of the river hydropower schemes from UK study sites

NASA Astrophysics Data System (ADS)

Pasten-Zapata, Ernesto; Jones, Julie; Moggridge, Helen

2015-04-01

As climate change is expected to generate variations on the Earth's precipitation and temperature, the water cycle will also experience changes. Consequently, water users will have to be prepared for possible changes in future water availability. The main objective of this research is to evaluate the impacts of climate change on river regimes and the implications to the operation and feasibility of run of the river hydropower schemes by analyzing four UK study sites. Run of the river schemes are selected for analysis due to their higher dependence to the available river flow volumes when compared to storage hydropower schemes that can rely on previously accumulated water volumes (linked to poster in session HS5.3). Global Climate Models (GCMs) represent the main tool to assess future climate change. In this research, Regional Climate Models (RCMs), which dynamically downscale GCM outputs providing higher resolutions, are used as starting point to evaluate climate change within the study catchments. RCM daily temperature and precipitation will be downscaled to an appropriate scale for impact studies and bias corrected using different statistical methods: linear scaling, local intensity scaling, power transformation, variance scaling and delta change correction. The downscaled variables will then be coupled to hydrological models that have been previously calibrated and validated against observed daily river flow data. The coupled hydrological and climate models will then be used to simulate historic river flows that are compared to daily observed values in order to evaluate the model accuracy. As this research will employ several different RCMs (from the EURO-CORDEX simulations), downscaling and bias correction methodologies, greenhouse emission scenarios and hydrological models, the uncertainty of each element will be estimated. According to their uncertainty magnitude, a prediction of the best downscaling approach (or approaches) is expected to be obtained. The current progress of the project will be presented along with the steps to be followed in the future.

A new climate modeling framework for convection-resolving simulation at continental scale

NASA Astrophysics Data System (ADS)

Charpilloz, Christophe; di Girolamo, Salvatore; Arteaga, Andrea; Fuhrer, Oliver; Hoefler, Torsten; Schulthess, Thomas; Schär, Christoph

2017-04-01

Major uncertainties remain in our understanding of the processes that govern the water cycle in a changing climate and their representation in weather and climate models. Of particular concern are heavy precipitation events of convective origin (thunderstorms and rain showers). The aim of the crCLIM project [1] is to propose a new climate modeling framework that alleviates the I/O-bottleneck in large-scale, convection-resolving climate simulations and thus to enable new analysis techniques for climate scientists. Due to the large computational costs, convection-resolving simulations are currently restricted to small computational domains or very short time scales, unless the largest available supercomputers system such as hybrid CPU-GPU architectures are used [3]. Hence, the COSMO model has been adapted to run on these architectures for research and production purposes [2]. However, the amount of generated data also increases and storing this data becomes infeasible making the analysis of simulations results impractical. To circumvent this problem and enable high-resolution models in climate we propose a data-virtualization layer (DVL) that re-runs simulations on demand and transparently manages the data for the analysis, that means we trade off computational effort (time) for storage (space). This approach also requires a bit-reproducible version of the COSMO model that produces identical results on different architectures (CPUs and GPUs) [4] that will be coupled with a performance model in order enable optimal re-runs depending on requirements of the re-run and available resources. In this contribution, we discuss the strategy to develop the DVL, a first performance model, the challenge of bit-reproducibility and the first results of the crCLIM project. [1] http://www.c2sm.ethz.ch/research/crCLIM.html [2] O. Fuhrer, C. Osuna, X. Lapillonne, T. Gysi, M. Bianco, and T. Schulthess. "Towards gpu-accelerated operational weather forecasting." In The GPU Technology Conference, GTC. 2013. [3] D. Leutwyler, O. Fuhrer, X. Lapillonne, D. Lüthi, and C. Schär. "Towards European-scale convection-resolving climate simulations with GPUs: a study with COSMO 4.19." Geoscientific Model Development 9, no. 9 (2016): 3393. [4] A. Arteaga, O. Fuhrer, and T. Hoefler. "Designing bit-reproducible portable high-performance applications." In Parallel and Distributed Processing Symposium, 2014 IEEE 28th International, pp. 1235-1244. IEEE, 2014.

Climate Change Impacts on US Agriculture and the Benefits of Greenhouse Gas Mitigation

NASA Astrophysics Data System (ADS)

Monier, E.; Sue Wing, I.; Stern, A.

2014-12-01

As contributors to the US EPA's Climate Impacts and Risk Assessment (CIRA) project, we present empirically-based projections of climate change impacts on the yields of five major US crops. Our analysis uses a 15-member ensemble of climate simulations using the MIT Integrated Global System Model (IGSM) linked to the NCAR Community Atmosphere Model (CAM), forced by 3 emissions scenarios (a "business as usual" reference scenario and two stabilization scenarios at 4.5W/m2 and 3.7 W/m2 by 2100), quantify the agricultural impacts avoided due to greenhouse gas emission reductions. Our innovation is the coupling of climate model outputs with empirical estimates of the long-run relationship between crop yields and temperature, precipitation and soil moisture derived from the co-variation between yields and weather across US counties over the last 50 years. Our identifying assumption is that since farmers' planting, management and harvesting decisions are based on land quality and expectations of weather, yields and meteorological variables share a long-run equilibrium relationship. In any given year, weather shocks cause yields to diverge from their expected long-run values, prompting farmers to revise their long-run expectations. We specify a dynamic panel error correction model (ECM) that statistically distinguishes these two processes. The ECM is estimated for maize, wheat, soybeans, sorghum and cotton using longitudinal data on production and harvested area for ~1,100 counties from 1948-2010, in conjunction with spatial fields of 3-hourly temperature, precipitation and soil moisture from the Global Land Data Assimilation System (GLDAS) forcing and output files, binned into annual counts of exposure over the growing season and mapped to county centroids. For scenarios of future warming the identical method was used to calculate counties' current (1986-2010) and future (2036-65 and 2086-2110) distributions of simulated 3-hourly growing season temperature, precipitation and soil moisture. Finally, we combine these variables with the fitted long-run response to obtain county-level yields under current average climate and projected future climate under our three warming scenarios. We close our presentation with a discussion of the implications for mitigation and adaptation decisions.

Using Web 2.0 Techniques To Bring Global Climate Modeling To More Users

NASA Astrophysics Data System (ADS)

Chandler, M. A.; Sohl, L. E.; Tortorici, S.

2012-12-01

The Educational Global Climate Model has been used for many years in undergraduate courses and professional development settings to teach the fundamentals of global climate modeling and climate change simulation to students and teachers. While course participants have reported a high level of satisfaction in these courses and overwhelmingly claim that EdGCM projects are worth the effort, there is often a high level of frustration during the initial learning stages. Many of the problems stem from issues related to installation of the software suite and to the length of time it can take to run initial experiments. Two or more days of continuous run time may be required before enough data has been gathered to begin analyses. Asking users to download existing simulation data has not been a solution because the GCM data sets are several gigabytes in size, requiring substantial bandwidth and stable dedicated internet connections. As a means of getting around these problems we have been developing a Web 2.0 utility called EzGCM (Easy G-G-M) which emphasizes that participants learn the steps involved in climate modeling research: constructing a hypothesis, designing an experiment, running a computer model and assessing when an experiment has finished (reached equilibrium), using scientific visualization to support analysis, and finally communicating the results through social networking methods. We use classic climate experiments that can be "rediscovered" through exercises with EzGCM and are attempting to make this Web 2.0 tool an entry point into climate modeling for teachers with little time to cover the subject, users with limited computer skills, and for those who want an introduction to the process before tackling more complex projects with EdGCM.

Hydroclimatic projections for the Murray-Darling Basin based on an ensemble derived from Intergovernmental Panel on Climate Change AR4 climate models

NASA Astrophysics Data System (ADS)

Sun, Fubao; Roderick, Michael L.; Lim, Wee Ho; Farquhar, Graham D.

2011-12-01

We assess hydroclimatic projections for the Murray-Darling Basin (MDB) using an ensemble of 39 Intergovernmental Panel on Climate Change AR4 climate model runs based on the A1B emissions scenario. The raw model output for precipitation, P, was adjusted using a quantile-based bias correction approach. We found that the projected change, ΔP, between two 30 year periods (2070-2099 less 1970-1999) was little affected by bias correction. The range for ΔP among models was large (˜±150 mm yr-1) with all-model run and all-model ensemble averages (4.9 and -8.1 mm yr-1) near zero, against a background climatological P of ˜500 mm yr-1. We found that the time series of actually observed annual P over the MDB was indistinguishable from that generated by a purely random process. Importantly, nearly all the model runs showed similar behavior. We used these facts to develop a new approach to understanding variability in projections of ΔP. By plotting ΔP versus the variance of the time series, we could easily identify model runs with projections for ΔP that were beyond the bounds expected from purely random variations. For the MDB, we anticipate that a purely random process could lead to differences of ±57 mm yr-1 (95% confidence) between successive 30 year periods. This is equivalent to ±11% of the climatological P and translates into variations in runoff of around ±29%. This sets a baseline for gauging modeled and/or observed changes.

76 FR 33923 - Endangered and Threatened Wildlife and Plants; 12-Month Finding on a Petition To List Abronia...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-09

... Sipes 2006, p. 76). The leaf blades are succulent (fleshy) and oval or diamond-shaped with smooth edges... of climate model runs performed at modeling centers worldwide using 22 global climate models (Ray et...

Intercomparison of model response and internal variability across climate model ensembles

NASA Astrophysics Data System (ADS)

Kumar, Devashish; Ganguly, Auroop R.

2017-10-01

Characterization of climate uncertainty at regional scales over near-term planning horizons (0-30 years) is crucial for climate adaptation. Climate internal variability (CIV) dominates climate uncertainty over decadal prediction horizons at stakeholders' scales (regional to local). In the literature, CIV has been characterized indirectly using projections of climate change from multi-model ensembles (MME) instead of directly using projections from multiple initial condition ensembles (MICE), primarily because adequate number of initial condition (IC) runs were not available for any climate model. Nevertheless, the recent availability of significant number of IC runs from one climate model allows for the first time to characterize CIV directly from climate model projections and perform a sensitivity analysis to study the dominance of CIV compared to model response variability (MRV). Here, we measure relative agreement (a dimensionless number with values ranging between 0 and 1, inclusive; a high value indicates less variability and vice versa) among MME and MICE and find that CIV is lower than MRV for all projection time horizons and spatial resolutions for precipitation and temperature. However, CIV exhibits greater dominance over MRV for seasonal and annual mean precipitation at higher latitudes where signals of climate change are expected to emerge sooner. Furthermore, precipitation exhibits large uncertainties and a rapid decline in relative agreement from global to continental, regional, or local scales for MICE compared to MME. The fractional contribution of uncertainty due to CIV is invariant for precipitation and decreases for temperature as lead time progresses towards the end of the century.

A commentary on the Atlantic meridional overturning circulation stability in climate models

NASA Astrophysics Data System (ADS)

Gent, Peter R.

2018-02-01

The stability of the Atlantic meridional overturning circulation (AMOC) in ocean models depends quite strongly on the model formulation, especially the vertical mixing, and whether it is coupled to an atmosphere model. A hysteresis loop in AMOC strength with respect to freshwater forcing has been found in several intermediate complexity climate models and in one fully coupled climate model that has very coarse resolution. Over 40% of modern climate models are in a bistable AMOC state according to the very frequently used simple stability criterion which is based solely on the sign of the AMOC freshwater transport across 33° S. In a recent freshwater hosing experiment in a climate model with an eddy-permitting ocean component, the change in the gyre freshwater transport across 33° S is larger than the AMOC freshwater transport change. This casts very strong doubt on the usefulness of this simple AMOC stability criterion. If a climate model uses large surface flux adjustments, then these adjustments can interfere with the atmosphere-ocean feedbacks, and strongly change the AMOC stability properties. AMOC can be shut off for many hundreds of years in modern fully coupled climate models if the hosing or carbon dioxide forcing is strong enough. However, in one climate model the AMOC recovers after between 1000 and 1400 years. Recent 1% increasing carbon dioxide runs and RCP8.5 future scenario runs have shown that the AMOC reduction is smaller using an eddy-resolving ocean component than in the comparable standard 1° ocean climate models.

A protocol for the development of Mediterranean climate services based on the experiences of the CLIM-RUN case studies

NASA Astrophysics Data System (ADS)

Goodess, Clare; Ruti, Paolo; Rousset, Nathalie

2014-05-01

During the closing stages of the CLIM-RUN EU FP7 project on Climate Local Information in the Mediterranean region Responding to User Needs, the real-world experiences encountered by the case-study teams are being assessed and synthesised to identify examples of good practice and, in particular, to produce the CLIM-RUN protocol for the development of Mediterranean climate services. The specific case studies have focused on renewable energy (Morocco, Spain, Croatia, Cyprus), tourism (Savoie, Tunisia, Croatia, Cyprus) and wild fires (Greece) as well as one cross-cutting case study (Veneto region). They have been implemented following a common programme of local workshops, questionnaires and interviews, with Climate Expert Team and Stakeholder Expert Team members collaborating to identify and translate user needs and subsequently develop climate products and information. Feedback from stakeholders has been essential in assessing and refining these products. The protocol covers the following issues: the overall process and methodological key stages; identification and selection of stakeholders; communication with stakeholders; identification of user needs; translation of needs; producing products; assessing and refining products; methodologies for evaluating the economic value of climate services; and beyond CLIM-RUN - the lessons learnt. Particular emphasis is given to stakeholder analysis in the context of the participatory, bottom-up approach promoted by CLIM-RUN and to the iterative approach taken in the development of climate products. Recommendations are also made for an envisioned three-tier business model for the development of climate services involving climate, intermediary and stakeholder tiers.

An improved ENSO simulation by representing chlorophyll-induced climate feedback in the NCAR Community Earth System Model.

PubMed

Kang, Xianbiao; Zhang, Rong-Hua; Gao, Chuan; Zhu, Jieshun

2017-12-07

The El Niño-Southern oscillation (ENSO) simulated in the Community Earth System Model of the National Center for Atmospheric Research (NCAR CESM) is much stronger than in reality. Here, satellite data are used to derive a statistical relationship between interannual variations in oceanic chlorophyll (CHL) and sea surface temperature (SST), which is then incorporated into the CESM to represent oceanic chlorophyll -induced climate feedback in the tropical Pacific. Numerical runs with and without the feedback (referred to as feedback and non-feedback runs) are performed and compared with each other. The ENSO amplitude simulated in the feedback run is more accurate than that in the non-feedback run; quantitatively, the Niño3 SST index is reduced by 35% when the feedback is included. The underlying processes are analyzed and the results show that interannual CHL anomalies exert a systematic modulating effect on the solar radiation penetrating into the subsurface layers, which induces differential heating in the upper ocean that affects vertical mixing and thus SST. The statistical modeling approach proposed in this work offers an effective and economical way for improving climate simulations.

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.

Precipitation forecast verification over Brazilian watersheds on present and future climate

NASA Astrophysics Data System (ADS)

Xavier, L.; Bruyere, C. L.; Rotunno, O.

2016-12-01

Evaluating the quality of precipitation forecast is an essential step for hydrological studies, among other applications, which is particularly relevant when taking into account climate change and the consequent likely modification of precipitation patterns. In this study we analyzed daily precipitation forecasts given by the global model CESM and the regional model WRF on present and future climate. For present runs, CESM data have been considered from 1980 to 2005, and WRF data from 1990 to 2000. CESM future runs were available for 3 RCP scenarios (4.5, 6.0 and 8.5), over 2005-2100 period; for WRF, future runs spanned 4 different 11-year periods (2020-2030, 2030-2040, 2050-2060 and 2080-2090). WRF simulations had been driven by bias-corrected forcings, and had been done on present climate for a 24 members ensemble created by varying the adopted parameterization schemes. On WRF future climate simulations, data from 3 members out of the original ensemble were available. Precipitation data have been spatially averaged over some large Brazilian watersheds (Amazon and subbasins, Tocantins, Sao Francisco, 4 of Parana`s subbasins) and have been evaluated for present climate against a gauge gridded dataset and ERA Interim data both spanning the 1980-2013 period. The evaluation was focused on the analysis of precipitation forecasts probabilities distribution. Taking into account daily and monthly mean precipitation aggregated on 3-month periods (DJF,MAM,JJA,SON), we adopted some skill measures, amongst them, the Perkins Skill Score (PSS). From the results we verified that on present climate WRF ensemble mean led to clearly better results when compared with CESM data for Amazon, Tocantins and Sao Francisco, but model was not as skillful to the other basins, which could be also been observed for future climate. PSS results from future runs showed that few changes would be observed over the different periods for the considered basins.

Global climate changes as forecast by Goddard Institute for Space Studies three-dimensional model

NASA Technical Reports Server (NTRS)

Hansen, J.; Fung, I.; Lacis, A.; Rind, D.; Lebedeff, S.; Ruedy, R.; Russell, G.

1988-01-01

The global climate effects of time-dependent atmospheric trace gas and aerosol variations are simulated by NASA-Goddard's three-dimensional climate model II, which possesses 8 x 10-deg horizontal resolution, for the cases of a 100-year control run and three different atmospheric composition scenarios in which trace gas growth is respectively a continuation of current exponential trends, a reduced linear growth, and a rapid curtailment of emissions due to which net climate forcing no longer increases after the year 2000. The experiments begin in 1958, run to the present, and encompass measured or estimated changes in CO2, CH4, N2O, chlorofluorocarbons, and stratospheric aerosols. It is shown that the greenhouse warming effect may be clearly identifiable in the 1990s.

Climate responses to anthropogenic emissions of short-lived climate pollutants

NASA Astrophysics Data System (ADS)

Baker, L. H.; Collins, W. J.; Olivié, D. J. L.; Cherian, R.; Hodnebrog, Ø.; Myhre, G.; Quaas, J.; Samset, B. H.

2015-02-01

Policies to control air quality focus on mitigating emissions of aerosols and their precursors, and other short-lived climate pollutants (SLCPs). On a local scale, these policies will have beneficial impacts on health and crop yields, by reducing particulate matter (PM) and surface ozone concentrations; however, the climate impacts of reducing emissions of SLCPs are less straightforward to predict. In this paper we consider a set of idealised, extreme mitigation strategies, in which the total anthropogenic emissions of individual SLCP emissions species are removed. This provides an upper bound on the potential climate impacts of such air quality strategies. We focus on evaluating the climate responses to changes in anthropogenic emissions of aerosol precursor species: black carbon (BC), organic carbon (OC) and sulphur dioxide (SO2). We perform climate integrations with four fully coupled atmosphere-ocean global climate models (AOGCMs), and examine the effects on global and regional climate of removing the total land-based anthropogenic emissions of each of the three aerosol precursor species. We find that the SO2 emissions reductions lead to the strongest response, with all three models showing an increase in surface temperature focussed in the northern hemisphere high latitudes, and a corresponding increase in global mean precipitation and run-off. Changes in precipitation and run-off patterns are driven mostly by a northward shift in the ITCZ, consistent with the hemispherically asymmetric warming pattern driven by the emissions changes. The BC and OC emissions reductions give a much weaker forcing signal, and there is some disagreement between models in the sign of the climate responses to these perturbations. These differences between models are due largely to natural variability in sea-ice extent, circulation patterns and cloud changes. This large natural variability component to the signal when the ocean circulation and sea-ice are free-running means that the BC and OC mitigation measures do not necessarily lead to a discernible climate response.

CMIP5 models' shortwave cloud radiative response and climate sensitivity linked to the climatological Hadley cell extent

NASA Astrophysics Data System (ADS)

Lipat, Bernard R.; Tselioudis, George; Grise, Kevin M.; Polvani, Lorenzo M.

2017-06-01

This study analyzes Coupled Model Intercomparison Project phase 5 (CMIP5) model output to examine the covariability of interannual Southern Hemisphere Hadley cell (HC) edge latitude shifts and shortwave cloud radiative effect (SWCRE). In control climate runs, during years when the HC edge is anomalously poleward, most models substantially reduce the shortwave radiation reflected by clouds in the lower midlatitude region (LML; ˜28°S-˜48°S), although no such reduction is seen in observations. These biases in HC-SWCRE covariability are linked to biases in the climatological HC extent. Notably, models with excessively equatorward climatological HC extents have weaker climatological LML subsidence and exhibit larger increases in LML subsidence with poleward HC edge expansion. This behavior, based on control climate interannual variability, has important implications for the CO2-forced model response. In 4×CO2-forced runs, models with excessively equatorward climatological HC extents produce stronger SW cloud radiative warming in the LML region and tend to have larger climate sensitivity values than models with more realistic climatological HC extents.

Dynamically downscaled climate simulations over North America: Methods, evaluation, and supporting documentation for users

USGS Publications Warehouse

Hostetler, S.W.; Alder, J.R.; Allan, A.M.

2011-01-01

We have completed an array of high-resolution simulations of present and future climate over Western North America (WNA) and Eastern North America (ENA) by dynamically downscaling global climate simulations using a regional climate model, RegCM3. The simulations are intended to provide long time series of internally consistent surface and atmospheric variables for use in climate-related research. In addition to providing high-resolution weather and climate data for the past, present, and future, we have developed an integrated data flow and methodology for processing, summarizing, viewing, and delivering the climate datasets to a wide range of potential users. Our simulations were run over 50- and 15-kilometer model grids in an attempt to capture more of the climatic detail associated with processes such as topographic forcing than can be captured by general circulation models (GCMs). The simulations were run using output from four GCMs. All simulations span the present (for example, 1968-1999), common periods of the future (2040-2069), and two simulations continuously cover 2010-2099. The trace gas concentrations in our simulations were the same as those of the GCMs: the IPCC 20th century time series for 1968-1999 and the A2 time series for simulations of the future. We demonstrate that RegCM3 is capable of producing present day annual and seasonal climatologies of air temperature and precipitation that are in good agreement with observations. Important features of the high-resolution climatology of temperature, precipitation, snow water equivalent (SWE), and soil moisture are consistently reproduced in all model runs over WNA and ENA. The simulations provide a potential range of future climate change for selected decades and display common patterns of the direction and magnitude of changes. As expected, there are some model to model differences that limit interpretability and give rise to uncertainties. Here, we provide background information about the GCMs and the RegCM3, a basic evaluation of the model output and examples of simulated future climate. We also provide information needed to access the web applications for visualizing and downloading the data, and give complete metadata that describe the variables in the datasets.

The Climate-Agriculture-Modeling and Decision Tool (CAMDT) for Climate Risk Management in Agriculture

NASA Astrophysics Data System (ADS)

Ines, A. V. M.; Han, E.; Baethgen, W.

2017-12-01

Advances in seasonal climate forecasts (SCFs) during the past decades have brought great potential to improve agricultural climate risk managements associated with inter-annual climate variability. In spite of popular uses of crop simulation models in addressing climate risk problems, the models cannot readily take seasonal climate predictions issued in the format of tercile probabilities of most likely rainfall categories (i.e, below-, near- and above-normal). When a skillful SCF is linked with the crop simulation models, the informative climate information can be further translated into actionable agronomic terms and thus better support strategic and tactical decisions. In other words, crop modeling connected with a given SCF allows to simulate "what-if" scenarios with different crop choices or management practices and better inform the decision makers. In this paper, we present a decision support tool, called CAMDT (Climate Agriculture Modeling and Decision Tool), which seamlessly integrates probabilistic SCFs to DSSAT-CSM-Rice model to guide decision-makers in adopting appropriate crop and agricultural water management practices for given climatic conditions. The CAMDT has a functionality to disaggregate a probabilistic SCF into daily weather realizations (either a parametric or non-parametric disaggregation method) and to run DSSAT-CSM-Rice with the disaggregated weather realizations. The convenient graphical user-interface allows easy implementation of several "what-if" scenarios for non-technical users and visualize the results of the scenario runs. In addition, the CAMDT also translates crop model outputs to economic terms once the user provides expected crop price and cost. The CAMDT is a practical tool for real-world applications, specifically for agricultural climate risk management in the Bicol region, Philippines, having a great flexibility for being adapted to other crops or regions in the world. CAMDT GitHub: https://github.com/Agro-Climate/CAMDT

Regional model simulations of New Zealand climate

NASA Astrophysics Data System (ADS)

Renwick, James A.; Katzfey, Jack J.; Nguyen, Kim C.; McGregor, John L.

1998-03-01

Simulation of New Zealand climate is examined through the use of a regional climate model nested within the output of the Commonwealth Scientific and Industrial Research Organisation nine-level general circulation model (GCM). R21 resolution GCM output is used to drive a regional model run at 125 km grid spacing over the Australasian region. The 125 km run is used in turn to drive a simulation at 50 km resolution over New Zealand. Simulations with a full seasonal cycle are performed for 10 model years. The focus is on the quality of the simulation of present-day climate, but results of a doubled-CO2 run are discussed briefly. Spatial patterns of mean simulated precipitation and surface temperatures improve markedly as horizontal resolution is increased, through the better resolution of the country's orography. However, increased horizontal resolution leads to a positive bias in precipitation. At 50 km resolution, simulated frequency distributions of daily maximum/minimum temperatures are statistically similar to those of observations at many stations, while frequency distributions of daily precipitation appear to be statistically different to those of observations at most stations. Modeled daily precipitation variability at 125 km resolution is considerably less than observed, but is comparable to, or exceeds, observed variability at 50 km resolution. The sensitivity of the simulated climate to changes in the specification of the land surface is discussed briefly. Spatial patterns of the frequency of extreme temperatures and precipitation are generally well modeled. Under a doubling of CO2, the frequency of precipitation extremes changes only slightly at most locations, while air frosts become virtually unknown except at high-elevation sites.

Climate Framework for Uncertainty, Negotiation, and Distribution (FUND)

EPA Science Inventory

FUND is an Integrated Assessment model that links socioeconomic, technology, and emission scenarios with atmospheric chemistry, climate dynamics, sea level rise, and the resulting economic impacts. The model runs in time-steps of one year from 1950 to 2300, and distinguishes 16 m...

How does spatial variability of climate affect catchment streamflow predictions?

EPA Science Inventory

Spatial variability of climate can negatively affect catchment streamflow predictions if it is not explicitly accounted for in hydrologic models. In this paper, we examine the changes in streamflow predictability when a hydrologic model is run with spatially variable (distribute...

Uncertainty Quantification given Discontinuous Climate Model Response and a Limited Number of Model Runs

NASA Astrophysics Data System (ADS)

Sargsyan, K.; Safta, C.; Debusschere, B.; Najm, H.

2010-12-01

Uncertainty quantification in complex climate models is challenged by the sparsity of available climate model predictions due to the high computational cost of model runs. Another feature that prevents classical uncertainty analysis from being readily applicable is bifurcative behavior in climate model response with respect to certain input parameters. A typical example is the Atlantic Meridional Overturning Circulation. The predicted maximum overturning stream function exhibits discontinuity across a curve in the space of two uncertain parameters, namely climate sensitivity and CO2 forcing. We outline a methodology for uncertainty quantification given discontinuous model response and a limited number of model runs. Our approach is two-fold. First we detect the discontinuity with Bayesian inference, thus obtaining a probabilistic representation of the discontinuity curve shape and location for arbitrarily distributed input parameter values. Then, we construct spectral representations of uncertainty, using Polynomial Chaos (PC) expansions on either side of the discontinuity curve, leading to an averaged-PC representation of the forward model that allows efficient uncertainty quantification. The approach is enabled by a Rosenblatt transformation that maps each side of the discontinuity to regular domains where desirable orthogonality properties for the spectral bases hold. We obtain PC modes by either orthogonal projection or Bayesian inference, and argue for a hybrid approach that targets a balance between the accuracy provided by the orthogonal projection and the flexibility provided by the Bayesian inference - where the latter allows obtaining reasonable expansions without extra forward model runs. The model output, and its associated uncertainty at specific design points, are then computed by taking an ensemble average over PC expansions corresponding to possible realizations of the discontinuity curve. The methodology is tested on synthetic examples of discontinuous model data with adjustable sharpness and structure. This work was supported by the Sandia National Laboratories Seniors’ Council LDRD (Laboratory Directed Research and Development) program. Sandia National Laboratories is a multi-program laboratory operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Company, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.

The global warming in the North Atlantic Sector and the role of the ocean

NASA Astrophysics Data System (ADS)

Hand, R.; Keenlyside, N. S.; Greatbatch, R. J.; Omrani, N. E.

2014-12-01

This work presents an analysis of North Atlantic ocean-atmosphere interaction in a warming climate, based on a long-term earth system model experiment forced by the RCP 8.5 scenario, the strongest greenhouse gas forcing used in the climate projections for the 5th Assessement report of the Intergovernmental Panel on Climate Change). In addition to a global increase in SSTs as a direct response to the radiative forcing, the model shows a distinct change of the local sea surface temperature (SST hereafter) patterns in the Gulf Stream region: The SST front moves northward by several hundred kilometers, likely as a response of the wind-driven part of the oceanic surface circulation, and becomes more zonal. As a consequence of a massive slowdown of the Atlantic Meridional Overturning Circulation, the northeast North Atlantic only shows a moderate warming compared to the rest of the ocean. The feedback of these changes on the atmosphere was studied in a set of sensitivity experiments based on the SST climatology of the coupled runs. The set consists of a control run based on the historical run, a run using the full SST from the coupled RCP 8.5 run and two runs, where the SST signal was deconstructed into a homogenous mean warming part and a local pattern change. In the region of the precipitation maximum in the historical run the future scenario shows an increase of absolute SSTs, but a significant decrease in local precipitation, low-level convergence and upward motion. Since warmer SSTs usually cause the opposite, this indicates that the local response in that region is connected to the (with respect to the historical run) weakened SST gradients rather than to the absolute SST. Consistently, the model shows enhanced precipitation north of this region, where the SST gradients are enhanced. However, the signal restricts to the low and mid-troposphere and does not reach the higher model levels. There is little evidence for a large-scale response to the changes in the Gulf Stream region; instead, the large scale signal is mainly controlled by the warmer background state and the AMOC slowdown and influenced by tropical SSTs. In a warmer climate the same change in SST gradient has a stronger effect on precipitation and the model produces a slightly enhanced North Atlantic storm track.

Climate impacts of deforestation/land-use changes in Central South America in the PRECIS regional climate model: mean precipitation and temperature response to present and future deforestation scenarios.

PubMed

Canziani, Pablo O; Carbajal Benitez, Gerardo

2012-01-01

Deforestation/land-use changes are major drivers of regional climate change in central South America, impacting upon Amazonia and Gran Chaco ecoregions. Most experimental and modeling studies have focused on the resulting perturbations within Amazonia. Using the Regional Climate Model PRECIS, driven by ERA-40 reanalysis and ECHAM4 Baseline model for the period 1961-2000 (40-year runs), potential effects of deforestation/land-use changes in these and other neighboring ecoregions are evaluated. Current 2002 and estimated 2030 land-use scenarios are used to assess PRECIS's response during 1960-2000. ERA-40 and ECHAM4 Baseline driven runs yield similar results. Precipitation changes for 2002 and 2030 land-use scenarios, while significant within deforested areas, do not result in significant regional changes. For temperature significant changes are found within deforested areas and beyond, with major temperature enhancements during winter and spring. Given the current climate, primary effects of deforestation/land-use changes remain mostly confined to the tropical latitudes of Gran Chaco, and Amazonia.

Climate Impacts of Deforestation/Land-Use Changes in Central South America in the PRECIS Regional Climate Model: Mean Precipitation and Temperature Response to Present and Future Deforestation Scenarios

PubMed Central

Canziani, Pablo O.; Carbajal Benitez, Gerardo

2012-01-01

Deforestation/land-use changes are major drivers of regional climate change in central South America, impacting upon Amazonia and Gran Chaco ecoregions. Most experimental and modeling studies have focused on the resulting perturbations within Amazonia. Using the Regional Climate Model PRECIS, driven by ERA-40 reanalysis and ECHAM4 Baseline model for the period 1961–2000 (40-year runs), potential effects of deforestation/land-use changes in these and other neighboring ecoregions are evaluated. Current 2002 and estimated 2030 land-use scenarios are used to assess PRECIS's response during 1960–2000. ERA-40 and ECHAM4 Baseline driven runs yield similar results. Precipitation changes for 2002 and 2030 land-use scenarios, while significant within deforested areas, do not result in significant regional changes. For temperature significant changes are found within deforested areas and beyond, with major temperature enhancements during winter and spring. Given the current climate, primary effects of deforestation/land-use changes remain mostly confined to the tropical latitudes of Gran Chaco, and Amazonia. PMID:22645487

A user-friendly earth system model of low complexity: the ESCIMO system dynamics model of global warming towards 2100

NASA Astrophysics Data System (ADS)

Randers, Jorgen; Golüke, Ulrich; Wenstøp, Fred; Wenstøp, Søren

2016-11-01

We have made a simple system dynamics model, ESCIMO (Earth System Climate Interpretable Model), which runs on a desktop computer in seconds and is able to reproduce the main output from more complex climate models. ESCIMO represents the main causal mechanisms at work in the Earth system and is able to reproduce the broad outline of climate history from 1850 to 2015. We have run many simulations with ESCIMO to 2100 and beyond. In this paper we present the effects of introducing in 2015 six possible global policy interventions that cost around USD 1000 billion per year - around 1 % of world GDP. We tentatively conclude (a) that these policy interventions can at most reduce the global mean surface temperature - GMST - by up to 0.5 °C in 2050 and up to 1.0 °C in 2100 relative to no intervention. The exception is injection of aerosols into the stratosphere, which can reduce the GMST by more than 1.0 °C in a decade but creates other serious problems. We also conclude (b) that relatively cheap human intervention can keep global warming in this century below +2 °C relative to preindustrial times. Finally, we conclude (c) that run-away warming is unlikely to occur in this century but is likely to occur in the longer run. The ensuing warming is slow, however. In ESCIMO, it takes several hundred years to lift the GMST to +3 °C above preindustrial times through gradual self-reinforcing melting of the permafrost. We call for research to test whether more complex climate models support our tentative conclusions from ESCIMO.

Near-global climate simulation at 1 km resolution: establishing a performance baseline on 4888 GPUs with COSMO 5.0

NASA Astrophysics Data System (ADS)

Fuhrer, Oliver; Chadha, Tarun; Hoefler, Torsten; Kwasniewski, Grzegorz; Lapillonne, Xavier; Leutwyler, David; Lüthi, Daniel; Osuna, Carlos; Schär, Christoph; Schulthess, Thomas C.; Vogt, Hannes

2018-05-01

The best hope for reducing long-standing global climate model biases is by increasing resolution to the kilometer scale. Here we present results from an ultrahigh-resolution non-hydrostatic climate model for a near-global setup running on the full Piz Daint supercomputer on 4888 GPUs (graphics processing units). The dynamical core of the model has been completely rewritten using a domain-specific language (DSL) for performance portability across different hardware architectures. Physical parameterizations and diagnostics have been ported using compiler directives. To our knowledge this represents the first complete atmospheric model being run entirely on accelerators on this scale. At a grid spacing of 930 m (1.9 km), we achieve a simulation throughput of 0.043 (0.23) simulated years per day and an energy consumption of 596 MWh per simulated year. Furthermore, we propose a new memory usage efficiency (MUE) metric that considers how efficiently the memory bandwidth - the dominant bottleneck of climate codes - is being used.

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.

VEMAP vs VINCERA: a DGVM sensitivity to differences in climate scenarios

Treesearch

Dominique Bachelet; James Lenihan; Ray Drapek; Ronald Neilson

2008-01-01

The MCI DGVM has been used in two international model comparison projects, VEMAP (Vegetation Ecosystem Modeling and Analysis Project) and VINCERA (Vulnerability and Impacts of North American forests to Climate Change: Ecosystem Responses and Adaptation). The latest version of MC1 was run on both VINCERA and VEMAP climate and soil input data to document how a change in...

Software Simplifies the Sharing of Numerical Models

NASA Technical Reports Server (NTRS)

2014-01-01

To ease the sharing of climate models with university students, Goddard Space Flight Center awarded SBIR funding to Reston, Virginia-based Parabon Computation Inc., a company that specializes in cloud computing. The firm developed a software program capable of running climate models over the Internet, and also created an online environment for people to collaborate on developing such models.

Climate Variability In The Euro-atlantic Sector As Simulated By Echam4

NASA Astrophysics Data System (ADS)

Menezes, I.; Corte-Real, J.; Ramos, A.; Conde, F.

The atmosphere is a fundamental component of the climate system and its influence in local and global climates results from its composition, structure and motion. The best available tools to simulate future climates are coupled atmosphere-ocean general circulation models (AOGCMs), ECHAM4 (T42 L19)[1] being a very relevant exam- ple of such a model due to its elaborated parametrizations of physical processes. The purpose of this work is twofold : (1) to assess the ability of ECHAM4 in reproducing the reference climate of 1961-1990, over the Euro-Atlantic sector (29N-71N; 67W- 59E) in terms of mean sea level pressure, surface temperature and total precipitation; (2) to evaluate the expected changes of the same climate elements in a warmer world. To attain the first goal the ECHAMSs control run output is compared with observed data obtained from the Climatic Research Unit (CRU data set)[2-5]; to achieve the second objective, the modelSs control run is compared with its transient run forced by greenhouse gases. In both cases, comparisons are made in terms of mean values, variability in space and time and extremes. References [1] E. Roeckner, K. Arpe, L. Bengtsson, M. Christoph, M. Claussen, L. Dümenil, M. Esch, M. Giorgetta, U. Schlese, and U. Schulzweida, 1996: The atmospheric gen- eral circulation model ECHAM4: Model description and simulation of present-day climate. Max Planck Institut für Meteorologie, Report No. 218, Hamburg, Germany, 90 pp. [2] M. Hulme, D. Conway, P.D. Jones, T. Jiang, E.M. Barrow, and C. Turney (1995), Construction of a 1961-90 European climatology for climate change impacts and mod- elling applications, Int. J. Climatol., 15, 1333-1363. [3] M. Hulme (1994), The cost of climate data U a European experience, Weather, 49, 168-175. [4] M. Hulme, and M.G. New (1997), Dependence of large-scale precipitation clima- tologies on temporal and spatial sampling, J. Climate, 10, 1099-1113. 1 [5] C.J. Willmot, S.M. Robeson and M.J. Janis (1996), Comparison of approaches for estimating time-averaged precipitation using data from the United States, Int. J. Cli- matol., 16, 1103-1115. 2

Do downscaled general circulation models reliably simulate historical climatic conditions?

USGS Publications Warehouse

Bock, Andrew R.; Hay, Lauren E.; McCabe, Gregory J.; Markstrom, Steven L.; Atkinson, R. Dwight

2018-01-01

The accuracy of statistically downscaled (SD) general circulation model (GCM) simulations of monthly surface climate for historical conditions (1950–2005) was assessed for the conterminous United States (CONUS). The SD monthly precipitation (PPT) and temperature (TAVE) from 95 GCMs from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) were used as inputs to a monthly water balance model (MWBM). Distributions of MWBM input (PPT and TAVE) and output [runoff (RUN)] variables derived from gridded station data (GSD) and historical SD climate were compared using the Kolmogorov–Smirnov (KS) test For all three variables considered, the KS test results showed that variables simulated using CMIP5 generally are more reliable than those derived from CMIP3, likely due to improvements in PPT simulations. At most locations across the CONUS, the largest differences between GSD and SD PPT and RUN occurred in the lowest part of the distributions (i.e., low-flow RUN and low-magnitude PPT). Results indicate that for the majority of the CONUS, there are downscaled GCMs that can reliably simulate historical climatic conditions. But, in some geographic locations, none of the SD GCMs replicated historical conditions for two of the three variables (PPT and RUN) based on the KS test, with a significance level of 0.05. In these locations, improved GCM simulations of PPT are needed to more reliably estimate components of the hydrologic cycle. Simple metrics and statistical tests, such as those described here, can provide an initial set of criteria to help simplify GCM selection.

The Role of Sea Ice in 2 x CO2 Climate Model Sensitivity. Part 2; Hemispheric Dependencies

NASA Technical Reports Server (NTRS)

Rind, D.; Healy, R.; Parkinson, C.; Martinson, D.

1997-01-01

How sensitive are doubled CO2 simulations to GCM control-run sea ice thickness and extent? This issue is examined in a series of 10 control-run simulations with different sea ice and corresponding doubled CO2 simulations. Results show that with increased control-run sea ice coverage in the Southern Hemisphere, temperature sensitivity with climate change is enhanced, while there is little effect on temperature sensitivity of (reasonable) variations in control-run sea ice thickness. In the Northern Hemisphere the situation is reversed: sea ice thickness is the key parameter, while (reasonable) variations in control-run sea ice coverage are of less importance. In both cases, the quantity of sea ice that can be removed in the warmer climate is the determining factor. Overall, the Southern Hemisphere sea ice coverage change had a larger impact on global temperature, because Northern Hemisphere sea ice was sufficiently thick to limit its response to doubled CO2, and sea ice changes generally occurred at higher latitudes, reducing the sea ice-albedo feedback. In both these experiments and earlier ones in which sea ice was not allowed to change, the model displayed a sensitivity of -0.02 C global warming per percent change in Southern Hemisphere sea ice coverage.

Bringing a Realistic Global Climate Modeling Experience to a Broader Audience

NASA Astrophysics Data System (ADS)

Sohl, L. E.; Chandler, M. A.; Zhou, J.

2010-12-01

EdGCM, the Educational Global Climate Model, was developed with the goal of helping students learn about climate change and climate modeling by giving them the ability to run a genuine NASA global climate model (GCM) on a desktop computer. Since EdGCM was first publicly released in January 2005, tens of thousands of users on seven continents have downloaded the software. EdGCM has been utilized by climate science educators from middle school through graduate school levels, and on occasion even by researchers who otherwise do not have ready access to climate model at national labs in the U.S. and elsewhere. The EdGCM software is designed to walk users through the same process a climate scientist would use in designing and running simulations, and analyzing and visualizing GCM output. Although the current interface design gives users a clear view of some of the complexities involved in using a climate model, it can be daunting for users whose main focus is on climate science rather than modeling per se. As part of the work funded by NASA’s Global Climate Change Education (GCCE) program, we will begin modifications to the user interface that will improve the accessibility of EdGCM to a wider array of users, especially at the middle school and high school levels, by: 1) Developing an automated approach (a “wizard”) to simplify the user experience in setting up new climate simulations; 2) Produce a catalog of “rediscovery experiments” that allow users to reproduce published climate model results, and in some cases compare model projections to real world data; and 3) Enhance distance learning and online learning opportunities through the development of a web-based interface. The prototypes for these modifications will then be presented to educators belonging to an EdGCM Users Group for feedback, so that we can further refine the EdGCM software, and thus deliver the tools and materials educators want and need across a wider range of learning environments.

Convergence in France facing Big Data era and Exascale challenges for Climate Sciences

NASA Astrophysics Data System (ADS)

Denvil, Sébastien; Dufresne, Jean-Louis; Salas, David; Meurdesoif, Yann; Valcke, Sophie; Caubel, Arnaud; Foujols, Marie-Alice; Servonnat, Jérôme; Sénési, Stéphane; Derouillat, Julien; Voury, Pascal

2014-05-01

The presentation will introduce a french national project : CONVERGENCE that has been funded for four years. This project will tackle big data and computational challenges faced by climate modeling community in HPC context. Model simulations are central to the study of complex mechanisms and feedbacks in the climate system and to provide estimates of future and past climate changes. Recent trends in climate modelling are to add more physical components in the modelled system, increasing the resolution of each individual component and the more systematic use of large suites of simulations to address many scientific questions. Climate simulations may therefore differ in their initial state, parameter values, representation of physical processes, spatial resolution, model complexity, and degree of realism or degree of idealisation. In addition, there is a strong need for evaluating, improving and monitoring the performance of climate models using a large ensemble of diagnostics and better integration of model outputs and observational data. High performance computing is currently reaching the exascale and has the potential to produce this exponential increase of size and numbers of simulations. However, post-processing, analysis, and exploration of the generated data have stalled and there is a strong need for new tools to cope with the growing size and complexity of the underlying simulations and datasets. Exascale simulations require new scalable software tools to generate, manage and mine those simulations ,and data to extract the relevant information and to take the correct decision. The primary purpose of this project is to develop a platform capable of running large ensembles of simulations with a suite of models, to handle the complex and voluminous datasets generated, to facilitate the evaluation and validation of the models and the use of higher resolution models. We propose to gather interdisciplinary skills to design, using a component-based approach, a specific programming environment for scalable scientific simulations and analytics, integrating new and efficient ways of deploying and analysing the applications on High Performance Computing (HPC) system. CONVERGENCE, gathering HPC and informatics expertise that cuts across the individual partners and the broader HPC community, will allow the national climate community to leverage information technology (IT) innovations to address its specific needs. Our methodology consists in developing an ensemble of generic elements needed to run the French climate models with different grids and different resolution, ensuring efficient and reliable execution of these models, managing large volume and number of data and allowing analysis of the results and precise evaluation of the models. These elements include data structure definition and input-output (IO), code coupling and interpolation, as well as runtime and pre/post-processing environments. A common data and metadata structure will allow transferring consistent information between the various elements. All these generic elements will be open source and publicly available. The IPSL-CM and CNRM-CM climate models will make use of these elements that will constitute a national platform for climate modelling. This platform will be used, in its entirety, to optimise and tune the next version of the IPSL-CM model and to develop a global coupled climate model with a regional grid refinement. It will also be used, at least partially, to run ensembles of the CNRM-CM model at relatively high resolution and to run a very-high resolution prototype of this model. The climate models we developed are already involved in many international projects. For instance we participate to the CMIP (Coupled Model Intercomparison Project) project that is very demanding but has a high visibility: its results are widely used and are in particular synthesised in the IPCC (Intergovernmental Panel on Climate Change) assessment reports. The CONVERGENCE project will constitute an invaluable step for the French climate community to prepare and better contribute to the next phase of the CMIP project.

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.

Generating High Resolution Climate Scenarios Through Regional Climate Modelling Over Southern Africa

NASA Astrophysics Data System (ADS)

Ndhlovu, G. Z.; Woyessa, Y. E.; Vijayaraghavan, S.

2017-12-01

limate change has impacted the global environment and the Continent of Africa, especially Southern Africa, regarded as one of the most vulnerable regions in Africa, has not been spared from these impacts. Global Climate Models (GCMs) with coarse horizontal resolutions of 150-300 km do not provide sufficient details at the local basin scale due to mismatch between the size of river basins and the grid cell of the GCM. This makes it difficult to apply the outputs of GCMs directly to impact studies such as hydrological modelling. This necessitates the use of regional climate modelling at high resolutions that provide detailed information at regional and local scales to study both climate change and its impacts. To this end, an experiment was set up and conducted with PRECIS, a regional climate model, to generate climate scenarios at a high resolution of 25km for the local region in Zambezi River basin of Southern Africa. The major input data used included lateral and surface boundary conditions based on the GCMs. The data is processed, analysed and compared with CORDEX climate change project data generated for Africa. This paper, highlights the major differences of the climate scenarios generated by PRECIS Model and CORDEX Project for Africa and further gives recommendations for further research on generation of climate scenarios. The climatic variables such as precipitation and temperatures have been analysed for flood and droughts in the region. The paper also describes the setting up and running of an experiment using a high-resolution PRECIS model. In addition, a description has been made in running the model and generating the output variables on a sub basin scale. Regional climate modelling which provides information on climate change impact may lead to enhanced understanding of adaptive water resources management. Understanding the regional climate modelling results on sub basin scale is the first step in analysing complex hydrological processes and a basis for designing of adaptation and mitigation strategies in the region. Key words: Climate change, regional climate modelling, hydrological processes, extremes, scenarios [1] Corresponding author: Email:gndhlovu@cut.ac.za Tel:+27 (0) 51 507 3072

Flexible Environments for Grand-Challenge Simulation in Climate Science

NASA Astrophysics Data System (ADS)

Pierrehumbert, R.; Tobis, M.; Lin, J.; Dieterich, C.; Caballero, R.

2004-12-01

Current climate models are monolithic codes, generally in Fortran, aimed at high-performance simulation of the modern climate. Though they adequately serve their designated purpose, they present major barriers to application in other problems. Tailoring them to paleoclimate of planetary simulations, for instance, takes months of work. Theoretical studies, where one may want to remove selected processes or break feedback loops, are similarly hindered. Further, current climate models are of little value in education, since the implementation of textbook concepts and equations in the code is obscured by technical detail. The Climate Systems Center at the University of Chicago seeks to overcome these limitations by bringing modern object-oriented design into the business of climate modeling. Our ultimate goal is to produce an end-to-end modeling environment capable of configuring anything from a simple single-column radiative-convective model to a full 3-D coupled climate model using a uniform, flexible interface. Technically, the modeling environment is implemented as a Python-based software component toolkit: key number-crunching procedures are implemented as discrete, compiled-language components 'glued' together and co-ordinated by Python, combining the high performance of compiled languages and the flexibility and extensibility of Python. We are incrementally working towards this final objective following a series of distinct, complementary lines. We will present an overview of these activities, including PyOM, a Python-based finite-difference ocean model allowing run-time selection of different Arakawa grids and physical parameterizations; CliMT, an atmospheric modeling toolkit providing a library of 'legacy' radiative, convective and dynamical modules which can be knitted into dynamical models, and PyCCSM, a version of NCAR's Community Climate System Model in which the coupler and run-control architecture are re-implemented in Python, augmenting its flexibility and adaptability.

Accelerating Climate Simulations Through Hybrid Computing

NASA Technical Reports Server (NTRS)

Zhou, Shujia; Sinno, Scott; Cruz, Carlos; Purcell, Mark

2009-01-01

Unconventional multi-core processors (e.g., IBM Cell B/E and NYIDIDA GPU) have emerged as accelerators in climate simulation. However, climate models typically run on parallel computers with conventional processors (e.g., Intel and AMD) using MPI. Connecting accelerators to this architecture efficiently and easily becomes a critical issue. When using MPI for connection, we identified two challenges: (1) identical MPI implementation is required in both systems, and; (2) existing MPI code must be modified to accommodate the accelerators. In response, we have extended and deployed IBM Dynamic Application Virtualization (DAV) in a hybrid computing prototype system (one blade with two Intel quad-core processors, two IBM QS22 Cell blades, connected with Infiniband), allowing for seamlessly offloading compute-intensive functions to remote, heterogeneous accelerators in a scalable, load-balanced manner. Currently, a climate solar radiation model running with multiple MPI processes has been offloaded to multiple Cell blades with approx.10% network overhead.

ARCAS (ACACIA Regional Climate-data Access System) -- a Web Access System for Climate Model Data Access, Visualization and Comparison

NASA Astrophysics Data System (ADS)

Hakkarinen, C.; Brown, D.; Callahan, J.; hankin, S.; de Koningh, M.; Middleton-Link, D.; Wigley, T.

2001-05-01

A Web-based access system to climate model output data sets for intercomparison and analysis has been produced, using the NOAA-PMEL developed Live Access Server software as host server and Ferret as the data serving and visualization engine. Called ARCAS ("ACACIA Regional Climate-data Access System"), and publicly accessible at http://dataserver.ucar.edu/arcas, the site currently serves climate model outputs from runs of the NCAR Climate System Model for the 21st century, for Business as Usual and Stabilization of Greenhouse Gas Emission scenarios. Users can select, download, and graphically display single variables or comparisons of two variables from either or both of the CSM model runs, averaged for monthly, seasonal, or annual time resolutions. The time length of the averaging period, and the geographical domain for download and display, are fully selectable by the user. A variety of arithmetic operations on the data variables can be computed "on-the-fly", as defined by the user. Expansions of the user-selectable options for defining analysis options, and for accessing other DOD-compatible ("Distributed Ocean Data System-compatible") data sets, residing at locations other than the NCAR hardware server on which ARCAS operates, are planned for this year. These expansions are designed to allow users quick and easy-to-operate web-based access to the largest possible selection of climate model output data sets available throughout the world.

Projected changes over western Canada using convection-permitting regional climate model and the pseudo-global warming method

NASA Astrophysics Data System (ADS)

Li, Y.; Kurkute, S.; Chen, L.

2017-12-01

Results from the General Circulation Models (GCMs) suggest more frequent and more severe extreme rain events in a climate warmer than the present. However, current GCMs cannot accurately simulate extreme rainfall events of short duration due to their coarse model resolutions and parameterizations. This limitation makes it difficult to provide the detailed quantitative information for the development of regional adaptation and mitigation strategies. Dynamical downscaling using nested Regional Climate Models (RCMs) are able to capture key regional and local climate processes with an affordable computational cost. Recent studies have demonstrated that the downscaling of GCM results with weather-permitting mesoscale models, such as the pseudo-global warming (PGW) technique, could be a viable and economical approach of obtaining valuable climate change information on regional scales. We have conducted a regional climate 4-km Weather Research and Forecast Model (WRF) simulation with one domain covering the whole western Canada, for a historic run (2000-2015) and a 15-year future run to 2100 and beyond with the PGW forcing. The 4-km resolution allows direct use of microphysics and resolves the convection explicitly, thus providing very convincing spatial detail. With this high-resolution simulation, we are able to study the convective mechanisms, specifically the control of convections over the Prairies, the projected changes of rainfall regimes, and the shift of the convective mechanisms in a warming climate, which has never been examined before numerically at such large scale with such high resolution.

Isolating the Effects of the Warming Trend from the General Climate Change in Water Resources: California Case

NASA Astrophysics Data System (ADS)

Wang, J.; Yin, H.; Chung, F.

2008-12-01

While the population growth, the future land use change, and the desire for better environmental preservation and protection are adding up pressure on water resources management in California, California is facing an extra challenge of addressing potential climate change impacts on water supple and demand in California. The concerns on water facilities planning and flood control caused by climate change include modified precipitation patterns, changes in snow levels and runoff patterns due to increased air temperatures. Although long-term climate projections are largely uncertain, there appears to be a strong consistency in predicting the warming trend of future surface temperature, and the resulting shift in the seasonal patterns of runoff. However, projected changes in precipitation (wetting or drying), which control annual runoff, are far less certain. This paper attempts to separate the effects of warming trend from the effects of precipitation trend on water planning especially in California where reservoir operations are more sensitive to seasonal patterns of runoff than to the total annual runoff. The water resources systems planning model, CALSIM2, is used to evaluate climate change impact on water resource management in California. Rather than directly ingesting estimated streamflows from climate model projections into CALSIM2, a three step perturbation ratio method is proposed to introduce climate change impact into the planning model. Firstly, monthly perturbation ratio of projected monthly inflow to simulated historical monthly inflow is applied to observed historical monthly inflow to generate climate change inflows to major dams and reservoirs. To isolate the effects of warming trend on water resources, a further annual inflow adjustment is applied to the inflows generated in step one to preserve the volume of the observed annual inflow. To re-introduce the effects of precipitation trend on water resources, an additional inflow trend adjustment is applied to the adjusted climate change inflow. Therefore, three CALSIM2 experiments will be implemented: (1) base run with the observed historic inflow (1921 to 2003); (2) sensitivity run with the adjusted climate change inflow through annual inflow adjustment; (3) sensitivity run with the adjusted climate change inflow through annual inflow adjustment and inflow trend adjustment. To account for the variability of various climate models in projecting future climates, the uncertainty in future emission scenarios, and the difference in different projection periods, estimated inflows from 6 climate models for 2 emission scenarios (A2 and B1) and two projection periods (2030-2059 and 2070-2099) are included in the CALSIM model experiments.

Climate SPHINX: High-resolution present-day and future climate simulations with an improved representation of small-scale variability

NASA Astrophysics Data System (ADS)

Davini, Paolo; von Hardenberg, Jost; Corti, Susanna; Subramanian, Aneesh; Weisheimer, Antje; Christensen, Hannah; Juricke, Stephan; Palmer, Tim

2016-04-01

The PRACE Climate SPHINX project investigates the sensitivity of climate simulations to model resolution and stochastic parameterization. The EC-Earth Earth-System Model is used to explore the impact of stochastic physics in 30-years climate integrations as a function of model resolution (from 80km up to 16km for the atmosphere). The experiments include more than 70 simulations in both a historical scenario (1979-2008) and a climate change projection (2039-2068), using RCP8.5 CMIP5 forcing. A total amount of 20 million core hours will be used at end of the project (March 2016) and about 150 TBytes of post-processed data will be available to the climate community. Preliminary results show a clear improvement in the representation of climate variability over the Euro-Atlantic following resolution increase. More specifically, the well-known atmospheric blocking negative bias over Europe is definitely resolved. High resolution runs also show improved fidelity in representation of tropical variability - such as the MJO and its propagation - over the low resolution simulations. It is shown that including stochastic parameterization in the low resolution runs help to improve some of the aspects of the MJO propagation further. These findings show the importance of representing the impact of small scale processes on the large scale climate variability either explicitly (with high resolution simulations) or stochastically (in low resolution simulations).

Climate Local Information over the Mediterranean to Respond User Needs

NASA Astrophysics Data System (ADS)

Ruti, P.

2012-12-01

CLIM-RUN aims at developing a protocol for applying new methodologies and improved modeling and downscaling tools for the provision of adequate climate information at regional to local scale that is relevant to and usable by different sectors of society (policymakers, industry, cities, etc.). Differently from current approaches, CLIM-RUN will develop a bottom-up protocol directly involving stakeholders early in the process with the aim of identifying well defined needs at the regional to local scale. The improved modeling and downscaling tools will then be used to optimally respond to these specific needs. The protocol is assessed by application to relevant case studies involving interdependent sectors, primarily tourism and energy, and natural hazards (wild fires) for representative target areas (mountainous regions, coastal areas, islands). The region of interest for the project is the Greater Mediterranean area, which is particularly important for two reasons. First, the Mediterranean is a recognized climate change hot-spot, i.e. a region particularly sensitive and vulnerable to global warming. Second, while a number of countries in Central and Northern Europe have already in place well developed climate service networks (e.g. the United Kingdom and Germany), no such network is available in the Mediterranean. CLIM-RUN is thus also intended to provide the seed for the formation of a Mediterranean basin-side climate service network which would eventually converge into a pan-European network. The general time horizon of interest for the project is the future period 2010-2050, a time horizon that encompasses the contributions of both inter-decadal variability and greenhouse-forced climate change. In particular, this time horizon places CLIM-RUN within the context of a new emerging area of research, that of decadal prediction, which will provide a strong potential for novel research.

[CLIMATE CHANGE AND ALLERGIC AIRWAY DISEASE] OBSERVATIONAL,LABORATORY, AND MODELING STUDIES OF THE IMPACTS OF CLIMATE CHANGE ONALLERGIC AIRWAY DISEASE

EPA Science Inventory

Based on these data and preliminary studies, this proposal will be composed of a multiscale source-to-dose analysis approach for assessing the exposure interactions of environmental and biological systems. Once the entire modeling system is validated, it will run f...

Sensitivity study of heavy precipitation in Limited Area Model climate simulations: influence of the size of the domain and the use of the spectral nudging technique

NASA Astrophysics Data System (ADS)

Colin, Jeanne; Déqué, Michel; Radu, Raluca; Somot, Samuel

2010-10-01

We assess the impact of two sources of uncertainties in a limited area model (LAM) on the representation of intense precipitation: the size of the domain of integration and the use of the spectral nudging technique (driving of the large-scale within the domain of integration). We work in a perfect-model approach where the LAM is driven by a general circulation model (GCM) run at the same resolution and sharing the same physics and dynamics as the LAM. A set of three 50 km resolution simulations run over Western Europe with the LAM ALADIN-Climate and the GCM ARPEGE-Climate are performed to address this issue. Results are consistent with previous studies regarding the seasonal-mean fields. Furthermore, they show that neither the use of the spectral nudging nor the choice of a small domain are detrimental to the modelling of heavy precipitation in the present experiment.

A personal perspective on modelling the climate system.

PubMed

Palmer, T N

2016-04-01

Given their increasing relevance for society, I suggest that the climate science community itself does not treat the development of error-free ab initio models of the climate system with sufficient urgency. With increasing levels of difficulty, I discuss a number of proposals for speeding up such development. Firstly, I believe that climate science should make better use of the pool of post-PhD talent in mathematics and physics, for developing next-generation climate models. Secondly, I believe there is more scope for the development of modelling systems which link weather and climate prediction more seamlessly. Finally, here in Europe, I call for a new European Programme on Extreme Computing and Climate to advance our ability to simulate climate extremes, and understand the drivers of such extremes. A key goal for such a programme is the development of a 1 km global climate system model to run on the first exascale supercomputers in the early 2020s.

Implication of Agricultural Land Use Change on Regional Climate Projection

NASA Astrophysics Data System (ADS)

Wang, G.; Ahmed, K. F.; You, L.

2015-12-01

Agricultural land use plays an important role in land-atmosphere interaction. Agricultural activity is one of the most important processes driving human-induced land use land cover change (LULCC) in a region. In addition to future socioeconomic changes, climate-induced changes in crop yield represent another important factor shaping agricultural land use. In feedback, the resulting LULCC influences the direction and magnitude of global, regional and local climate change by altering Earth's radiative equilibrium. Therefore, assessment of climate change impact on future agricultural land use and its feedback is of great importance in climate change study. In this study, to evaluate the feedback of projected land use changes to the regional climate in West Africa, we employed an asynchronous coupling between a regional climate model (RegCM) and a prototype land use projection model (LandPro). The LandPro model, which was developed to project the future change in agricultural land use and the resulting shift in natural vegetation in West Africa, is a spatially explicit model that can account for both climate and socioeconomic changes in projecting future land use changes. In the asynchronously coupled modeling framework, LandPro was run for every five years during the period of 2005-2050 accounting for climate-induced change in crop yield and socioeconomic changes to project the land use pattern by the mid-21st century. Climate data at 0.5˚ was derived from RegCM to drive the crop model DSSAT for each of the five-year periods to simulate crop yields, which was then provided as input data to LandPro. Subsequently, the land use land cover map required to run RegCM was updated every five years using the outputs from the LandPro simulations. Results from the coupled model simulations improve the understanding of climate change impact on future land use and the resulting feedback to regional climate.

Climate@Home: Crowdsourcing Climate Change Research

NASA Astrophysics Data System (ADS)

Xu, C.; Yang, C.; Li, J.; Sun, M.; Bambacus, M.

2011-12-01

Climate change deeply impacts human wellbeing. Significant amounts of resources have been invested in building super-computers that are capable of running advanced climate models, which help scientists understand climate change mechanisms, and predict its trend. Although climate change influences all human beings, the general public is largely excluded from the research. On the other hand, scientists are eagerly seeking communication mediums for effectively enlightening the public on climate change and its consequences. The Climate@Home project is devoted to connect the two ends with an innovative solution: crowdsourcing climate computing to the general public by harvesting volunteered computing resources from the participants. A distributed web-based computing platform will be built to support climate computing, and the general public can 'plug-in' their personal computers to participate in the research. People contribute the spare computing power of their computers to run a computer model, which is used by scientists to predict climate change. Traditionally, only super-computers could handle such a large computing processing load. By orchestrating massive amounts of personal computers to perform atomized data processing tasks, investments on new super-computers, energy consumed by super-computers, and carbon release from super-computers are reduced. Meanwhile, the platform forms a social network of climate researchers and the general public, which may be leveraged to raise climate awareness among the participants. A portal is to be built as the gateway to the climate@home project. Three types of roles and the corresponding functionalities are designed and supported. The end users include the citizen participants, climate scientists, and project managers. Citizen participants connect their computing resources to the platform by downloading and installing a computing engine on their personal computers. Computer climate models are defined at the server side. Climate scientists configure computer model parameters through the portal user interface. After model configuration, scientists then launch the computing task. Next, data is atomized and distributed to computing engines that are running on citizen participants' computers. Scientists will receive notifications on the completion of computing tasks, and examine modeling results via visualization modules of the portal. Computing tasks, computing resources, and participants are managed by project managers via portal tools. A portal prototype has been built for proof of concept. Three forums have been setup for different groups of users to share information on science aspect, technology aspect, and educational outreach aspect. A facebook account has been setup to distribute messages via the most popular social networking platform. New treads are synchronized from the forums to facebook. A mapping tool displays geographic locations of the participants and the status of tasks on each client node. A group of users have been invited to test functions such as forums, blogs, and computing resource monitoring.

Invisible transportation infrastructure technology to mitigate energy and environment.

PubMed

Hossain, Md Faruque

2017-01-01

Traditional transportation infrastructure built by heat trapping products and the transportation vehiles run by fossil fuel, both causing deadly climate change. Thus, a new technology of invisible Flying Transportation system has been proposed to mitigate energy and environmental crisis caused by traditional infrastructure system. Underground Maglev system has been modeled to be constructed for all transportation systems to run the vehicle smoothly just over two feet over the earth surface by propulsive and impulsive force at flying stage. A wind energy modeling has also been added to meet the vehicle's energy demand when it runs on a non-maglev area. Naturally, all maglev infrastructures network to be covered by evergreen herb except pedestrian walkways to absorb CO 2 , ambient heat, and moisture (vapor) from the surrounding environment to make it cool. The research revealed that the vehicle will not require any energy since it will run by superconducting electromagnetic force while it runs on a maglev infrastructure area and directed by wind energy while it runs on non-maglev area. The proposed maglev transportation infrastructure technology will indeed be an innovative discovery in modern engineering science which will reduce fossil fuel energy consumption and climate change dramatically.

Model confirmation in climate economics

PubMed Central

Millner, Antony; McDermott, Thomas K. J.

2016-01-01

Benefit–cost integrated assessment models (BC-IAMs) inform climate policy debates by quantifying the trade-offs between alternative greenhouse gas abatement options. They achieve this by coupling simplified models of the climate system to models of the global economy and the costs and benefits of climate policy. Although these models have provided valuable qualitative insights into the sensitivity of policy trade-offs to different ethical and empirical assumptions, they are increasingly being used to inform the selection of policies in the real world. To the extent that BC-IAMs are used as inputs to policy selection, our confidence in their quantitative outputs must depend on the empirical validity of their modeling assumptions. We have a degree of confidence in climate models both because they have been tested on historical data in hindcasting experiments and because the physical principles they are based on have been empirically confirmed in closely related applications. By contrast, the economic components of BC-IAMs often rely on untestable scenarios, or on structural models that are comparatively untested on relevant time scales. Where possible, an approach to model confirmation similar to that used in climate science could help to build confidence in the economic components of BC-IAMs, or focus attention on which components might need refinement for policy applications. We illustrate the potential benefits of model confirmation exercises by performing a long-run hindcasting experiment with one of the leading BC-IAMs. We show that its model of long-run economic growth—one of its most important economic components—had questionable predictive power over the 20th century. PMID:27432964

Impact of Spatial Scales on the Intercomparison of Climate Scenarios

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

Luo, Wei; Steptoe, Michael; Chang, Zheng

2017-01-01

Scenario analysis has been widely applied in climate science to understand the impact of climate change on the future human environment, but intercomparison and similarity analysis of different climate scenarios based on multiple simulation runs remain challenging. Although spatial heterogeneity plays a key role in modeling climate and human systems, little research has been performed to understand the impact of spatial variations and scales on similarity analysis of climate scenarios. To address this issue, the authors developed a geovisual analytics framework that lets users perform similarity analysis of climate scenarios from the Global Change Assessment Model (GCAM) using a hierarchicalmore » clustering approach.« less

Assessing Disparities of Dengue Virus Transmission Risk across the US-Mexican Border Using a Climate Driven Vector-Epidemiological Model

NASA Technical Reports Server (NTRS)

Morin, Cory; Monaghan, Andrew; Quattrochi, Dale; Crosson, William; Hayden, Mary; Ernst, Kacey

2015-01-01

Dengue fever is a mosquito-borne viral disease reemerging throughout much of the tropical Americas. Dengue virus transmission is explicitly influenced by climate and the environment through its primary vector, Aedes aegypti. Temperature regulates Ae. aegypti development, survival, and replication rates as well as the incubation period of the virus within the mosquito. Precipitation provides water for many of the preferred breeding habitats of the mosquito, including buckets, old tires, and other places water can collect. Although transmission regularly occurs along the border region in Mexico, dengue virus transmission in bordering Arizona has not occurred. Using NASA's TRMM (Tropical Rainfall Measuring Mission) satellite for precipitation input and Daymet for temperature and supplemental precipitation input, we modeled dengue transmission along a US-Mexico transect using a dynamic dengue transmission model that includes interacting vector ecology and epidemiological components. Model runs were performed for 5 cities in Sonora, Mexico and southern Arizona. Employing a Monte Carlo approach, we performed ensembles of several thousands of model simulations in order to resolve the model uncertainty arising from using different combinations of parameter values that are not well known. For cities with reported dengue case data, the top model simulations that best reproduced dengue case numbers were retained and their parameter values were extracted for comparison. These parameter values were used to run simulations in areas where dengue virus transmission does not occur or where dengue fever case data was unavailable. Additional model runs were performed to reveal how changes in climate or parameter values could alter transmission risk along the transect. The relative influence of climate variability and model parameters on dengue virus transmission is assessed to help public health workers prepare location specific infection prevention strategies.

Challenges in the development of very high resolution Earth System Models for climate science

NASA Astrophysics Data System (ADS)

Rasch, Philip J.; Xie, Shaocheng; Ma, Po-Lun; Lin, Wuyin; Wan, Hui; Qian, Yun

2017-04-01

The authors represent the 20+ members of the ACME atmosphere development team. The US Department of Energy (DOE) has, like many other organizations around the world, identified the need for an Earth System Model capable of rapid completion of decade to century length simulations at very high (vertical and horizontal) resolution with good climate fidelity. Two years ago DOE initiated a multi-institution effort called ACME (Accelerated Climate Modeling for Energy) to meet this an extraordinary challenge, targeting a model eventually capable of running at 10-25km horizontal and 20-400m vertical resolution through the troposphere on exascale computational platforms at speeds sufficient to complete 5+ simulated years per day. I will outline the challenges our team has encountered in development of the atmosphere component of this model, and the strategies we have been using for tuning and debugging a model that we can barely afford to run on today's computational platforms. These strategies include: 1) evaluation at lower resolutions; 2) ensembles of short simulations to explore parameter space, and perform rough tuning and evaluation; 3) use of regionally refined versions of the model for probing high resolution model behavior at less expense; 4) use of "auto-tuning" methodologies for model tuning; and 5) brute force long climate simulations.

The health effects of climate change: a survey of recent quantitative research.

PubMed

Grasso, Margherita; Manera, Matteo; Chiabai, Aline; Markandya, Anil

2012-05-01

In recent years there has been a large scientific and public debate on climate change and its direct as well as indirect effects on human health. In particular, a large amount of research on the effects of climate changes on human health has addressed two fundamental questions. First, can historical data be of some help in revealing how short-run or long-run climate variations affect the occurrence of infectious diseases? Second, is it possible to build more accurate quantitative models which are capable of predicting the future effects of different climate conditions on the transmissibility of particularly dangerous infectious diseases? The primary goal of this paper is to review the most relevant contributions which have directly tackled those questions, both with respect to the effects of climate changes on the diffusion of non-infectious and infectious diseases, with malaria as a case study. Specific attention will be drawn on the methodological aspects of each study, which will be classified according to the type of quantitative model considered, namely time series models, panel data and spatial models, and non-statistical approaches. Since many different disciplines and approaches are involved, a broader view is necessary in order to provide a better understanding of the interactions between climate and health. In this respect, our paper also presents a critical summary of the recent literature related to more general aspects of the impacts of climate changes on human health, such as: the economics of climate change; how to manage the health effects of climate change; the establishment of Early Warning Systems for infectious diseases.

The Health Effects of Climate Change: A Survey of Recent Quantitative Research

PubMed Central

Grasso, Margherita; Manera, Matteo; Chiabai, Aline; Markandya, Anil

2012-01-01

In recent years there has been a large scientific and public debate on climate change and its direct as well as indirect effects on human health. In particular, a large amount of research on the effects of climate changes on human health has addressed two fundamental questions. First, can historical data be of some help in revealing how short-run or long-run climate variations affect the occurrence of infectious diseases? Second, is it possible to build more accurate quantitative models which are capable of predicting the future effects of different climate conditions on the transmissibility of particularly dangerous infectious diseases? The primary goal of this paper is to review the most relevant contributions which have directly tackled those questions, both with respect to the effects of climate changes on the diffusion of non-infectious and infectious diseases, with malaria as a case study. Specific attention will be drawn on the methodological aspects of each study, which will be classified according to the type of quantitative model considered, namely time series models, panel data and spatial models, and non-statistical approaches. Since many different disciplines and approaches are involved, a broader view is necessary in order to provide a better understanding of the interactions between climate and health. In this respect, our paper also presents a critical summary of the recent literature related to more general aspects of the impacts of climate changes on human health, such as: the economics of climate change; how to manage the health effects of climate change; the establishment of Early Warning Systems for infectious diseases. PMID:22754455

Test of High-resolution Global and Regional Climate Model Projections

NASA Astrophysics Data System (ADS)

Stenchikov, Georgiy; Nikulin, Grigory; Hansson, Ulf; Kjellström, Erik; Raj, Jerry; Bangalath, Hamza; Osipov, Sergey

2014-05-01

In scope of CORDEX project we have simulated the past (1975-2005) and future (2006-2050) climates using the GFDL global high-resolution atmospheric model (HIRAM) and the Rossby Center nested regional model RCA4 for the Middle East and North Africa (MENA) region. Both global and nested runs were performed with roughly the same spatial resolution of 25 km in latitude and longitude, and were driven by the 2°x2.5°-resolution fields from GFDL ESM2M IPCC AR5 runs. The global HIRAM simulations could naturally account for interaction of regional processes with the larger-scale circulation features like Indian Summer Monsoon, which is lacking from regional model setup. Therefore in this study we specifically address the consistency of "global" and "regional" downscalings. The performance of RCA4, HIRAM, and ESM2M is tested based on mean, extreme, trends, seasonal and inter-annual variability of surface temperature, precipitation, and winds. The impact of climate change on dust storm activity, extreme precipitation and water resources is specifically addressed. We found that the global and regional climate projections appear to be quite consistent for the modeled period and differ more significantly from ESM2M than between each other.

Facilitating a stakeholder-led approach to the development of Mediterranean climate services: co-ordinating the CLIM-RUN case studies

NASA Astrophysics Data System (ADS)

Goodess, C. M.

2012-04-01

The CLIM-RUN case studies provide a real-world context for bringing together experts on the demand and supply side of climate services. They are essential to the CLIM-RUN objective of using iterative and bottom-up (i.e., stakeholder led) approaches for optimizing the two-way information transfer between climate experts and stakeholders. The region of interest for CLIM-RUN is the Mediterranean, which is a recognised climate change hotspot (i.e., a region particularly sensitive and vulnerable to global warming) and which does not currently have developed climate service networks such as exist in a number of Central and Northern European countries. The case studies focus on the energy and tourism sectors, but also include a cross-cutting study on wild fires (an issue of increasing concern in the Mediterranean) as well as a cross-sectorial integrated case study for the Venice lagoon. They span coastal (e.g., Tunisia and Croatia), island (e.g., Cyprus) and mountain (e.g., Savoie) environments, the eastern (e.g., Greece) to western (e.g., Spain, Morocco) Mediterranean regions, and regional to local foci. Stakeholder involvement has been critical from the start of the project in March 2011, with a series of targeted workshops helping to define the framework for each case study. Two specific workshop objectives were to (i) better understand who are the climate services stakeholders and (ii) what they need/want from climate services (both in terms of data products and broader knowledge). Many of the workshops were held in local languages to maximise stakeholder participation, with expert knowledge provided by the CLIM-RUN climate and stakeholder expert teams (the CET and SET). Following the workshops, CET members are 'translating' the user needs into specific requirements from climate observations and models and identifying areas where additional modelling and analysis are required. As part of the central co-ordination of the case studies, a perception and data needs questionnaire was produced to solicit information about stakeholder institutions and organisations, risk perception and current use of climate/weather information, perspectives on climate services, data requirements and handling uncertainties. The questionnaire was designed to be used in a very flexible way, adapted to individual case studies. It has been circulated via email, during and after workshops, made available in on-line form and has also provided the basis for structured interviews with stakeholders. From the preliminary CLIM-RUN work, it is evident that the different sectorial requirements and contexts, including differences in stakeholder expertise and perspectives and the importance of non-climatic considerations in decision making, support the tailored, bottom-up approach adopted. For instance, the energy sector is more keen to use detailed present-day climate information, while tourist stakeholders, although less constrained by climate issues, prefer seasonal timescale information. At the same time, these differences provide a challenge in terms of developing common methodologies and identifying priorities for the provision of climate services. Other challenges relate to the differences in stakeholder engagement across the case studies.

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.

Weather extremes in very large, high-resolution ensembles: the weatherathome experiment

NASA Astrophysics Data System (ADS)

Allen, M. R.; Rosier, S.; Massey, N.; Rye, C.; Bowery, A.; Miller, J.; Otto, F.; Jones, R.; Wilson, S.; Mote, P.; Stone, D. A.; Yamazaki, Y. H.; Carrington, D.

2011-12-01

Resolution and ensemble size are often seen as alternatives in climate modelling. Models with sufficient resolution to simulate many classes of extreme weather cannot normally be run often enough to assess the statistics of rare events, still less how these statistics may be changing. As a result, assessments of the impact of external forcing on regional climate extremes must be based either on statistical downscaling from relatively coarse-resolution models, or statistical extrapolation from 10-year to 100-year events. Under the weatherathome experiment, part of the climateprediction.net initiative, we have compiled the Met Office Regional Climate Model HadRM3P to run on personal computer volunteered by the general public at 25 and 50km resolution, embedded within the HadAM3P global atmosphere model. With a global network of about 50,000 volunteers, this allows us to run time-slice ensembles of essentially unlimited size, exploring the statistics of extreme weather under a range of scenarios for surface forcing and atmospheric composition, allowing for uncertainty in both boundary conditions and model parameters. Current experiments, developed with the support of Microsoft Research, focus on three regions, the Western USA, Europe and Southern Africa. We initially simulate the period 1959-2010 to establish which variables are realistically simulated by the model and on what scales. Our next experiments are focussing on the Event Attribution problem, exploring how the probability of various types of extreme weather would have been different over the recent past in a world unaffected by human influence, following the design of Pall et al (2011), but extended to a longer period and higher spatial resolution. We will present the first results of the unique, global, participatory experiment and discuss the implications for the attribution of recent weather events to anthropogenic influence on climate.

On the use of tower-flux measurements to assess the performance of global ecosystem models

NASA Astrophysics Data System (ADS)

El Maayar, M.; Kucharik, C.

2003-04-01

Global ecosystem models are important tools for the study of biospheric processes and their responses to environmental changes. Such models typically translate knowledge, gained from local observations, into estimates of regional or even global outcomes of ecosystem processes. A typical test of ecosystem models consists of comparing their output against tower-flux measurements of land surface-atmosphere exchange of heat and mass. To perform such tests, models are typically run using detailed information on soil properties (texture, carbon content,...) and vegetation structure observed at the experimental site (e.g., vegetation height, vegetation phenology, leaf photosynthetic characteristics,...). In global simulations, however, earth's vegetation is typically represented by a limited number of plant functional types (PFT; group of plant species that have similar physiological and ecological characteristics). For each PFT (e.g., temperate broadleaf trees, boreal conifer evergreen trees,...), which can cover a very large area, a set of typical physiological and physical parameters are assigned. Thus, a legitimate question arises: How does the performance of a global ecosystem model run using detailed site-specific parameters compare with the performance of a less detailed global version where generic parameters are attributed to a group of vegetation species forming a PFT? To answer this question, we used a multiyear dataset, measured at two forest sites with contrasting environments, to compare seasonal and interannual variability of surface-atmosphere exchange of water and carbon predicted by the Integrated BIosphere Simulator-Dynamic Global Vegetation Model. Two types of simulations were, thus, performed: a) Detailed runs: observed vegetation characteristics (leaf area index, vegetation height,...) and soil carbon content, in addition to climate and soil type, are specified for model run; and b) Generic runs: when only observed climates and soil types at the measurement sites are used to run the model. The generic runs were performed for the number of years equal to the current age of the forests, initialized with no vegetation and a soil carbon density equal to zero.

How are interannual modes of variability IOD, ENSO, SAM, AMO excited by natural and anthropogenic forcing?

NASA Astrophysics Data System (ADS)

Maher, Nicola; Marotzke, Jochem

2017-04-01

Natural climate variability is found in observations, paleo-proxies, and climate models. Such climate variability can be intrinsic internal variability or externally forced, for example by changes in greenhouse gases or large volcanic eruptions. There are still questions concerning how external forcing, both natural (e.g., volcanic eruptions and solar variability) and anthropogenic (e.g., greenhouse gases and ozone) may excite both interannual modes of variability in the climate system. This project aims to address some of these problems, utilising the large ensemble of the MPI-ESM-LR climate model. In this study we investigate the statistics of four modes of interannual variability, namely the North Atlantic Oscillation (NAO), the Indian Ocean Dipole (IOD), the Southern Annular Mode (SAM) and the El Niño Southern Oscillation (ENSO). Using the 100-member ensemble of MPI-ESM-LR the statistical properties of these modes (amplitude and standard deviation) can be assessed over time. Here we compare the properties in the pre-industrial control run, historical run and future scenarios (RCP4.5, RCP2.6) and present preliminary results.

Long-Term Climatic and Anthropogenic Impacts on Streamwater Salinity in New York State: INCA Simulations Offer Cautious Optimism.

PubMed

Gutchess, Kristina; Jin, Li; Ledesma, José L J; Crossman, Jill; Kelleher, Christa; Lautz, Laura; Lu, Zunli

2018-02-06

The long-term application of road salts has led to a rise in surface water chloride (Cl - ) concentrations. While models have been used to assess the potential future impacts of continued deicing practices, prior approaches have not incorporated changes in climate that are projected to impact hydrogeology in the 21st century. We use an INtegrated CAtchment (INCA) model to simulate Cl - concentrations in the Tioughnioga River watershed. The model was run over a baseline period (1961-1990) and climate simulations from a range of GCMs run over three 30-year intervals (2010-2039; 2040-2069; 2070-2099). Model projections suggest that Cl - concentrations in the two river branches will continue to rise for several decades, before beginning to decline around 2040-2069, with all GCM scenarios indicating reductions in snowfall and associated salt applications over the 21st century. The delay in stream response is most likely attributed to climate change and continued contribution of Cl - from aquifers. By 2100, surface water Cl - concentrations will decrease to below 1960s values. Catchments dominated by urban lands will experience a decrease in average surface water Cl - , although moderate compared to more rural catchments.

Transient climate simulations of the deglaciation 21-9 thousand years before present (version 1) - PMIP4 Core experiment design and boundary conditions

NASA Astrophysics Data System (ADS)

Ivanovic, Ruza F.; Gregoire, Lauren J.; Kageyama, Masa; Roche, Didier M.; Valdes, Paul J.; Burke, Andrea; Drummond, Rosemarie; Peltier, W. Richard; Tarasov, Lev

2016-07-01

The last deglaciation, which marked the transition between the last glacial and present interglacial periods, was punctuated by a series of rapid (centennial and decadal) climate changes. Numerical climate models are useful for investigating mechanisms that underpin the climate change events, especially now that some of the complex models can be run for multiple millennia. We have set up a Paleoclimate Modelling Intercomparison Project (PMIP) working group to coordinate efforts to run transient simulations of the last deglaciation, and to facilitate the dissemination of expertise between modellers and those engaged with reconstructing the climate of the last 21 000 years. Here, we present the design of a coordinated Core experiment over the period 21-9 thousand years before present (ka) with time-varying orbital forcing, greenhouse gases, ice sheets and other geographical changes. A choice of two ice sheet reconstructions is given, and we make recommendations for prescribing ice meltwater (or not) in the Core experiment. Additional focussed simulations will also be coordinated on an ad hoc basis by the working group, for example to investigate more thoroughly the effect of ice meltwater on climate system evolution, and to examine the uncertainty in other forcings. Some of these focussed simulations will target shorter durations around specific events in order to understand them in more detail and allow for the more computationally expensive models to take part.

A personal perspective on modelling the climate system

PubMed Central

Palmer, T. N.

2016-01-01

Given their increasing relevance for society, I suggest that the climate science community itself does not treat the development of error-free ab initio models of the climate system with sufficient urgency. With increasing levels of difficulty, I discuss a number of proposals for speeding up such development. Firstly, I believe that climate science should make better use of the pool of post-PhD talent in mathematics and physics, for developing next-generation climate models. Secondly, I believe there is more scope for the development of modelling systems which link weather and climate prediction more seamlessly. Finally, here in Europe, I call for a new European Programme on Extreme Computing and Climate to advance our ability to simulate climate extremes, and understand the drivers of such extremes. A key goal for such a programme is the development of a 1 km global climate system model to run on the first exascale supercomputers in the early 2020s. PMID:27274686

Modelling past hydrology of an interfluve area in the Campine region (NE Belgium)

NASA Astrophysics Data System (ADS)

Leterme, Bertrand; Beerten, Koen; Gedeon, Matej; Vandersteen, Katrijn

2015-04-01

This study aims at hydrological model verification of a small lowland interfluve area (18.6 km²) in NE Belgium, for conditions that are different than today. We compare the current state with five reference periods in the past (AD 1500, 1770, 1854, 1909 and 1961) representing important stages of landscape evolution in the study area. Historical information and proxy data are used to derive conceptual model features and boundary conditions specific to each period: topography, surface water geometry (canal, drains and lakes), land use, soils, vegetation and climate. The influence of landscape evolution on the hydrological cycle is assessed using numerical simulations of a coupled unsaturated zone - groundwater model (HYDRUS-MODFLOW). The induced hydrological changes are assessed in terms of groundwater level, recharge, evapotranspiration, and surface water discharge. HYDRUS-MODFLOW coupling allows including important processes such as the groundwater contribution to evapotranspiration. Major land use change occurred between AD 1854 and 1909, with about 41% of the study area being converted from heath to coniferous forest, together with the development of a drainage network. Results show that this led to a significant decrease of groundwater recharge and lowering of the groundwater table. A limitation of the study lies in the comparison of simulated past hydrology with appropriate palaeo-records. Examples are given as how some indicators (groundwater head, swamp zones) can be used to tend to model validation. Quantifying the relative impact of land use and climate changes requires running sensitivity simulations where the models using alternative land use are run with the climate forcing of other periods. A few examples of such sensitivity runs are presented in order to compare the influence of land use and climate change on the study area hydrology.

An effective online data monitoring and saving strategy for large-scale climate simulations

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

Xian, Xiaochen; Archibald, Rick; Mayer, Benjamin

Large-scale climate simulation models have been developed and widely used to generate historical data and study future climate scenarios. These simulation models often have to run for a couple of months to understand the changes in the global climate over the course of decades. This long-duration simulation process creates a huge amount of data with both high temporal and spatial resolution information; however, how to effectively monitor and record the climate changes based on these large-scale simulation results that are continuously produced in real time still remains to be resolved. Due to the slow process of writing data to disk,more » the current practice is to save a snapshot of the simulation results at a constant, slow rate although the data generation process runs at a very high speed. This study proposes an effective online data monitoring and saving strategy over the temporal and spatial domains with the consideration of practical storage and memory capacity constraints. Finally, our proposed method is able to intelligently select and record the most informative extreme values in the raw data generated from real-time simulations in the context of better monitoring climate changes.« less

An effective online data monitoring and saving strategy for large-scale climate simulations

DOE PAGES

Xian, Xiaochen; Archibald, Rick; Mayer, Benjamin; ...

2018-01-22

Large-scale climate simulation models have been developed and widely used to generate historical data and study future climate scenarios. These simulation models often have to run for a couple of months to understand the changes in the global climate over the course of decades. This long-duration simulation process creates a huge amount of data with both high temporal and spatial resolution information; however, how to effectively monitor and record the climate changes based on these large-scale simulation results that are continuously produced in real time still remains to be resolved. Due to the slow process of writing data to disk,more » the current practice is to save a snapshot of the simulation results at a constant, slow rate although the data generation process runs at a very high speed. This study proposes an effective online data monitoring and saving strategy over the temporal and spatial domains with the consideration of practical storage and memory capacity constraints. Finally, our proposed method is able to intelligently select and record the most informative extreme values in the raw data generated from real-time simulations in the context of better monitoring climate changes.« less

Regional sea level variability in a high-resolution global coupled climate model

NASA Astrophysics Data System (ADS)

Palko, D.; Kirtman, B. P.

2016-12-01

The prediction of trends at regional scales is essential in order to adapt to and prepare for the effects of climate change. However, GCMs are unable to make reliable predictions at regional scales. The prediction of local sea level trends is particularly critical. The main goal of this research is to utilize high-resolution (HR) (0.1° resolution in the ocean) coupled model runs of CCSM4 to analyze regional sea surface height (SSH) trends. Unlike typical, lower resolution (1.0°) GCM runs these HR runs resolve features in the ocean, like the Gulf Stream, which may have a large effect on regional sea level. We characterize the variability of regional SSH along the Atlantic coast of the US using tide gauge observations along with fixed radiative forcing runs of CCSM4 and HR interactive ensemble runs. The interactive ensemble couples an ensemble mean atmosphere with a single ocean realization. This coupling results in a 30% decrease in the strength of the Atlantic meridional overturning circulation; therefore, the HR interactive ensemble is analogous to a HR hosing experiment. By characterizing the variability in these high-resolution GCM runs and observations we seek to understand what processes influence coastal SSH along the Eastern Coast of the United States and better predict future SLR.

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.

Projected atoll shoreline and run-up changes in response to sea-level rise and varying large wave conditions at Wake and Midway Atolls, Northwestern Hawaiian Islands

USGS Publications Warehouse

Shope, James B.; Storlazzi, Curt; Hoeke, Ron

2017-01-01

Atoll islands are dynamic features that respond to seasonal alterations in wave conditions and sea level. It is unclear how shoreline wave run-up and erosion patterns along these low elevation islands will respond to projected sea-level rise (SLR) and changes in wave climate over the next century, hindering communities' preparation for the future. To elucidate how these processes may respond to climate change, extreme boreal winter and summer wave conditions under future sea-level rise (SLR) and wave climate scenarios were simulated at two atolls, Wake and Midway, using a shallow-water hydrodynamic model. Nearshore wave conditions were used to compute the potential longshore sediment flux along island shorelines via the CERC empirical formula and wave-driven erosion was calculated as the divergence of the longshore drift; run-up and the locations where the run-up exceed the berm elevation were also determined. SLR is projected to predominantly drive future island morphological change and flooding. Seaward shorelines (i.e., ocean fronted shorelines directly facing incident wave energy) were projected to experience greater erosion and flooding with SLR and in hypothetical scenarios where changes to deep water wave directions were altered, as informed by previous climate change forced Pacific wave modeling efforts. These changes caused nearshore waves to become more shore-normal, increasing wave attack along previously protected shorelines. With SLR, leeward shorelines (i.e., an ocean facing shoreline but sheltered from incident wave energy) became more accretive on windward islands and marginally more erosive along leeward islands. These shorelines became more accretionary and subject to more flooding with nearshore waves becoming more shore-normal. Lagoon shorelines demonstrated the greatest SLR-driven increase in erosion and run-up. They exhibited the greatest relative change with increasing wave heights where both erosion and run-up magnitudes increased. Wider reef flat-fronted seaward shorelines became more accretive as all oceanographic forcing parameters increased in magnitude and exhibited large run-up increases following increasing wave heights. Island end shorelines became subject to increased flooding, erosion at Wake, and accretion at Midway with SLR. Under future conditions, windward and leeward islands are projected to become thinner as ocean facing and lagoonal shorelines erode, with leeward islands becoming more elongate. Island shorelines will change dramatically over the next century as SLR and altered wave climates drive new erosional regimes. It is vital to the sustainability of island communities that the relative magnitudes of these effects are addressed when planning for projected future climates.

Projected atoll shoreline and run-up changes in response to sea-level rise and varying large wave conditions at Wake and Midway Atolls, Northwestern Hawaiian Islands

NASA Astrophysics Data System (ADS)

Shope, James B.; Storlazzi, Curt D.; Hoeke, Ron K.

2017-10-01

Atoll islands are dynamic features that respond to seasonal alterations in wave conditions and sea level. It is unclear how shoreline wave run-up and erosion patterns along these low elevation islands will respond to projected sea-level rise (SLR) and changes in wave climate over the next century, hindering communities' preparation for the future. To elucidate how these processes may respond to climate change, extreme boreal winter and summer wave conditions under future sea-level rise (SLR) and wave climate scenarios were simulated at two atolls, Wake and Midway, using a shallow-water hydrodynamic model. Nearshore wave conditions were used to compute the potential longshore sediment flux along island shorelines via the CERC empirical formula and wave-driven erosion was calculated as the divergence of the longshore drift; run-up and the locations where the run-up exceed the berm elevation were also determined. SLR is projected to predominantly drive future island morphological change and flooding. Seaward shorelines (i.e., ocean fronted shorelines directly facing incident wave energy) were projected to experience greater erosion and flooding with SLR and in hypothetical scenarios where changes to deep water wave directions were altered, as informed by previous climate change forced Pacific wave modeling efforts. These changes caused nearshore waves to become more shore-normal, increasing wave attack along previously protected shorelines. With SLR, leeward shorelines (i.e., an ocean facing shoreline but sheltered from incident wave energy) became more accretive on windward islands and marginally more erosive along leeward islands. These shorelines became more accretionary and subject to more flooding with nearshore waves becoming more shore-normal. Lagoon shorelines demonstrated the greatest SLR-driven increase in erosion and run-up. They exhibited the greatest relative change with increasing wave heights where both erosion and run-up magnitudes increased. Wider reef flat-fronted seaward shorelines became more accretive as all oceanographic forcing parameters increased in magnitude and exhibited large run-up increases following increasing wave heights. Island end shorelines became subject to increased flooding, erosion at Wake, and accretion at Midway with SLR. Under future conditions, windward and leeward islands are projected to become thinner as ocean facing and lagoonal shorelines erode, with leeward islands becoming more elongate. Island shorelines will change dramatically over the next century as SLR and altered wave climates drive new erosional regimes. It is vital to the sustainability of island communities that the relative magnitudes of these effects are addressed when planning for projected future climates.

A Python Implementation of an Intermediate-Level Tropical Circulation Model and Implications for How Modeling Science is Done

NASA Astrophysics Data System (ADS)

Lin, J. W. B.

2015-12-01

Historically, climate models have been developed incrementally and in compiled languages like Fortran. While the use of legacy compiledlanguages results in fast, time-tested code, the resulting model is limited in its modularity and cannot take advantage of functionalityavailable with modern computer languages. Here we describe an effort at using the open-source, object-oriented language Pythonto create more flexible climate models: the package qtcm, a Python implementation of the intermediate-level Neelin-Zeng Quasi-Equilibrium Tropical Circulation model (QTCM1) of the atmosphere. The qtcm package retains the core numerics of QTCM1, written in Fortran, to optimize model performance but uses Python structures and utilities to wrap the QTCM1 Fortran routines and manage model execution. The resulting "mixed language" modeling package allows order and choice of subroutine execution to be altered at run time, and model analysis and visualization to be integrated in interactively with model execution at run time. This flexibility facilitates more complex scientific analysis using less complex code than would be possible using traditional languages alone and provides tools to transform the traditional "formulate hypothesis → write and test code → run model → analyze results" sequence into a feedback loop that can be executed automatically by the computer.

The effects of atmospheric cloud radiative forcing on climate

NASA Technical Reports Server (NTRS)

Randall, David A.

1989-01-01

In order to isolate the effects of atmospheric cloud radiative forcing (ACRF) on climate, the general circulation of an ocean-covered earth called 'Seaworld' was simulated using the Colorado State University GCM. Most current climate models, however, do not include an interactive ocean. The key simplifications in 'Seaworld' are the fixed boundary temperature with no land points, the lack of mountains and the zonal uniformity of the boundary conditions. Two 90-day 'perpetual July' simulations were performed and analyzed the last sixty days of each. The first run included all the model's physical parameterizations, while the second omitted the effects of clouds in both the solar and terrestrial radiation parameterizations. Fixed and identical boundary temperatures were set for the two runs, and resulted in differences revealing the direct and indirect effects of the ACRF on the large-scale circulation and the parameterized hydrologic processes.

GEM-AC, a stratospheric-tropospheric global and regional model for air quality and climate change: evaluation of gas phase properties

NASA Astrophysics Data System (ADS)

Kaminski, J. W.; Semeniuk, K.; McConnell, J. C.; Lupu, A.; Mamun, A.

2012-12-01

The Global Environmental Multiscale model for Air Quality and climate change (GEM-AC) is a global general circulation model based on the GEM model developed by the Meteorological Service of Canada for operational weather forecasting. It can be run with a global uniform (GU) grid or a global variable (GV) grid where the core has uniform grid spacing and the exterior grid expands. With a GV grid high resolution regional runs can be accomplished without a concern for boundary conditions. The work described here uses GEM version 3.3.2. The gas-phase chemistry consists in detailed reactions of Ox, NOx, HOx, CO, CH4, NMVOCs, halocarbons, ClOx and BrO. We have recently added elements of the Global Modal-aerosol eXtension (GMXe) scheme to address aerosol microphysics and gas-aerosol partitioning. The evaluation of the MESSY GMXe aerosol scheme is addressed in another poster. The Canadian aerosol module (CAM) is also available. Tracers are advected using the semi-Lagrangian scheme native to GEM. The vertical transport includes parameterized subgrid scale turbulence and large scale convection. Dry deposition is implemented as a flux boundary condition in the vertical diffusion equation. For climate runs the GHGs CO2, CH4, N2O, CFCs in the radiation scheme are adjusted to the scenario considered. In GV regional mode at high resolutions a lake model, FLAKE is also included. Wet removal comprises both in-cloud and below-cloud scavenging. With the gas phase chemistry the model has been run for a series of ten year time slices on a 3°×3° global grid with 77 hybrid levels from the surface to 0.15 hPa. The tropospheric and stratospheric gas phase results are compared with satellite measurements including, ACE, MIPAS, MOPITT, and OSIRIS. Current evaluations of the ozone field and other stratospheric fields are encouraging and tropospheric lifetimes for CH4 and CH3CCl3 are in reasonable accord with tropospheric models. We will present results for current and future climate conditions forced by SST for 2050.

qtcm 0.1.2: A Python Implementation of the Neelin-Zeng Quasi-Equilibrium Tropical Circulation model

NASA Astrophysics Data System (ADS)

Lin, J. W.-B.

2008-10-01

Historically, climate models have been developed incrementally and in compiled languages like Fortran. While the use of legacy compiled languages results in fast, time-tested code, the resulting model is limited in its modularity and cannot take advantage of functionality available with modern computer languages. Here we describe an effort at using the open-source, object-oriented language Python to create more flexible climate models: the package qtcm, a Python implementation of the intermediate-level Neelin-Zeng Quasi-Equilibrium Tropical Circulation model (QTCM1) of the atmosphere. The qtcm package retains the core numerics of QTCM1, written in Fortran to optimize model performance, but uses Python structures and utilities to wrap the QTCM1 Fortran routines and manage model execution. The resulting "mixed language" modeling package allows order and choice of subroutine execution to be altered at run time, and model analysis and visualization to be integrated in interactively with model execution at run time. This flexibility facilitates more complex scientific analysis using less complex code than would be possible using traditional languages alone, and provides tools to transform the traditional "formulate hypothesis → write and test code → run model → analyze results" sequence into a feedback loop that can be executed automatically by the computer.

qtcm 0.1.2: a Python implementation of the Neelin-Zeng Quasi-Equilibrium Tropical Circulation Model

NASA Astrophysics Data System (ADS)

Lin, J. W.-B.

2009-02-01

Historically, climate models have been developed incrementally and in compiled languages like Fortran. While the use of legacy compiled languages results in fast, time-tested code, the resulting model is limited in its modularity and cannot take advantage of functionality available with modern computer languages. Here we describe an effort at using the open-source, object-oriented language Python to create more flexible climate models: the package qtcm, a Python implementation of the intermediate-level Neelin-Zeng Quasi-Equilibrium Tropical Circulation model (QTCM1) of the atmosphere. The qtcm package retains the core numerics of QTCM1, written in Fortran to optimize model performance, but uses Python structures and utilities to wrap the QTCM1 Fortran routines and manage model execution. The resulting "mixed language" modeling package allows order and choice of subroutine execution to be altered at run time, and model analysis and visualization to be integrated in interactively with model execution at run time. This flexibility facilitates more complex scientific analysis using less complex code than would be possible using traditional languages alone, and provides tools to transform the traditional "formulate hypothesis → write and test code → run model → analyze results" sequence into a feedback loop that can be executed automatically by the computer.

Soil Moisture and Snow Cover: Active or Passive Elements of Climate

NASA Technical Reports Server (NTRS)

Oglesby, Robert J.; Marshall, Susan; Erickson, David J., III; Robertson, Franklin R.; Roads, John O.; Arnold, James E. (Technical Monitor)

2002-01-01

A key question is the extent to which surface effects such as soil moisture and snow cover are simply passive elements or whether they can affect the evolution of climate on seasonal and longer time scales. We have constructed ensembles of predictability studies using the NCAR CCM3 in which we compared the relative roles of initial surface and atmospheric conditions over the central and western U.S. in determining the subsequent evolution of soil moisture and of snow cover. Results from simulations with realistic soil moisture anomalies indicate that internal climate variability may be the strongest factor, with some indication that the initial atmospheric state is also important. Model runs with exaggerated soil moisture reductions (near-desert conditions) showed a much larger effect, with warmer surface temperatures, reduced precipitation, and lower surface pressures; the latter indicating a response of the atmospheric circulation. These results suggest the possibility of a threshold effect in soil moisture, whereby an anomaly must be of a sufficient size before it can have a significant impact on the atmospheric circulation and climate. Results from simulations with realistic snow cover anomalies indicate that the time of year can be crucial. When introduced in late winter, these anomalies strongly affected the subsequent evolution of snow cover. When introduced in early winter, however, little or no effect is seen on the subsequent snow cover. Runs with greatly exaggerated initial snow cover indicate that the high reflectivity of snow is the most important process by which snow cover can impact climate, through lower surface temperatures and increased surface pressures. The results to date were obtained for model runs with present-day conditions. We are currently analyzing runs made with projected forcings for the 21st century to see if these results are modified in any way under likely scenarios of future climate change. An intriguing new statistical technique involving 'clustering' is developed to assist in this analysis.

Improved Analysis of Earth System Models and Observations using Simple Climate Models

NASA Astrophysics Data System (ADS)

Nadiga, B. T.; Urban, N. M.

2016-12-01

Earth system models (ESM) are the most comprehensive tools we have to study climate change and develop climate projections. However, the computational infrastructure required and the cost incurred in running such ESMs precludes direct use of such models in conjunction with a wide variety of tools that can further our understanding of climate. Here we are referring to tools that range from dynamical systems tools that give insight into underlying flow structure and topology to tools that come from various applied mathematical and statistical techniques and are central to quantifying stability, sensitivity, uncertainty and predictability to machine learning tools that are now being rapidly developed or improved. Our approach to facilitate the use of such models is to analyze output of ESM experiments (cf. CMIP) using a range of simpler models that consider integral balances of important quantities such as mass and/or energy in a Bayesian framework.We highlight the use of this approach in the context of the uptake of heat by the world oceans in the ongoing global warming. Indeed, since in excess of 90% of the anomalous radiative forcing due greenhouse gas emissions is sequestered in the world oceans, the nature of ocean heat uptake crucially determines the surface warming that is realized (cf. climate sensitivity). Nevertheless, ESMs themselves are never run long enough to directly assess climate sensitivity. So, we consider a range of models based on integral balances--balances that have to be realized in all first-principles based models of the climate system including the most detailed state-of-the art climate simulations. The models range from simple models of energy balance to those that consider dynamically important ocean processes such as the conveyor-belt circulation (Meridional Overturning Circulation, MOC), North Atlantic Deep Water (NADW) formation, Antarctic Circumpolar Current (ACC) and eddy mixing. Results from Bayesian analysis of such models using both ESM experiments and actual observations are presented. One such result points to the importance of direct sequestration of heat below 700 m, a process that is not allowed for in the simple models that have been traditionally used to deduce climate sensitivity.

The influence of initial and surface boundary conditions on a model-generated January climatology

NASA Technical Reports Server (NTRS)

Wu, K. F.; Spar, J.

1981-01-01

The influence on a model-generated January climate of various surface boundary conditions, as well as initial conditions, was studied by using the GISS coarse-mesh climate model. Four experiments - two with water planets, one with flat continents, and one with mountains - were used to investigate the effects of initial conditions, and the thermal and dynamical effects of the surface on the model generated-climate. However, climatological mean zonal-symmetric sea surface temperature is used in all four runs over the model oceans. Moreover, zero ground wetness and uniform ground albedo except for snow are used in the last experiments.

The influence of ice sheets on temperature during the past 38 million years inferred from a one-dimensional ice sheet-climate model

NASA Astrophysics Data System (ADS)

Stap, Lennert B.; van de Wal, Roderik S. W.; de Boer, Bas; Bintanja, Richard; Lourens, Lucas J.

2017-09-01

Since the inception of the Antarctic ice sheet at the Eocene-Oligocene transition (˜ 34 Myr ago), land ice has played a crucial role in Earth's climate. Through feedbacks in the climate system, land ice variability modifies atmospheric temperature changes induced by orbital, topographical, and greenhouse gas variations. Quantification of these feedbacks on long timescales has hitherto scarcely been undertaken. In this study, we use a zonally averaged energy balance climate model bidirectionally coupled to a one-dimensional ice sheet model, capturing the ice-albedo and surface-height-temperature feedbacks. Potentially important transient changes in topographic boundary conditions by tectonics and erosion are not taken into account but are briefly discussed. The relative simplicity of the coupled model allows us to perform integrations over the past 38 Myr in a fully transient fashion using a benthic oxygen isotope record as forcing to inversely simulate CO2. Firstly, we find that the results of the simulations over the past 5 Myr are dependent on whether the model run is started at 5 or 38 Myr ago. This is because the relation between CO2 and temperature is subject to hysteresis. When the climate cools from very high CO2 levels, as in the longer transient 38 Myr run, temperatures in the lower CO2 range of the past 5 Myr are higher than when the climate is initialised at low temperatures. Consequently, the modelled CO2 concentrations depend on the initial state. Taking the realistic warm initialisation into account, we come to a best estimate of CO2, temperature, ice-volume-equivalent sea level, and benthic δ18O over the past 38 Myr. Secondly, we study the influence of ice sheets on the evolution of global temperature and polar amplification by comparing runs with ice sheet-climate interaction switched on and off. By passing only albedo or surface height changes to the climate model, we can distinguish the separate effects of the ice-albedo and surface-height-temperature feedbacks. We find that ice volume variability has a strong enhancing effect on atmospheric temperature changes, particularly in the regions where the ice sheets are located. As a result, polar amplification in the Northern Hemisphere decreases towards warmer climates as there is little land ice left to melt. Conversely, decay of the Antarctic ice sheet increases polar amplification in the Southern Hemisphere in the high-CO2 regime. Our results also show that in cooler climates than the pre-industrial, the ice-albedo feedback predominates the surface-height-temperature feedback, while in warmer climates they are more equal in strength.

Simulating Future Changes in Spatio-temporal Precipitation by Identifying and Characterizing Individual Rainstorm Events

NASA Astrophysics Data System (ADS)

Chang, W.; Stein, M.; Wang, J.; Kotamarthi, V. R.; Moyer, E. J.

2015-12-01

A growing body of literature suggests that human-induced climate change may cause significant changes in precipitation patterns, which could in turn influence future flood levels and frequencies and water supply and management practices. Although climate models produce full three-dimensional simulations of precipitation, analyses of model precipitation have focused either on time-averaged distributions or on individual timeseries with no spatial information. We describe here a new approach based on identifying and characterizing individual rainstorms in either data or model output. Our approach enables us to readily characterize important spatio-temporal aspects of rainstorms including initiation location, intensity (mean and patterns), spatial extent, duration, and trajectory. We apply this technique to high-resolution precipitation over the continental U.S. both from radar-based observations (NCEP Stage IV QPE product, 1-hourly, 4 km spatial resolution) and from model runs with dynamical downscaling (WRF regional climate model, 3-hourly, 12 km spatial resolution). In the model studies we investigate the changes in storm characteristics under a business-as-usual warming scenario to 2100 (RCP 8.5). We find that in these model runs, rainstorm intensity increases as expected with rising temperatures (approximately 7%/K, following increased atmospheric moisture content), while total precipitation increases by a lesser amount (3%/K), consistent with other studies. We identify for the first time the necessary compensating mechanism: in these model runs, individual precipitation events become smaller. Other aspects are approximately unchanged in the warmer climate. Because these spatio-temporal changes in rainfall patterns would impact regional hydrology, it is important that they be accurately incorporated into any impacts assessment. For this purpose we have developed a methodology for producing scenarios of future precipitation that combine observational data and model-projected changes. We statistically describe the future changes in rainstorm characteristics suggested by the WRF model and apply those changes to observational data. The resulting high spatial and temporal resolution scenarios have immediate applications for impacts assessment and adaptation studies.

IMPLICATIONS OF CLIMATE CHANCE SCENARIOS ON SOIL EROSION POTENTIAL IN THE UNITED STATES

EPA Science Inventory

Atmospheric general circulation models (GCMS) project that rising atmospheric concentrations of CO, and other greenhouse gases may result in lobal changes in temperature and precipitation over the next 50-100 years. quilibrium climate scenarios from 4 GCMs run under doubled CO2 c...

ENERGY COSTS OF IAQ CONTROL THROUGH INCREASED VENTILATION IN A SMALL OFFICE IN A WARM, HUMID CLIMATE: PARAMETRIC ANALYSIS USING THE DOE-2 COMPUTER MODEL

EPA Science Inventory

The report gives results of a series of computer runs using the DOE-2.1E building energy model, simulating a small office in a hot, humid climate (Miami). These simulations assessed the energy and relative humidity (RH) penalties when the outdoor air (OA) ventilation rate is inc...

Regional Climate Sensitivity- and Historical-Based Projections to 2100

NASA Astrophysics Data System (ADS)

Hébert, Raphaël.; Lovejoy, Shaun

2018-05-01

Reliable climate projections at the regional scale are needed in order to evaluate climate change impacts and inform policy. We develop an alternative method for projections based on the transient climate sensitivity (TCS), which relies on a linear relationship between the forced temperature response and the strongly increasing anthropogenic forcing. The TCS is evaluated at the regional scale (5° by 5°), and projections are made accordingly to 2100 using the high and low Representative Concentration Pathways emission scenarios. We find that there are large spatial discrepancies between the regional TCS from 5 historical data sets and 32 global climate model (GCM) historical runs and furthermore that the global mean GCM TCS is about 15% too high. Given that the GCM Representative Concentration Pathway scenario runs are mostly linear with respect to their (inadequate) TCS, we conclude that historical methods of regional projection are better suited given that they are directly calibrated on the real world (historical) climate.

How uncertain are climate model projections of water availability indicators across the Middle East?

PubMed

Hemming, Debbie; Buontempo, Carlo; Burke, Eleanor; Collins, Mat; Kaye, Neil

2010-11-28

The projection of robust regional climate changes over the next 50 years presents a considerable challenge for the current generation of climate models. Water cycle changes are particularly difficult to model in this area because major uncertainties exist in the representation of processes such as large-scale and convective rainfall and their feedback with surface conditions. We present climate model projections and uncertainties in water availability indicators (precipitation, run-off and drought index) for the 1961-1990 and 2021-2050 periods. Ensembles from two global climate models (GCMs) and one regional climate model (RCM) are used to examine different elements of uncertainty. Although all three ensembles capture the general distribution of observed annual precipitation across the Middle East, the RCM is consistently wetter than observations, especially over the mountainous areas. All future projections show decreasing precipitation (ensemble median between -5 and -25%) in coastal Turkey and parts of Lebanon, Syria and Israel and consistent run-off and drought index changes. The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) GCM ensemble exhibits drying across the north of the region, whereas the Met Office Hadley Centre work Quantifying Uncertainties in Model ProjectionsAtmospheric (QUMP-A) GCM and RCM ensembles show slight drying in the north and significant wetting in the south. RCM projections also show greater sensitivity (both wetter and drier) and a wider uncertainty range than QUMP-A. The nature of these uncertainties suggests that both large-scale circulation patterns, which influence region-wide drying/wetting patterns, and regional-scale processes, which affect localized water availability, are important sources of uncertainty in these projections. To reduce large uncertainties in water availability projections, it is suggested that efforts would be well placed to focus on the understanding and modelling of both large-scale processes and their teleconnections with Middle East climate and localized processes involved in orographic precipitation.

Climateprediction.com: Public Involvement, Multi-Million Member Ensembles and Systematic Uncertainty Analysis

NASA Astrophysics Data System (ADS)

Stainforth, D. A.; Allen, M.; Kettleborough, J.; Collins, M.; Heaps, A.; Stott, P.; Wehner, M.

2001-12-01

The climateprediction.com project is preparing to carry out the first systematic uncertainty analysis of climate forecasts using large ensembles of GCM climate simulations. This will be done by involving schools, businesses and members of the public, and utilizing the novel technology of distributed computing. Each participant will be asked to run one member of the ensemble on their PC. The model used will initially be the UK Met Office's Unified Model (UM). It will be run under Windows and software will be provided to enable those involved to view their model output as it develops. The project will use this method to carry out large perturbed physics GCM ensembles and thereby analyse the uncertainty in the forecasts from such models. Each participant/ensemble member will therefore have a version of the UM in which certain aspects of the model physics have been perturbed from their default values. Of course the non-linear nature of the system means that it will be necessary to look not just at perturbations to individual parameters in specific schemes, such as the cloud parameterization, but also to the many combinations of perturbations. This rapidly leads to the need for very large, perhaps multi-million member ensembles, which could only be undertaken using the distributed computing methodology. The status of the project will be presented and the Windows client will be demonstrated. In addition, initial results will be presented from beta test runs using a demo release for Linux PCs and Alpha workstations. Although small by comparison to the whole project, these pilot results constitute a 20-50 member perturbed physics climate ensemble with results indicating how climate sensitivity can be substantially affected by individual parameter values in the cloud scheme.

Future changes in regional precipitation simulated by a half-degree coupled climate model: Sensitivity to horizontal resolution

DOE PAGES

Shields, Christine A.; Kiehl, Jeffrey T.; Meehl, Gerald A.

2016-06-02

The global fully coupled half-degree Community Climate System Model Version 4 (CCSM4) was integrated for a suite of climate change ensemble simulations including five historical runs, five Representative Concentration Pathway 8.5 [RCP8.5) runs, and a long Pre-Industrial control run. This study focuses on precipitation at regional scales and its sensitivity to horizontal resolution. The half-degree historical CCSM4 simulations are compared to observations, where relevant, and to the standard 1° CCSM4. Both the halfdegree and 1° resolutions are coupled to a nominal 1° ocean. North American and South Asian/Indian monsoon regimes are highlighted because these regimes demonstrate improvements due to highermore » resolution, primarily because of better-resolved topography. Agriculturally sensitive areas are analyzed and include Southwest, Central, and Southeast U.S., Southern Europe, and Australia. Both mean and extreme precipitation is discussed for convective and large-scale precipitation processes. Convective precipitation tends to decrease with increasing resolution and large-scale precipitation tends to increase. Improvements for the half-degree agricultural regions can be found for mean and extreme precipitation in the Southeast U.S., Southern Europe, and Australian regions. Climate change responses differ between the model resolutions for the U.S. Southwest/Central regions and are seasonally dependent in the Southeast and Australian regions. Both resolutions project a clear drying signal across Southern Europe due to increased greenhouse warming. As a result, differences between resolutions tied to the representation of convective and large-scale precipitation play an important role in the character of the climate change and depend on regional influences.« less

Real-Time Climate Simulations in the Interactive 3D Game Universe Sandbox ²

NASA Astrophysics Data System (ADS)

Goldenson, N. L.

2014-12-01

Exploration in an open-ended computer game is an engaging way to explore climate and climate change. Everyone can explore physical models with real-time visualization in the educational simulator Universe Sandbox ² (universesandbox.com/2), which includes basic climate simulations on planets. I have implemented a time-dependent, one-dimensional meridional heat transport energy balance model to run and be adjustable in real time in the midst of a larger simulated system. Universe Sandbox ² is based on the original game - at its core a gravity simulator - with other new physically-based content for stellar evolution, and handling collisions between bodies. Existing users are mostly science enthusiasts in informal settings. We believe that this is the first climate simulation to be implemented in a professionally developed computer game with modern 3D graphical output in real time. The type of simple climate model we've adopted helps us depict the seasonal cycle and the more drastic changes that come from changing the orbit or other external forcings. Users can alter the climate as the simulation is running by altering the star(s) in the simulation, dragging to change orbits and obliquity, adjusting the climate simulation parameters directly or changing other properties like CO2 concentration that affect the model parameters in representative ways. Ongoing visuals of the expansion and contraction of sea ice and snow-cover respond to the temperature calculations, and make it accessible to explore a variety of scenarios and intuitive to understand the output. Variables like temperature can also be graphed in real time. We balance computational constraints with the ability to capture the physical phenomena we wish to visualize, giving everyone access to a simple open-ended meridional energy balance climate simulation to explore and experiment with. The software lends itself to labs at a variety of levels about climate concepts including seasons, the Greenhouse effect, reservoirs and flows, albedo feedback, Snowball Earth, climate sensitivity, and model experiment design. Climate calculations are extended to Mars with some modifications to the Earth climate component, and could be used in lessons about the Mars atmosphere, and exploring scenarios of Mars climate history.

Keno-21: Fundamental Issues in the Design of Geophysical Simulation Experiments and Resource Allocation in Climate Modelling

NASA Astrophysics Data System (ADS)

Smith, L. A.

2001-05-01

Many sources of uncertainty come into play when modelling geophysical systems by simulation. These include uncertainty in the initial condition, uncertainty in model parameter values (and the parameterisations themselves) and error in the model class from which the model(s) was selected. In recent decades, climate simulations have focused resources on reducing the last of these by including more and more details into the model. One can question when this ``kitchen sink'' approach should be complimented with realistic estimates of the impact from other uncertainties noted above. Indeed while the impact of model error can never be fully quantified, as all simulation experiments are interpreted a the rosy scenario which assumes a priori that nothing crucial is missing, the impact of other uncertainties can be quantified at only the cost of computational power; as illustrated, for example, in ensemble climate modelling experiments like Casino-21. This talk illustrates the interplay uncertainties in the context of a trivial nonlinear system and an ensemble of models. The simple systems considered in this small scale experiment, Keno-21, are meant to illustrate issues of experimental design; they are not intended to provide true climate simulations. The use of simulation models with huge numbers of parameters given limited data is usually justified by an appeal to the Laws of Physics: the number of free degrees-of-freedom are many fewer than the number of variables; both variables, parameterisations, and parameter values are constrained by ``the physics" and the resulting simulation yields a realistic reproduction of the entire planet's climate system to within reasonable bounds. But what bounds? exactly? In a single model run under transient forcing scenario, there are good statistical grounds for considering only large space and time averages; most of these reasons vanish if an ensemble of runs are made. Ensemble runs can quantify the (in)ability of a model to provide insight on regional changes: if a model cannot capture regional variations in the data on which the model was constructed (that is, in-sample) claims that out-of-sample predictions of those same regional averages should be used in policy making are vacuous. While motivated by climate modelling and illustrated on a trivial nonlinear system, these issues have implications across the range of geophysical modelling. These include implications for appropriate resource allocation, on the making of science policy, and on the public understanding of science and the role of uncertainty in decision making.

Effects of dynamic agricultural decision making in an ecohydrological model

NASA Astrophysics Data System (ADS)

Reichenau, T. G.; Krimly, T.; Schneider, K.

2012-04-01

Due to various interdependencies between the cycles of water, carbon, nitrogen, and energy the impacts of climate change on ecohydrological systems can only be investigated in an integrative way. Furthermore, the human intervention in the environmental processes makes the system even more complex. On the one hand human impact affects natural systems. On the other hand the changing natural systems have a feedback on human decision making. One of the most important examples for this kind of interaction can be found in the agricultural sector. Management dates (planting, fertilization, harvesting) are chosen based on meteorological conditions and yield expectations. A faster development of crops under a warmer climate causes shorter cropping seasons. The choice of crops depends on their profitability, which is mainly determined by market prizes, the agro-political framework, and the (climate dependent) crop yield. This study investigates these relations for the district Günzburg located in the Upper Danube catchment in southern Germany. The modeling system DANUBIA was used to perform dynamically coupled simulations of plant growth, surface and soil hydrological processes, soil nitrogen transformations, and agricultural decision making. The agro-economic model simulates decisions on management dates (based on meteorological conditions and the crops' development state), on fertilization intensities (based on yield expectations), and on choice of crops (based on profitability). The environmental models included in DANUBIA are to a great extent process based to enable its use in a climate change scenario context. Scenario model runs until 2058 were performed using an IPCC A1B forcing. In consecutive runs, dynamic crop management, dynamic crop selection, and a changing agro-political framework were activated. Effects of these model features on hydrological and ecological variables were analyzed separately by comparing the results to a model run with constant crop distribution and constant management. Results show that the influence of the modeled dynamic management adaptation on variables like transpiration, carbon uptake, or nitrate leaching from the vadose zone is stronger than the influence of a dynamic choice of crops. Climate change was found to have a stronger impact on this modeled choice of crops than the agro-political framework. These results suggest that scenario studies in areas with a large share of arable land should take into account management adaptations to changing climate.

An Interactive Multi-Model for Consensus on Climate Change

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

Kocarev, Ljupco

This project purports to develop a new scheme for forming consensus among alternative climate models, that give widely divergent projections as to the details of climate change, that is more intelligent than simply averaging the model outputs, or averaging with ex post facto weighting factors. The method under development effectively allows models to assimilate data from one another in run time with weights that are chosen in an adaptive training phase using 20th century data, so that the models synchronize with one another as well as with reality. An alternate approach that is being explored in parallel is the automatedmore » combination of equations from different models in an expert-system-like framework.« less

Evaluating the ClimEx Single Model Large Ensemble in Comparison with EURO-CORDEX Results of Seasonal Means and Extreme Precipitation Indicators

NASA Astrophysics Data System (ADS)

von Trentini, F.; Schmid, F. J.; Braun, M.; Brisette, F.; Frigon, A.; Leduc, M.; Martel, J. L.; Willkofer, F.; Wood, R. R.; Ludwig, R.

2017-12-01

Meteorological extreme events seem to become more frequent in the present and future, and a seperation of natural climate variability and a clear climate change effect on these extreme events gains more and more interest. Since there is only one realisation of historical events, natural variability in terms of very long timeseries for a robust statistical analysis is not possible with observation data. A new single model large ensemble (SMLE), developed for the ClimEx project (Climate change and hydrological extreme events - risks and perspectives for water management in Bavaria and Québec) is supposed to overcome this lack of data by downscaling 50 members of the CanESM2 (RCP 8.5) with the Canadian CRCM5 regional model (using the EURO-CORDEX grid specifications) for timeseries of 1950-2099 each, resulting in 7500 years of simulated climate. This allows for a better probabilistic analysis of rare and extreme events than any preceding dataset. Besides seasonal sums, several extreme indicators like R95pTOT, RX5day and others are calculated for the ClimEx ensemble and several EURO-CORDEX runs. This enables us to investigate the interaction between natural variability (as it appears in the CanESM2-CRCM5 members) and a climate change signal of those members for past, present and future conditions. Adding the EURO-CORDEX results to this, we can also assess the role of internal model variability (or natural variability) in climate change simulations. A first comparison shows similar magnitudes of variability of climate change signals between the ClimEx large ensemble and the CORDEX runs for some indicators, while for most indicators the spread of the SMLE is smaller than the spread of different CORDEX models.

Inter-model variability in hydrological extremes projections for Amazonian sub-basins

NASA Astrophysics Data System (ADS)

Andres Rodriguez, Daniel; Garofolo, Lucas; Lázaro de Siqueira Júnior, José; Samprogna Mohor, Guilherme; Tomasella, Javier

2014-05-01

Irreducible uncertainties due to knowledge's limitations, chaotic nature of climate system and human decision-making process drive uncertainties in Climate Change projections. Such uncertainties affect the impact studies, mainly when associated to extreme events, and difficult the decision-making process aimed at mitigation and adaptation. However, these uncertainties allow the possibility to develop exploratory analyses on system's vulnerability to different sceneries. The use of different climate model's projections allows to aboard uncertainties issues allowing the use of multiple runs to explore a wide range of potential impacts and its implications for potential vulnerabilities. Statistical approaches for analyses of extreme values are usually based on stationarity assumptions. However, nonstationarity is relevant at the time scales considered for extreme value analyses and could have great implications in dynamic complex systems, mainly under climate change transformations. Because this, it is required to consider the nonstationarity in the statistical distribution parameters. We carried out a study of the dispersion in hydrological extremes projections using climate change projections from several climate models to feed the Distributed Hydrological Model of the National Institute for Spatial Research, MHD-INPE, applied in Amazonian sub-basins. This model is a large-scale hydrological model that uses a TopModel approach to solve runoff generation processes at the grid-cell scale. MHD-INPE model was calibrated for 1970-1990 using observed meteorological data and comparing observed and simulated discharges by using several performance coeficients. Hydrological Model integrations were performed for present historical time (1970-1990) and for future period (2010-2100). Because climate models simulate the variability of the climate system in statistical terms rather than reproduce the historical behavior of climate variables, the performances of the model's runs during the historical period, when feed with climate model data, were tested using descriptors of the Flow Duration Curves. The analyses of projected extreme values were carried out considering the nonstationarity of the GEV distribution parameters and compared with extremes events in present time. Results show inter-model variability in a broad dispersion on projected extreme's values. Such dispersion implies different degrees of socio-economic impacts associated to extreme hydrological events. Despite the no existence of one optimum result, this variability allows the analyses of adaptation strategies and its potential vulnerabilities.

Long-run evolution of the global economy: 2. Hindcasts of innovation and growth

NASA Astrophysics Data System (ADS)

Garrett, T. J.

2015-03-01

Long-range climate forecasts rely upon integrated assessment models that link the global economy to greenhouse gas emissions. This paper evaluates an alternative economic framework, outlined in Part 1, that is based on physical principles rather than explicitly resolved societal dynamics. Relative to a reference model of persistence in trends, model hindcasts that are initialized with data from 1950 to 1960 reproduce trends in global economic production and energy consumption between 2000 and 2010 with a skill score greater than 90%. In part, such high skill appears to be because civilization has responded to an impulse of fossil fuel discovery in the mid-twentieth century. Forecasting the coming century will be more of a challenge because the effect of the impulse appears to have nearly run its course. Nonetheless, the model offers physically constrained futures for the coupled evolution of civilization and climate during the Anthropocene.

Hydrologic Effects of Global Climate Change on a Large Drained Pine Forest

Treesearch

Devendra M. Amatya; Ge Sun; R. W. Skaggs; G. M Chescheir; J. E. Nettles

2006-01-01

A simulation study using a watershed scale forest hydrology model (DRAINWAT) was conducted to evaluate potential effects of climate change on the hydrology of a 3,000 ha managed pine forest in coastal North Carolina. The model was first validated with a five-year (1996-2000) data set fro111 the study site and then run with 50-years (1951-00) of historic weather data...

Investigating NARCCAP Precipitation Extremes via Bivariate Extreme Value Theory (Invited)

NASA Astrophysics Data System (ADS)

Weller, G. B.; Cooley, D. S.; Sain, S. R.; Bukovsky, M. S.; Mearns, L. O.

2013-12-01

We introduce methodology from statistical extreme value theory to examine the ability of reanalysis-drive regional climate models to simulate past daily precipitation extremes. Going beyond a comparison of summary statistics such as 20-year return values, we study whether the most extreme precipitation events produced by climate model simulations exhibit correspondence to the most extreme events seen in observational records. The extent of this correspondence is formulated via the statistical concept of tail dependence. We examine several case studies of extreme precipitation events simulated by the six models of the North American Regional Climate Change Assessment Program (NARCCAP) driven by NCEP reanalysis. It is found that the NARCCAP models generally reproduce daily winter precipitation extremes along the Pacific coast quite well; in contrast, simulation of past daily summer precipitation extremes in a central US region is poor. Some differences in the strength of extremal correspondence are seen in the central region between models which employ spectral nudging and those which do not. We demonstrate how these techniques may be used to draw a link between extreme precipitation events and large-scale atmospheric drivers, as well as to downscale extreme precipitation simulated by a future run of a regional climate model. Specifically, we examine potential future changes in the nature of extreme precipitation along the Pacific coast produced by the pineapple express (PE) phenomenon. A link between extreme precipitation events and a "PE Index" derived from North Pacific sea-surface pressure fields is found. This link is used to study PE-influenced extreme precipitation produced by a future-scenario climate model run.

Future efficiency of run of the river hydropower schemes based on climate change scenarios: case study in UK catchments

NASA Astrophysics Data System (ADS)

Pasten Zapata, Ernesto; Moggridge, Helen; Jones, Julie; Widmann, Martin

2017-04-01

Run-of-the-River (ROR) hydropower schemes are expected to be importantly affected by climate change as they rely in the availability of river flow to generate energy. As temperature and precipitation are expected to vary in the future, the hydrological cycle will also undergo changes. Therefore, climate models based on complex physical atmospheric interactions have been developed to simulate future climate scenarios considering the atmosphere's greenhouse gas concentrations. These scenarios are classified according to the Representative Concentration Pathways (RCP) that are generated according to the concentration of greenhouse gases. This study evaluates possible scenarios for selected ROR hydropower schemes within the UK, considering three different RCPs: 2.6, 4.5 and 8.5 W/m2 for 2100 relative to pre-industrial values. The study sites cover different climate, land cover, topographic and hydropower scheme characteristics representative of the UK's heterogeneity. Precipitation and temperature outputs from state-of-the-art Regional Climate Models (RCMs) from the Euro-CORDEX project are used as input for a HEC-HMS hydrological model to simulate the future river flow available. Both uncorrected and bias-corrected RCM simulations are analyzed. The results of this project provide an insight of the possible effects of climate change towards the generation of power from the ROR hydropower schemes according to the different RCP scenarios and contrasts the results obtained from uncorrected and bias-corrected RCMs. This analysis can aid on the adaptation to climate change as well as the planning of future ROR schemes in the region.

European extra-tropical storm damage risk from a multi-model ensemble of dynamically-downscaled global climate models

NASA Astrophysics Data System (ADS)

Haylock, M. R.

2011-10-01

Uncertainty in the return levels of insured loss from European wind storms was quantified using storms derived from twenty-two 25 km regional climate model runs driven by either the ERA40 reanalyses or one of four coupled atmosphere-ocean global climate models. Storms were identified using a model-dependent storm severity index based on daily maximum 10 m wind speed. The wind speed from each model was calibrated to a set of 7 km historical storm wind fields using the 70 storms with the highest severity index in the period 1961-2000, employing a two stage calibration methodology. First, the 25 km daily maximum wind speed was downscaled to the 7 km historical model grid using the 7 km surface roughness length and orography, also adopting an empirical gust parameterisation. Secondly, downscaled wind gusts were statistically scaled to the historical storms to match the geographically-dependent cumulative distribution function of wind gust speed. The calibrated wind fields were run through an operational catastrophe reinsurance risk model to determine the return level of loss to a European population density-derived property portfolio. The risk model produced a 50-yr return level of loss of between 0.025% and 0.056% of the total insured value of the portfolio.

EdGCM: Research Tools for Training the Climate Change Generation

NASA Astrophysics Data System (ADS)

Chandler, M. A.; Sohl, L. E.; Zhou, J.; Sieber, R.

2011-12-01

Climate scientists employ complex computer simulations of the Earth's physical systems to prepare climate change forecasts, study the physical mechanisms of climate, and to test scientific hypotheses and computer parameterizations. The Intergovernmental Panel on Climate Change 4th Assessment Report (2007) demonstrates unequivocally that policy makers rely heavily on such Global Climate Models (GCMs) to assess the impacts of potential economic and emissions scenarios. However, true climate modeling capabilities are not disseminated to the majority of world governments or U.S. researchers - let alone to the educators who will be training the students who are about to be presented with a world full of climate change stakeholders. The goal is not entirely quixotic; in fact, by the mid-1990's prominent climate scientists were predicting with certainty that schools and politicians would "soon" be running GCMs on laptops [Randall, 1996]. For a variety of reasons this goal was never achieved (nor even really attempted). However, around the same time NASA and the National Science Foundation supported a small pilot project at Columbia University to show the potential of putting sophisticated computer climate models - not just "demos" or "toy models" - into the hands of non-specialists. The Educational Global Climate Modeling Project (EdGCM) gave users access to a real global climate model and provided them with the opportunity to experience the details of climate model setup, model operation, post-processing and scientific visualization. EdGCM was designed for use in both research and education - it is a full-blown research GCM, but the ultimate goal is to develop a capability to embed these crucial technologies across disciplines, networks, platforms, and even across academia and industry. With this capability in place we can begin training the skilled workforce that is necessary to deal with the multitude of climate impacts that will occur over the coming decades. To further increase the educational potential of climate models, the EdGCM project has also created "EZgcm". Through a joint venture of NASA, Columbia University and McGill University EZgcm moves the focus toward a greater use of Web 1.0 and Web 2.0-based technologies. It shifts the educational objectives towards a greater emphasis on teaching students how science is conducted and what role science plays in assessing climate change. That is, students learn about the steps of the scientific process as conveyed by climate modeling research: constructing a hypothesis, designing an experiment, running a computer model, using scientific visualization to support analysis, communicating the results of that analysis, and role playing the scientific peer review process. This is in stark contrast to what they learn from the political debate over climate change, which they often confuse with a scientific debate.

The effects of climate downscaling technique and observational data set on modeled ecological responses.

PubMed

Pourmokhtarian, Afshin; Driscoll, Charles T; Campbell, John L; Hayhoe, Katharine; Stoner, Anne M K

2016-07-01

Assessments of future climate change impacts on ecosystems typically rely on multiple climate model projections, but often utilize only one downscaling approach trained on one set of observations. Here, we explore the extent to which modeled biogeochemical responses to changing climate are affected by the selection of the climate downscaling method and training observations used at the montane landscape of the Hubbard Brook Experimental Forest, New Hampshire, USA. We evaluated three downscaling methods: the delta method (or the change factor method), monthly quantile mapping (Bias Correction-Spatial Disaggregation, or BCSD), and daily quantile regression (Asynchronous Regional Regression Model, or ARRM). Additionally, we trained outputs from four atmosphere-ocean general circulation models (AOGCMs) (CCSM3, HadCM3, PCM, and GFDL-CM2.1) driven by higher (A1fi) and lower (B1) future emissions scenarios on two sets of observations (1/8º resolution grid vs. individual weather station) to generate the high-resolution climate input for the forest biogeochemical model PnET-BGC (eight ensembles of six runs).The choice of downscaling approach and spatial resolution of the observations used to train the downscaling model impacted modeled soil moisture and streamflow, which in turn affected forest growth, net N mineralization, net soil nitrification, and stream chemistry. All three downscaling methods were highly sensitive to the observations used, resulting in projections that were significantly different between station-based and grid-based observations. The choice of downscaling method also slightly affected the results, however not as much as the choice of observations. Using spatially smoothed gridded observations and/or methods that do not resolve sub-monthly shifts in the distribution of temperature and/or precipitation can produce biased results in model applications run at greater temporal and/or spatial resolutions. These results underscore the importance of carefully considering field observations used for training, as well as the downscaling method used to generate climate change projections, for smaller-scale modeling studies. Different sources of variability including selection of AOGCM, emissions scenario, downscaling technique, and data used for training downscaling models, result in a wide range of projected forest ecosystem responses to future climate change. © 2016 by the Ecological Society of America.

Downscaling CESM1 climate change projections for the MENA-CORDEX domain using WRF

NASA Astrophysics Data System (ADS)

Zittis, George; Hadjinicolaou, Panos; Lelieveld, Jos

2017-04-01

According to analysis of observations and global climate model projections, the broader Middle East, North Africa and Mediterranean region is found to be a climate change hotspot. Substantial changes in precipitation amounts and patterns and strong summer warming (including an intensification of heat extremes) is a likely future scenario for the region, but a recent uncertainty analysis indicated good model agreement for temperature but much less for precipitation. Although the horizontal resolution of global models has increased over the last years, it is still not adequate for impact and adaptation assessments of regional or national level and further downscaling of the climate information is required. The region is now studied within the CORDEX initiative (Coordinated Regional Climate Downscaling Experiment) with the establishment of a domain covering the Middle East - North Africa (MENA-CORDEX) region (http://mena-cordex.cyi.ac.cy/). In this study, we present the first climate change projections for the MENA produced by dynamically downscaling a bias-corrected output of the CESM1 global earth system model. For the downscaling, we use a climate configuration of the Weather, Research and Forecasting model (WRF). Our simulations use a standard CORDEX Phase I 50-km grid in three simulations, a historical (1950-2005) and two scenario runs (2006-2100) with the greenhouse gas forcing following the RCP 4.5 and 8.5. We evaluate precipitation, temperature and other surface meteorological variables from the historical using gridded and station observational datasets. Maps of projected changes are constructed for different periods in the future as differences of the two scenarios model output against the data from the historical run. The main spatial and temporal patterns of change are discussed, especially in the context of the United Nations Framework Convention on Climate Change agreement in Paris to limit the global average temperature increase to 1.5 degrees above pre-industrial levels.

The Met Office HadGEM3-ES chemistry-climate model: evaluation of stratospheric dynamics and its impact on ozone

NASA Astrophysics Data System (ADS)

Hardiman, Steven C.; Butchart, Neal; O'Connor, Fiona M.; Rumbold, Steven T.

2017-03-01

Free-running and nudged versions of a Met Office chemistry-climate model are evaluated and used to investigate the impact of dynamics versus transport and chemistry within the model on the simulated evolution of stratospheric ozone. Metrics of the dynamical processes relevant for simulating stratospheric ozone are calculated, and the free-running model is found to outperform the previous model version in 10 of the 14 metrics. In particular, large biases in stratospheric transport and tropical tropopause temperature, which existed in the previous model version, are substantially reduced, making the current model more suitable for the simulation of stratospheric ozone. The spatial structure of the ozone hole, the area of polar stratospheric clouds, and the increased ozone concentrations in the Northern Hemisphere winter stratosphere following sudden stratospheric warmings, were all found to be sensitive to the accuracy of the dynamics and were better simulated in the nudged model than in the free-running model. Whilst nudging can, in general, provide a useful tool for removing the influence of dynamical biases from the evolution of chemical fields, this study shows that issues can remain in the climatology of nudged models. Significant biases in stratospheric vertical velocities, age of air, water vapour, and total column ozone still exist in the Met Office nudged model. Further, these can lead to biases in the downward flux of ozone into the troposphere.

Mid-Holocene and last glacial maximum climate simulations with the IPSL model: part II: model-data comparisons

NASA Astrophysics Data System (ADS)

Kageyama, Masa; Braconnot, Pascale; Bopp, Laurent; Mariotti, Véronique; Roy, Tilla; Woillez, Marie-Noëlle; Caubel, Arnaud; Foujols, Marie-Alice; Guilyardi, Eric; Khodri, Myriam; Lloyd, James; Lombard, Fabien; Marti, Olivier

2013-05-01

The climates of the mid-Holocene (MH, 6,000 years ago) and the Last Glacial Maximum (LGM, 21,000 years ago) have been extensively documented and as such, have become targets for the evaluation of climate models for climate contexts very different from the present. In Part 1 of the present work, we have studied the MH and LGM simulations performed with the last two versions of the IPSL model: IPSL_CM4, run for the PMIP2/CMIP3 (Coupled Model Intercomparion Project) projects and IPSL_CM5A, run for the most recent PMIP3/CMIP5 projets. We have shown that not only are these models different in their simulations of the PI climate, but also in their simulations of the climatic anomalies for the MH and LGM. In the Part 2 of this paper, we first examine whether palaeo-data can help discriminate between the model performances. This is indeed the case for the African monsoon for the MH or for North America south of the Laurentide ice sheet, the South Atlantic or the southern Indian ocean for the LGM. For the LGM, off-line vegetation modelling appears to offer good opportunities to distinguish climate model results because glacial vegetation proves to be very sensitive to even small differences in LGM climate. For other cases such as the LGM North Atlantic or the LGM equatorial Pacific, the large uncertainty on the SST reconstructions, prevents model discrimination. We have examined the use of other proxy-data for model evaluation, which has become possible with the inclusion of the biogeochemistry morel PISCES in the IPSL_CM5A model. We show a broad agreement of the LGM-PI export production changes with reconstructions. These changes are related to the mixed layer depth in most regions and to sea-ice variations in the high latitudes. We have also modelled foraminifer abundances with the FORAMCLIM model and shown that the changes in foraminifer abundance in the equatorial Pacific are mainly forced by changes in SSTs, hence confirming the SST-foraminifer abundance relationship. Yet, this is not the case in all regions in the North Atlantic, where food availability can have a strong impact of foraminifer abundances. Further work will be needed to exhaustively examine the role of factors other than climate in piloting changes in palaeo-indicators.

“ How Reliable is the Couple of WRF & VIC Models”

EPA Science Inventory

The ability of the fully coupling of Weather Research & Forecasting Model (WRF) and Variable Infiltration Capacity (VIC) model to produce hydrological and climate variables was evaluated. First, the VIC model was run by using observed meteorological data and calibrated in the Upp...

Coupled model simulations of climate changes in the 20th century and beyond

NASA Astrophysics Data System (ADS)

Yu, Yongqiang; Zhi, Hai; Wang, Bin; Wan, Hui; Li, Chao; Liu, Hailong; Li, Wei; Zheng, Weipeng; Zhou, Tianjun

2008-07-01

Several scenario experiments of the IPCC 4th Assessment Report (AR4) are performed by version g1.0 of a Flexible coupled Ocean-Atmosphere-Land System Model (FGOALS) developed at the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP/CAS), including the “Climate of the 20th century experiment”, “CO2 1% increase per year to doubling experiment” and two separate IPCC greenhouse gases emission scenarios A1B and B1 experiments. To distinguish between the different impacts of natural variations and human activities on the climate change, three-member ensemble runs are performed for each scenario experiment. The coupled model simulations show: (1) from 1900 to 2000, the global mean temperature increases about 0.5°C and the major increase occurs during the later half of the 20th century, which is in consistent with the observations that highlights the coupled model’s ability to reproduce the climate changes since the industrial revolution; (2) the global mean surface air temperature increases about 1.6°C in the CO2 doubling experiment and 1.5°C and 2.4°C in the A1B and B1 scenarios, respectively. The global warming is indicated by not only the changes of the surface temperature and precipitation but also the temperature increase in the deep ocean. The thermal expansion of the sea water would induce the rise of the global mean sea level. Both the control run and the 20th century climate change run are carried out again with version g1.1 of FGOALS, in which the cold biases in the high latitudes were removed. They are then compared with those from version g1.0 of FGOALS in order to distinguish the effect of the model biases on the simulation of global warming.

Transient climate simulations of the deglaciation 21-9 thousand years before present; PMIP4 Core experiment design and boundary conditions

NASA Astrophysics Data System (ADS)

Ivanovic, Ruza; Gregoire, Lauren; Kageyama, Masa; Roche, Didier; Valdes, Paul; Burke, Andrea; Drummond, Rosemarie; Peltier, W. Richard; Tarasov, Lev

2016-04-01

The last deglaciation, which marked the transition between the last glacial and present interglacial periods, was punctuated by a series of rapid (centennial and decadal) climate changes. Numerical climate models are useful for investigating mechanisms that underpin the events, especially now that some of the complex models can be run for multiple millennia. We have set up a Paleoclimate Modelling Intercomparison Project (PMIP) working group to coordinate efforts to run transient simulations of the last deglaciation, and to facilitate the dissemination of expertise between modellers and those engaged with reconstructing the climate of the last 21 thousand years. Here, we present the design of a coordinated Core simulation over the period 21-9 thousand years before present (ka) with time varying orbital forcing, greenhouse gases, ice sheets, and other geographical changes. A choice of two ice sheet reconstructions is given. Additional focussed simulations will also be coordinated on an ad-hoc basis by the working group, for example to investigate the effect of ice sheet and iceberg meltwater, and the uncertainty in other forcings. Some of these focussed simulations will concentrate on shorter durations around specific events to allow the more computationally expensive models to take part. Ivanovic, R. F., Gregoire, L. J., Kageyama, M., Roche, D. M., Valdes, P. J., Burke, A., Drummond, R., Peltier, W. R., and Tarasov, L.: Transient climate simulations of the deglaciation 21-9 thousand years before present; PMIP4 Core experiment design and boundary conditions, Geosci. Model Dev. Discuss., 8, 9045-9102, doi:10.5194/gmdd-8-9045-2015, 2015.

The Prodiguer Messaging Platform

NASA Astrophysics Data System (ADS)

Denvil, S.; Greenslade, M. A.; Carenton, N.; Levavasseur, G.; Raciazek, J.

2015-12-01

CONVERGENCE is a French multi-partner national project designed to gather HPC and informatics expertise to innovate in the context of running French global climate models with differing grids and at differing resolutions. Efficient and reliable execution of these models and the management and dissemination of model output are some of the complexities that CONVERGENCE aims to resolve.At any one moment in time, researchers affiliated with the Institut Pierre Simon Laplace (IPSL) climate modeling group, are running hundreds of global climate simulations. These simulations execute upon a heterogeneous set of French High Performance Computing (HPC) environments. The IPSL's simulation execution runtime libIGCM (library for IPSL Global Climate Modeling group) has recently been enhanced so as to support hitherto impossible realtime use cases such as simulation monitoring, data publication, metrics collection, simulation control, visualizations … etc. At the core of this enhancement is Prodiguer: an AMQP (Advanced Message Queue Protocol) based event driven asynchronous distributed messaging platform. libIGCM now dispatches copious amounts of information, in the form of messages, to the platform for remote processing by Prodiguer software agents at IPSL servers in Paris. Such processing takes several forms: Persisting message content to database(s); Launching rollback jobs upon simulation failure; Notifying downstream applications; Automation of visualization pipelines; We will describe and/or demonstrate the platform's: Technical implementation; Inherent ease of scalability; Inherent adaptiveness in respect to supervising simulations; Web portal receiving simulation notifications in realtime.

Projected Changes on the Global Surface Wave Drift Climate towards the END of the Twenty-First Century

NASA Astrophysics Data System (ADS)

Carrasco, Ana; Semedo, Alvaro; Behrens, Arno; Weisse, Ralf; Breivik, Øyvind; Saetra, Øyvind; Håkon Christensen, Kai

2016-04-01

The global wave-induced current (the Stokes Drift - SD) is an important feature of the ocean surface, with mean values close to 10 cm/s along the extra-tropical storm tracks in both hemispheres. Besides the horizontal displacement of large volumes of water the SD also plays an important role in the ocean mix-layer turbulence structure, particularly in stormy or high wind speed areas. The role of the wave-induced currents in the ocean mix-layer and in the sea surface temperature (SST) is currently a hot topic of air-sea interaction research, from forecast to climate ranges. The SD is mostly driven by wind sea waves and highly sensitive to changes in the overlaying wind speed and direction. The impact of climate change in the global wave-induced current climate will be presented. The wave model WAM has been forced by the global climate model (GCM) ECHAM5 wind speed (at 10 m height) and ice, for present-day and potential future climate conditions towards the end of the end of the twenty-first century, represented by the Intergovernmental Panel for Climate Change (IPCC) CMIP3 (Coupled Model Inter-comparison Project phase 3) A1B greenhouse gas emission scenario (usually referred to as a ''medium-high emissions'' scenario). Several wave parameters were stored as output in the WAM model simulations, including the wave spectra. The 6 hourly and 0.5°×0.5°, temporal and space resolution, wave spectra were used to compute the SD global climate of two 32-yr periods, representative of the end of the twentieth (1959-1990) and twenty-first (1969-2100) centuries. Comparisons of the present climate run with the ECMWF (European Centre for Medium-Range Weather Forecasts) ERA-40 reanalysis are used to assess the capability of the WAM-ECHAM5 runs to produce realistic SD results. This study is part of the WRCP-JCOMM COWCLIP (Coordinated Ocean Wave Climate Project) effort.

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.

Approximating uncertainty of annual runoff and reservoir yield using stochastic replicates of global climate model data

NASA Astrophysics Data System (ADS)

Peel, M. C.; Srikanthan, R.; McMahon, T. A.; Karoly, D. J.

2015-04-01

Two key sources of uncertainty in projections of future runoff for climate change impact assessments are uncertainty between global climate models (GCMs) and within a GCM. Within-GCM uncertainty is the variability in GCM output that occurs when running a scenario multiple times but each run has slightly different, but equally plausible, initial conditions. The limited number of runs available for each GCM and scenario combination within the Coupled Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) data sets, limits the assessment of within-GCM uncertainty. In this second of two companion papers, the primary aim is to present a proof-of-concept approximation of within-GCM uncertainty for monthly precipitation and temperature projections and to assess the impact of within-GCM uncertainty on modelled runoff for climate change impact assessments. A secondary aim is to assess the impact of between-GCM uncertainty on modelled runoff. Here we approximate within-GCM uncertainty by developing non-stationary stochastic replicates of GCM monthly precipitation and temperature data. These replicates are input to an off-line hydrologic model to assess the impact of within-GCM uncertainty on projected annual runoff and reservoir yield. We adopt stochastic replicates of available GCM runs to approximate within-GCM uncertainty because large ensembles, hundreds of runs, for a given GCM and scenario are unavailable, other than the Climateprediction.net data set for the Hadley Centre GCM. To date within-GCM uncertainty has received little attention in the hydrologic climate change impact literature and this analysis provides an approximation of the uncertainty in projected runoff, and reservoir yield, due to within- and between-GCM uncertainty of precipitation and temperature projections. In the companion paper, McMahon et al. (2015) sought to reduce between-GCM uncertainty by removing poorly performing GCMs, resulting in a selection of five better performing GCMs from CMIP3 for use in this paper. Here we present within- and between-GCM uncertainty results in mean annual precipitation (MAP), mean annual temperature (MAT), mean annual runoff (MAR), the standard deviation of annual precipitation (SDP), standard deviation of runoff (SDR) and reservoir yield for five CMIP3 GCMs at 17 worldwide catchments. Based on 100 stochastic replicates of each GCM run at each catchment, within-GCM uncertainty was assessed in relative form as the standard deviation expressed as a percentage of the mean of the 100 replicate values of each variable. The average relative within-GCM uncertainties from the 17 catchments and 5 GCMs for 2015-2044 (A1B) were MAP 4.2%, SDP 14.2%, MAT 0.7%, MAR 10.1% and SDR 17.6%. The Gould-Dincer Gamma (G-DG) procedure was applied to each annual runoff time series for hypothetical reservoir capacities of 1 × MAR and 3 × MAR and the average uncertainties in reservoir yield due to within-GCM uncertainty from the 17 catchments and 5 GCMs were 25.1% (1 × MAR) and 11.9% (3 × MAR). Our approximation of within-GCM uncertainty is expected to be an underestimate due to not replicating the GCM trend. However, our results indicate that within-GCM uncertainty is important when interpreting climate change impact assessments. Approximately 95% of values of MAP, SDP, MAT, MAR, SDR and reservoir yield from 1 × MAR or 3 × MAR capacity reservoirs are expected to fall within twice their respective relative uncertainty (standard deviation/mean). Within-GCM uncertainty has significant implications for interpreting climate change impact assessments that report future changes within our range of uncertainty for a given variable - these projected changes may be due solely to within-GCM uncertainty. Since within-GCM variability is amplified from precipitation to runoff and then to reservoir yield, climate change impact assessments that do not take into account within-GCM uncertainty risk providing water resources management decision makers with a sense of certainty that is unjustified.

Handling Uncertainty in Palaeo-Climate Models and Data

NASA Astrophysics Data System (ADS)

Voss, J.; Haywood, A. M.; Dolan, A. M.; Domingo, D.

2017-12-01

The study of palaeoclimates can provide data on the behaviour of the Earth system with boundary conditions different from the ones we observe in the present. One of the main challenges in this approach is that data on past climates comes with large uncertainties, since quantities of interest cannot be observed directly, but must be derived from proxies instead. We consider proxy-derived data from the Pliocene (around 3 millions years ago; the last interval in Earth history when CO2 was at modern or near future levels) and contrast this data to the output of complex climate models. In order to perform a meaningful data-model comparison, uncertainties must be taken into account. In this context, we discuss two examples of complex data-model comparison problems. Both examples have in common that they involve fitting a statistical model to describe how the output of the climate simulations depends on various model parameters, including atmospheric CO2 concentration and orbital parameters (obliquity, excentricity, and precession). This introduces additional uncertainties, but allows to explore a much larger range of model parameters than would be feasible by only relying on simulation runs. The first example shows how Gaussian process emulators can be used to perform data-model comparison when simulation runs only differ in the choice of orbital parameters, but temperature data is given in the (somewhat inconvenient) form of "warm peak averages". The second example shows how a simpler approach, based on linear regression, can be used to analyse a more complex problem where we use a larger and more varied ensemble of climate simulations with the aim to estimate Earth System Sensitivity.

Combining Statistics and Physics to Improve Climate Downscaling

NASA Astrophysics Data System (ADS)

Gutmann, E. D.; Eidhammer, T.; Arnold, J.; Nowak, K.; Clark, M. P.

2017-12-01

Getting useful information from climate models is an ongoing problem that has plagued climate science and hydrologic prediction for decades. While it is possible to develop statistical corrections for climate models that mimic current climate almost perfectly, this does not necessarily guarantee that future changes are portrayed correctly. In contrast, convection permitting regional climate models (RCMs) have begun to provide an excellent representation of the regional climate system purely from first principles, providing greater confidence in their change signal. However, the computational cost of such RCMs prohibits the generation of ensembles of simulations or long time periods, thus limiting their applicability for hydrologic applications. Here we discuss a new approach combining statistical corrections with physical relationships for a modest computational cost. We have developed the Intermediate Complexity Atmospheric Research model (ICAR) to provide a climate and weather downscaling option that is based primarily on physics for a fraction of the computational requirements of a traditional regional climate model. ICAR also enables the incorporation of statistical adjustments directly within the model. We demonstrate that applying even simple corrections to precipitation while the model is running can improve the simulation of land atmosphere feedbacks in ICAR. For example, by incorporating statistical corrections earlier in the modeling chain, we permit the model physics to better represent the effect of mountain snowpack on air temperature changes.

Potential impact of climate change on coffee rust over Mexico and Central America

NASA Astrophysics Data System (ADS)

Calderon-Ezquerro, Maria del Carmen; Martinez-Lopez, Benjamin; Cabos Narvaez, William David; Sein, Dmitry

2017-04-01

In this work, some meteorological variables from a regional climate model are used to characterize the dispersion of coffee rust (a fungal disease) from Central America to Mexico, during the 20 Century. The climate model consists of the regional atmosphere model REMO coupled to the MPIOM global ocean model with increased resolution in the Atlantic Ocean. Lateral atmospheric and upper oceanic boundary conditions outside the coupled domain were prescribed using both ERA-40 and ERA-Interim reanalysis data. In addition to the historical simulation, a projection of the evolution of the coffee rust for the 21 Century was obtained from a REMO run using MPIESM data for the lateral forcing.

An Investigation of Bomb Cyclogenesis in NCEP's CFS Model

NASA Astrophysics Data System (ADS)

Alvarez, F. M.; Eichler, T.; Gottschalck, J.

2008-12-01

With the concerns, impacts and consequences of climate change increasing, the need for climate models to simulate daily weather is very important. Given the improvements in resolution and physical parameterizations, climate models are becoming capable of resolving extreme weather events. A particular type of extreme event which has large impacts on transportation, industry and the general public is a rapidly intensifying cyclone referred to as a "bomb." In this study, bombs are investigated using the National Center for Environmental Prediction's (NCEP) Climate Forecast System (CFS) model. We generate storm tracks based on 6-hourly sea-level pressure (SLP) from long-term climate runs of the CFS model. Investigation of this dataset has revealed that the CFS model is capable of producing bombs. We show a case study of a bomb in the CFS model and demonstrate that it has characteristics similar to the observed. Since the CFS model is capable of producing bombs, future work will focus on trends in their frequency and intensity so that an assessment of the potential role of the bomb in climate change can be assessed.

National Centers for Environmental Prediction

Science.gov Websites

Modeling Mesoscale Modeling Marine Modeling and Analysis Teams Climate Data Assimilation Ensembles and Post missed NDAS cycles since 1 Apr 1995 Log of NAM model code changes Log of NAM model test runs Problems and Prediction (NCWCP) 5830 University Research Court College Park, MD 20740 Page Author: EMC Webmaster Page

Multifractal analysis of a GCM climate

NASA Astrophysics Data System (ADS)

Carl, P.

2003-04-01

Multifractal analysis using the Wavelet Transform Modulus Maxima (WTMM) approach is being applied to the climate of a Mintz--Arakawa type, coarse resolution, two--layer AGCM. The model shows a backwards running period multiplication scenario throughout the northern summer, subsequent to a 'hard', subcritical Hopf bifurcation late in spring. This 'route out of chaos' (seen in cross sections of a toroidal phase space structure) is born in the planetary monsoon system which inflates the seasonal 'cycle' into these higher order structures and is blamed for the pronounced intraseasonal--to--centennial model climate variability. Previous analyses of the latter using advanced modal decompositions showed regularity based patterns in the time--frequency plane which are qualitatively similar to those obtained from the real world. The closer look here at the singularity structures, as a fundamental diagnostic supplement, aims at both more complete understanding (and quantification) of the model's qualitative dynamics and search for further tools of model intercomparison and verification in this respect. Analysing wavelet is the 10th derivative of the Gaussian which might suffice to suppress regular patterns in the data. Intraseasonal attractors, studied in time series of model precipitation over Central India, show shifting and braodening singularity spectra towards both more violent extreme events (premonsoon--monsoon transition) and weaker events (late summer to postmonsoon transition). Hints at a fractal basin boundary are found close to transition from period--2 to period--1 in the monsoon activity cycle. Interannual analyses are provided for runs with varied solar constants. To address the (in--)stationarity issue, first results are presented with a windowed multifractal analysis of longer--term runs ("singularity spectrogram").

"Development of an interactive crop growth web service architecture to review and forecast agricultural sustainability"

NASA Astrophysics Data System (ADS)

Seamon, E.; Gessler, P. E.; Flathers, E.; Walden, V. P.

2014-12-01

As climate change and weather variability raise issues regarding agricultural production, agricultural sustainability has become an increasingly important component for farmland management (Fisher, 2005, Akinci, 2013). Yet with changes in soil quality, agricultural practices, weather, topography, land use, and hydrology - accurately modeling such agricultural outcomes has proven difficult (Gassman et al, 2007, Williams et al, 1995). This study examined agricultural sustainability and soil health over a heterogeneous multi-watershed area within the Inland Pacific Northwest of the United States (IPNW) - as part of a five year, USDA funded effort to explore the sustainability of cereal production systems (Regional Approaches to Climate Change for Pacific Northwest Agriculture - award #2011-68002-30191). In particular, crop growth and soil erosion were simulated across a spectrum of variables and time periods - using the CropSyst crop growth model (Stockle et al, 2002) and the Water Erosion Protection Project Model (WEPP - Flanagan and Livingston, 1995), respectively. A preliminary range of historical scenarios were run, using a high-resolution, 4km gridded dataset of surface meteorological variables from 1979-2010 (Abatzoglou, 2012). In addition, Coupled Model Inter-comparison Project (CMIP5) global climate model (GCM) outputs were used as input to run crop growth model and erosion future scenarios (Abatzoglou and Brown, 2011). To facilitate our integrated data analysis efforts, an agricultural sustainability web service architecture (THREDDS/Java/Python based) is under development, to allow for the programmatic uploading, sharing and processing of variable input data, running model simulations, as well as downloading and visualizing output results. The results of this study will assist in better understanding agricultural sustainability and erosion relationships in the IPNW, as well as provide a tangible server-based tool for use by researchers and farmers - for both small scale field examination, or more regionalized scenarios.

Climate change impact assessment on Veneto and Friuli Plain groundwater. Part I: an integrated modeling approach for hazard scenario construction.

PubMed

Baruffi, F; Cisotto, A; Cimolino, A; Ferri, M; Monego, M; Norbiato, D; Cappelletto, M; Bisaglia, M; Pretner, A; Galli, A; Scarinci, A; Marsala, V; Panelli, C; Gualdi, S; Bucchignani, E; Torresan, S; Pasini, S; Critto, A; Marcomini, A

2012-12-01

Climate change impacts on water resources, particularly groundwater, is a highly debated topic worldwide, triggering international attention and interest from both researchers and policy makers due to its relevant link with European water policy directives (e.g. 2000/60/EC and 2007/118/EC) and related environmental objectives. The understanding of long-term impacts of climate variability and change is therefore a key challenge in order to address effective protection measures and to implement sustainable management of water resources. This paper presents the modeling approach adopted within the Life+ project TRUST (Tool for Regional-scale assessment of groUndwater Storage improvement in adaptation to climaTe change) in order to provide climate change hazard scenarios for the shallow groundwater of high Veneto and Friuli Plain, Northern Italy. Given the aim to evaluate potential impacts on water quantity and quality (e.g. groundwater level variation, decrease of water availability for irrigation, variations of nitrate infiltration processes), the modeling approach integrated an ensemble of climate, hydrologic and hydrogeologic models running from the global to the regional scale. Global and regional climate models and downscaling techniques were used to make climate simulations for the reference period 1961-1990 and the projection period 2010-2100. The simulation of the recent climate was performed using observed radiative forcings, whereas the projections have been done prescribing the radiative forcings according to the IPCC A1B emission scenario. The climate simulations and the downscaling, then, provided the precipitation, temperatures and evapo-transpiration fields used for the impact analysis. Based on downscaled climate projections, 3 reference scenarios for the period 2071-2100 (i.e. the driest, the wettest and the mild year) were selected and used to run a regional geomorphoclimatic and hydrogeological model. The final output of the model ensemble produced information about the potential variations of the water balance components (e.g. river discharge, groundwater level and volume) due to climate change. Such projections were used to develop potential hazard scenarios for the case study area, to be further applied within climate change risk assessment studies for groundwater resources and associated ecosystems. This paper describes the models' chain and the methodological approach adopted in the TRUST project and analyzes the hazard scenarios produced in order to investigate climate change risks for the case study area. Copyright © 2012 Elsevier B.V. All rights reserved.

Information-computational platform for collaborative multidisciplinary investigations of regional climatic changes and their impacts

NASA Astrophysics Data System (ADS)

Gordov, Evgeny; Lykosov, Vasily; Krupchatnikov, Vladimir; Okladnikov, Igor; Titov, Alexander; Shulgina, Tamara

2013-04-01

Analysis of growing volume of related to climate change data from sensors and model outputs requires collaborative multidisciplinary efforts of researchers. To do it timely and in reliable way one needs in modern information-computational infrastructure supporting integrated studies in the field of environmental sciences. Recently developed experimental software and hardware platform Climate (http://climate.scert.ru/) provides required environment for regional climate change related investigations. The platform combines modern web 2.0 approach, GIS-functionality and capabilities to run climate and meteorological models, process large geophysical datasets and support relevant analysis. It also supports joint software development by distributed research groups, and organization of thematic education for students and post-graduate students. In particular, platform software developed includes dedicated modules for numerical processing of regional and global modeling results for consequent analysis and visualization. Also run of integrated into the platform WRF and «Planet Simulator» models, modeling results data preprocessing and visualization is provided. All functions of the platform are accessible by a user through a web-portal using common graphical web-browser in the form of an interactive graphical user interface which provides, particularly, capabilities of selection of geographical region of interest (pan and zoom), data layers manipulation (order, enable/disable, features extraction) and visualization of results. Platform developed provides users with capabilities of heterogeneous geophysical data analysis, including high-resolution data, and discovering of tendencies in climatic and ecosystem changes in the framework of different multidisciplinary researches. Using it even unskilled user without specific knowledge can perform reliable computational processing and visualization of large meteorological, climatic and satellite monitoring datasets through unified graphical web-interface. Partial support of RF Ministry of Education and Science grant 8345, SB RAS Program VIII.80.2 and Projects 69, 131, 140 and APN CBA2012-16NSY project is acknowledged.

Continuously on-­going regional climate hindcast simulations for impact applications

NASA Astrophysics Data System (ADS)

Anders, Ivonne; Piringer, Martin; Kaufmann, Hildegard; Knauder, Werner; Resch, Gernot; Andre, Konrad

2017-04-01

Observational data for e.g. temperature, precipitation, radiation, or wind are often used as meteorological forcing for different impact models, like e.g. crop models, urban models, economic models and energy system models. To assess a climate signal, the time period covered by the observation is often too short, they have gaps in between, and are inhomogeneous over time, due to changes in the measurements itself or in the near surrounding. Thus output from global and regional climate models can close the gap and provide homogeneous and physically consistent time series of meteorological parameters. CORDEX evaluation runs performed for the IPCC-AR5 provide a good base for the regional scale. However, with respect to climate services, continuously on-going hindcast simulations are required for regularly updated applications. The Climate Research group at the national Austrian weather service, ZAMG, is focusing on high mountain regions and, especially on the Alps. The hindcast-simulation performed with the regional climate model COSMO-CLM is forced by ERAinterim and optimized for the Alpine Region. The simulation available for the period of 1979-2015 in a spatial resolution of about 10km is prolonged ongoing and fullfils the customer's needs with respect of output variables, levels, intervals and statistical measures. One of the main tasks is to capture strong precipitation events which often occur during summer when low pressure systems develop over the Golf of Genoa, moving to the Northeast. This leads to floods and landslide events in Austria, Czech Republic and Germany. Such events are not sufficiently represented in the CORDEX-evaluation runs. ZAMG use high quality gridded precipitation and temperature data for the Alpine Region (1-6km) to evaluate the model performance. Data is provided e.g. to hydrological modellers (high water, low water), but also to assess icing capability of infrastructure or the calculation the separation distances between livestock farming and residential area.

Supermodeling by Synchronization of Alternative SPEEDO Models

NASA Astrophysics Data System (ADS)

Duane, Gregory; Selten, Frank

2016-04-01

The supermodeling approach, wherein different imperfect models of the same objective process are dynamically combined in run-time to reduce systematic error, is tested using SPEEDO - a primitive equation atmospheric model coupled to the CLIO ocean model. Three versions of SPEEDO are defined by parameters that differ in a range that arguably mimics differences among state-of-the-art climate models. A fourth model is taken to represent truth. The "true" ocean drives all three model atmospheres. The three models are also connected to one another at every level, with spatially uniform nudging coefficients that are trained so that the three models, which synchronize with one another, also synchronize with truth when data is continuously assimilated, as in weather prediction. The SPEEDO supermodel is evaluated in weather-prediction mode, with nudging to truth. It is found that the supemodel performs better than any of the three models and marginally better than the best weighted average of the outputs of the three models run separately. To evaluate the utility for climate projection, parameters corresponding to green house gas levels are changed in truth and in the three models. The supermodel formed with inter-model connections from the present-CO2 runs no longer give the optimal configuration for the supermodel in the doubled-CO2 realm, but the supermodel with the previously trained connections is still useful as compared to the separate models or averages of their outputs. In ongoing work, a training algorithm is examined that attempts to match the blocked-zonal index cycle of the SPEEDO model atmosphere to truth, rather than simply minimizing the RMS error in the various fields. Such an approach comes closer to matching the model attractor to the true attractor - the desired effect in climate projection - rather than matching instantaneous states. Gradient descent in a cost function defined over a finite temporal window can indeed be done efficiently. Preliminary results are presented for a crudely defined index cycle.

Global Climate Impacts of Fixing the Southern Ocean Shortwave Radiation Bias in the Community Earth System Model (CESM)

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

Kay, Jennifer E.; Wall, Casey; Yettella, Vineel

Here, a large, long-standing, and pervasive climate model bias is excessive absorbed shortwave radiation (ASR) over the midlatitude oceans, especially the Southern Ocean. This study investigates both the underlying mechanisms for and climate impacts of this bias within the Community Earth System Model, version 1, with the Community Atmosphere Model, version 5 [CESM1(CAM5)]. Excessive Southern Ocean ASR in CESM1(CAM5) results in part because low-level clouds contain insufficient amounts of supercooled liquid. In a present-day atmosphere-only run, an observationally motivated modification to the shallow convection detrainment increases supercooled cloud liquid, brightens low-level clouds, and substantially reduces the Southern Ocean ASR bias.more » Tuning to maintain global energy balance enables reduction of a compensating tropical ASR bias. In the resulting preindustrial fully coupled run with a brighter Southern Ocean and dimmer tropics, the Southern Ocean cools and the tropics warm. As a result of the enhanced meridional temperature gradient, poleward heat transport increases in both hemispheres (especially the Southern Hemisphere), and the Southern Hemisphere atmospheric jet strengthens. Because northward cross-equatorial heat transport reductions occur primarily in the ocean (80%), not the atmosphere (20%), a proposed atmospheric teleconnection linking Southern Ocean ASR bias reduction and cooling with northward shifts in tropical precipitation has little impact. In summary, observationally motivated supercooled liquid water increases in shallow convective clouds enable large reductions in long-standing climate model shortwave radiation biases. Of relevance to both model bias reduction and climate dynamics, quantifying the influence of Southern Ocean cooling on tropical precipitation requires a model with dynamic ocean heat transport.« less

Global Climate Impacts of Fixing the Southern Ocean Shortwave Radiation Bias in the Community Earth System Model (CESM)

DOE PAGES

Kay, Jennifer E.; Wall, Casey; Yettella, Vineel; ...

2016-06-10

Here, a large, long-standing, and pervasive climate model bias is excessive absorbed shortwave radiation (ASR) over the midlatitude oceans, especially the Southern Ocean. This study investigates both the underlying mechanisms for and climate impacts of this bias within the Community Earth System Model, version 1, with the Community Atmosphere Model, version 5 [CESM1(CAM5)]. Excessive Southern Ocean ASR in CESM1(CAM5) results in part because low-level clouds contain insufficient amounts of supercooled liquid. In a present-day atmosphere-only run, an observationally motivated modification to the shallow convection detrainment increases supercooled cloud liquid, brightens low-level clouds, and substantially reduces the Southern Ocean ASR bias.more » Tuning to maintain global energy balance enables reduction of a compensating tropical ASR bias. In the resulting preindustrial fully coupled run with a brighter Southern Ocean and dimmer tropics, the Southern Ocean cools and the tropics warm. As a result of the enhanced meridional temperature gradient, poleward heat transport increases in both hemispheres (especially the Southern Hemisphere), and the Southern Hemisphere atmospheric jet strengthens. Because northward cross-equatorial heat transport reductions occur primarily in the ocean (80%), not the atmosphere (20%), a proposed atmospheric teleconnection linking Southern Ocean ASR bias reduction and cooling with northward shifts in tropical precipitation has little impact. In summary, observationally motivated supercooled liquid water increases in shallow convective clouds enable large reductions in long-standing climate model shortwave radiation biases. Of relevance to both model bias reduction and climate dynamics, quantifying the influence of Southern Ocean cooling on tropical precipitation requires a model with dynamic ocean heat transport.« less

High-resolution regional climate simulations of precipitation and snowpack over the US northern Rockies in a changing climate

NASA Astrophysics Data System (ADS)

Wang, Y.; Geerts, B.; Liu, C.

2015-12-01

This work first examines the performance of a regional climate model in capturing orographic precipitation and snowpack dynamics in the northern US Rockies. The Weather Research and Forecasting (WRF) model is run at a sufficiently fine resolution (4-km horizontal grid spacing), over a sub-continental domain driven by the Climate Forecast System Reanalysis (CFSR), to examine WRF's ability to simulate the observed seasonal precipitation and snowpack dynamics. WRF retrospective simulations are being run over a 30-year period from 1980 to 2010. Observations from Snow Telemetry (SNOTEL, providing precipitation rate and snowpack snow water equivalent (SWE)) and the Parameter-elevation Regressions on Independent Slopes Model (PRISM, providing fine-scale monthly mean values of precipitation and temperature) are used for validation. The results show that WRF captures observed seasonal precipitation and snowpack build-up reasonably well. The second part of this work is in progress. A pseudo-global warming (PGW) technique is used to perturb the retrospective reanalysis with the anticipated change according to the consensus global model guidance under the CMIP5 "high emissions" (RCP8.5) scenario produced by the CCSM4. This technique preserves low-frequency general circulation patterns and the characteristics of storms entering the domain. The WRF model is rerun over 30 years centered on 2050 with perturbed initial and boundary conditions. The results will be used to examine the effect of climate variability and projected global warming on the statistical distributions of precipitation amounts and SWE in the studied domain.

Climate change projections for Tamil Nadu, India: deriving high-resolution climate data by a downscaling approach using PRECIS

NASA Astrophysics Data System (ADS)

Bal, Prasanta Kumar; Ramachandran, A.; Geetha, R.; Bhaskaran, B.; Thirumurugan, P.; Indumathi, J.; Jayanthi, N.

2016-02-01

In this paper, we present regional climate change projections for the Tamil Nadu state of India, simulated by the Met Office Hadley Centre regional climate model. The model is run at 25 km horizontal resolution driven by lateral boundary conditions generated by a perturbed physical ensemble of 17 simulations produced by a version of Hadley Centre coupled climate model, known as HadCM3Q under A1B scenario. The large scale features of these 17 simulations were evaluated for the target region to choose lateral boundary conditions from six members that represent a range of climate variations over the study region. The regional climate, known as PRECIS, was then run 130 years from 1970. The analyses primarily focus on maximum and minimum temperatures and rainfall over the region. For the Tamil Nadu as a whole, the projections of maximum temperature show an increase of 1.0, 2.2 and 3.1 °C for the periods 2020s (2005-2035), 2050s (2035-2065) and 2080s (2065-2095), respectively, with respect to baseline period (1970-2000). Similarly, the projections of minimum temperature show an increase of 1.1, 2.4 and 3.5 °C, respectively. This increasing trend is statistically significant (Mann-Kendall trend test). The annual rainfall projections for the same periods indicate a general decrease in rainfall of about 2-7, 1-4 and 4-9 %, respectively. However, significant exceptions are noticed over some pockets of western hilly areas and high rainfall areas where increases in rainfall are seen. There are also indications of increasing heavy rainfall events during the northeast monsoon season and a slight decrease during the southwest monsoon season. Such an approach of using climate models may maximize the utility of high-resolution climate change information for impact-adaptation-vulnerability assessments.

Towards a regional climate model coupled to a comprehensive hydrological model

NASA Astrophysics Data System (ADS)

Rasmussen, S. H.; Drews, M.; Christensen, J. H.; Butts, M. B.; Jensen, K. H.; Refsgaard, J.; Hydrological ModellingAssessing Climate Change Impacts At Different Scales (Hyacints)

2010-12-01

When planing new ground water abstractions wells, building areas, roads or other land use activities information about expected future groundwater table location for the lifetime of the construction may be critical. The life time of an abstraction well can be expected to be more than 50 years, while if for buildings may be up to 100 years or more. The construction of an abstraction well is expensive and it is important to know if clean groundwater is available for its expected life time. The future groundwater table is depending on the future climate. With climate change the hydrology is expected to change as well. Traditionally, this assessment has been done by driving hydrological models with output from a climate model. In this way feedback between the groundwater hydrology and the climate is neglected. Neglecting this feedback can lead to imprecise or wrong results. The goal of this work is to couple the regional climate model HIRHAM (Christensen et al. 2006) to the hydrological model MIKE SHE (Graham and Butts, 2006). The coupling exploits the new OpenMI technology that provides a standardized interface to define, describe and transfer data on a time step basis between software components that run simultaneously (Gregersen et al., 2007). HIRHAM runs on a UNIX platform whereas MIKE SHE and OpenMI are under WINDOWS. Therefore the first critical task has been to develop an effective communication link between the platforms. The first step towards assessing the coupled models performance are addressed by looking at simulated land-surface atmosphere feedback through variables such as evapotranspiration, sensible heat flux and soil moisture content. Christensen, O.B., Drews, M., Christensen, J.H., Dethloff, K., Ketelsen, K., Hebestadt, I. and Rinke, A. (2006) The HIRHAM Regional Climate Model. Version 5; DMI Scientific Report 0617. Danish Meteorological Institute. Graham, D.N. and Butts, M.B. (2005) Flexible, integrated watershed modelling with MIKE SHE, In Watershed Models, (Eds. V.P. Singh & D.K. Frevert) CRC Press. Pages 245-272, ISBN: 0849336090. Gregersen, J.B., Gijsbers, P.J.A. and Westen, S.J.P. (2007) OpenMI: Open modelling interface. Journal of Hydroinformatics, 09.3, 175191. doi: 10.2166/hydro.2007.023.

DOE unveils climate model in advance of global test

NASA Astrophysics Data System (ADS)

Popkin, Gabriel

2018-05-01

The world's growing collection of climate models has a high-profile new entry. Last week, after nearly 4 years of work, the U.S. Department of Energy (DOE) released computer code and initial results from an ambitious effort to simulate the Earth system. The new model is tailored to run on future supercomputers and designed to forecast not just how climate will change, but also how those changes might stress energy infrastructure. Results from an upcoming comparison of global models may show how well the new entrant works. But so far it is getting a mixed reception, with some questioning the need for another model and others saying the $80 million effort has yet to improve predictions of the future climate. Even the project's chief scientist, Ruby Leung of the Pacific Northwest National Laboratory in Richland, Washington, acknowledges that the model is not yet a leader.

High skill in low-frequency climate response through fluctuation dissipation theorems despite structural instability.

PubMed

Majda, Andrew J; Abramov, Rafail; Gershgorin, Boris

2010-01-12

Climate change science focuses on predicting the coarse-grained, planetary-scale, longtime changes in the climate system due to either changes in external forcing or internal variability, such as the impact of increased carbon dioxide. The predictions of climate change science are carried out through comprehensive, computational atmospheric, and oceanic simulation models, which necessarily parameterize physical features such as clouds, sea ice cover, etc. Recently, it has been suggested that there is irreducible imprecision in such climate models that manifests itself as structural instability in climate statistics and which can significantly hamper the skill of computer models for climate change. A systematic approach to deal with this irreducible imprecision is advocated through algorithms based on the Fluctuation Dissipation Theorem (FDT). There are important practical and computational advantages for climate change science when a skillful FDT algorithm is established. The FDT response operator can be utilized directly for multiple climate change scenarios, multiple changes in forcing, and other parameters, such as damping and inverse modelling directly without the need of running the complex climate model in each individual case. The high skill of FDT in predicting climate change, despite structural instability, is developed in an unambiguous fashion using mathematical theory as guidelines in three different test models: a generic class of analytical models mimicking the dynamical core of the computer climate models, reduced stochastic models for low-frequency variability, and models with a significant new type of irreducible imprecision involving many fast, unstable modes.

A first look at the influence of anthropogenic climate change on the future delivery of fluvial sediment to the Ganges-Brahmaputra-Meghna delta.

PubMed

Darby, Stephen E; Dunn, Frances E; Nicholls, Robert J; Rahman, Munsur; Riddy, Liam

2015-09-01

We employ a climate-driven hydrological water balance and sediment transport model (HydroTrend) to simulate future climate-driven sediment loads flowing into the Ganges-Brahmaputra-Meghna (GBM) mega-delta. The model was parameterised using high-quality topographic data and forced with daily temperature and precipitation data obtained from downscaled Regional Climate Model (RCM) simulations for the period 1971-2100. Three perturbed RCM model runs were selected to quantify the potential range of future climate conditions associated with the SRES A1B scenario. Fluvial sediment delivery rates to the GBM delta associated with these climate data sets are projected to increase under the influence of anthropogenic climate change, albeit with the magnitude of the increase varying across the two catchments. Of the two study basins, the Brahmaputra's fluvial sediment load is predicted to be more sensitive to future climate change. Specifically, by the middle part of the 21(st) century, our model results suggest that sediment loads increase (relative to the 1981-2000 baseline period) over a range of between 16% and 18% (depending on climate model run) for the Ganges, but by between 25% and 28% for the Brahmaputra. The simulated increase in sediment flux emanating from the two catchments further increases towards the end of the 21(st) century, reaching between 34% and 37% for the Ganges and between 52% and 60% for the Brahmaputra by the 2090s. The variability in these changes across the three climate change simulations is small compared to the changes, suggesting they represent a significant increase. The new data obtained in this study offer the first estimate of whether and how anthropogenic climate change may affect the delivery of fluvial sediment to the GBM delta, informing assessments of the future sustainability and resilience of one of the world's most vulnerable mega-deltas. Specifically, such significant increases in future sediment loads could increase the resilience of the delta to sea-level rise by giving greater potential for vertical accretion. However, these increased sediment fluxes may not be realised due to uncertainties in the monsoon related response to climate change or other human-induced changes in the catchment: this is a subject for further research.

A Coupled Regional Climate Simulator for the Gulf of St. Lawrence, Canada

NASA Astrophysics Data System (ADS)

Faucher, M.; Saucier, F.; Caya, D.

2003-12-01

The climate of Eastern Canada is characterized by atmosphere-ocean-ice interactions due to the closeness of the North Atlantic Ocean and the Labrador Sea. Also, there are three relatively large inner basins: the Gulf of St-Lawrence, the Hudson Bay / Hudson Strait / Foxe Basin system and the Great Lakes, influencing the evolution of weather systems and therefore the regional climate. These basins are characterized by irregular coastlines and variables sea-ice in winter, so that the interactions between the atmosphere and the ocean are more complex. There are coupled general circulation models (GCMs) that are available to study the climate of Eastern Canada, but their resolution (near 350km) is to low to resolve the details of the regional climate of this area and to provide valuable information for climate impact studies. The goal of this work is to develop a coupled regional climate simulator for Eastern Canada to study the climate and its variability, necessary to assess the future climate in a double CO2 situation. An off-line coupling strategy through the interacting fields is used to link the Canadian Regional Climate Model developed at the "Universite du Quebec a Montreal" (CRCM, Caya and Laprise 1999) to the Gulf of St. Lawrence ocean model developed at the "Institut Maurice-Lamontagne" (GOM, Saucier et al. 2002). This strategy involves running both simulators separately and alternatively, using variables from the other simulator to supply the needed forcing fields every day. We present the results of a first series of seasonal simulations performed with this system to show the ability of our climate simulator to reproduce the known characteristics of the regional circulation such as mesoscale oceanic features, fronts and sea-ice. The simulations were done for the period from December 1st, 1989 to March 31st, 1990. The results are compared with those of previous uncoupled runs (Faucher et al. 2003) and with observations.

Visualization and Analysis of Climate Simulation Performance Data

NASA Astrophysics Data System (ADS)

Röber, Niklas; Adamidis, Panagiotis; Behrens, Jörg

2015-04-01

Visualization is the key process of transforming abstract (scientific) data into a graphical representation, to aid in the understanding of the information hidden within the data. Climate simulation data sets are typically quite large, time varying, and consist of many different variables sampled on an underlying grid. A large variety of climate models - and sub models - exist to simulate various aspects of the climate system. Generally, one is mainly interested in the physical variables produced by the simulation runs, but model developers are also interested in performance data measured along with these simulations. Climate simulation models are carefully developed complex software systems, designed to run in parallel on large HPC systems. An important goal thereby is to utilize the entire hardware as efficiently as possible, that is, to distribute the workload as even as possible among the individual components. This is a very challenging task, and detailed performance data, such as timings, cache misses etc. have to be used to locate and understand performance problems in order to optimize the model implementation. Furthermore, the correlation of performance data to the processes of the application and the sub-domains of the decomposed underlying grid is vital when addressing communication and load imbalance issues. High resolution climate simulations are carried out on tens to hundreds of thousands of cores, thus yielding a vast amount of profiling data, which cannot be analyzed without appropriate visualization techniques. This PICO presentation displays and discusses the ICON simulation model, which is jointly developed by the Max Planck Institute for Meteorology and the German Weather Service and in partnership with DKRZ. The visualization and analysis of the models performance data allows us to optimize and fine tune the model, as well as to understand its execution on the HPC system. We show and discuss our workflow, as well as present new ideas and solutions that greatly aided our understanding. The software employed is based on Avizo Green, ParaView and SimVis, as well as own developed software extensions.

Terrestrial water cycle and the impact of climate change.

PubMed

Tao, Fulu; Yokozawa, Masayuki; Hayashi, Yousay; Lin, Erda

2003-06-01

The terrestrial water cycle and the impact of climate change are critical for agricultural and natural ecosystems. In this paper, we assess both by running a macro-scale water balance model under a baseline condition and 2 General Circulation Model (GCM)-based climate change scenarios. The results show that in 2021-2030, water demand will increase worldwide due to climate change. Water shortage is expected to worsen in western Asia, the Arabian Peninsula, northern and southern Africa, northeastern Australia, southwestern North America, and central South America. A significant increase in surface runoff is expected in southern Asia and a significant decrease is expected in northern South America. These changes will have implications for regional environment and socioeconomics.

Detection of greenhouse-gas-induced climatic change. Progress report, July 1, 1994--July 31, 1995

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

Jones, P.D.; Wigley, T.M.L.

1995-07-21

The objective of this research is to assembly and analyze instrumental climate data and to develop and apply climate models as a basis for detecting greenhouse-gas-induced climatic change, and validation of General Circulation Models. In addition to changes due to variations in anthropogenic forcing, including greenhouse gas and aerosol concentration changes, the global climate system exhibits a high degree of internally-generated and externally-forced natural variability. To detect the anthropogenic effect, its signal must be isolated from the ``noise`` of this natural climatic variability. A high quality, spatially extensive data base is required to define the noise and its spatial characteristics.more » To facilitate this, available land and marine data bases will be updated and expanded. The data will be analyzed to determine the potential effects on climate of greenhouse gas and aerosol concentration changes and other factors. Analyses will be guided by a variety of models, from simple energy balance climate models to coupled atmosphere ocean General Circulation Models. These analyses are oriented towards obtaining early evidence of anthropogenic climatic change that would lead either to confirmation, rejection or modification of model projections, and towards the statistical validation of General Circulation Model control runs and perturbation experiments.« less

Evaluation of the new EMAC-SWIFT chemistry climate model

NASA Astrophysics Data System (ADS)

Scheffler, Janice; Langematz, Ulrike; Wohltmann, Ingo; Rex, Markus

2016-04-01

It is well known that the representation of atmospheric ozone chemistry in weather and climate models is essential for a realistic simulation of the atmospheric state. Including atmospheric ozone chemistry into climate simulations is usually done by prescribing a climatological ozone field, by including a fast linear ozone scheme into the model or by using a climate model with complex interactive chemistry. While prescribed climatological ozone fields are often not aligned with the modelled dynamics, a linear ozone scheme may not be applicable for a wide range of climatological conditions. Although interactive chemistry provides a realistic representation of atmospheric chemistry such model simulations are computationally very expensive and hence not suitable for ensemble simulations or simulations with multiple climate change scenarios. A new approach to represent atmospheric chemistry in climate models which can cope with non-linearities in ozone chemistry and is applicable to a wide range of climatic states is the Semi-empirical Weighted Iterative Fit Technique (SWIFT) that is driven by reanalysis data and has been validated against observational satellite data and runs of a full Chemistry and Transport Model. SWIFT has recently been implemented into the ECHAM/MESSy (EMAC) chemistry climate model that uses a modular approach to climate modelling where individual model components can be switched on and off. Here, we show first results of EMAC-SWIFT simulations and validate these against EMAC simulations using the complex interactive chemistry scheme MECCA, and against observations.

Agricultural Decision Support Through Robust Assimilation of Satellite Derived Soil Moisture Estimates

NASA Astrophysics Data System (ADS)

Mishra, V.; Cruise, J.; Mecikalski, J. R.

2012-12-01

Soil Moisture is a key component in the hydrological process, affects surface and boundary layer energy fluxes and is the driving factor in agricultural production. Multiple in situ soil moisture measuring instruments such as Time-domain Reflectrometry (TDR), Nuclear Probes etc. are in use along with remote sensing methods like Active and Passive Microwave (PM) sensors. In situ measurements, despite being more accurate, can only be obtained at discrete points over small spatial scales. Remote sensing estimates, on the other hand, can be obtained over larger spatial domains with varying spatial and temporal resolutions. Soil moisture profiles derived from satellite based thermal infrared (TIR) imagery can overcome many of the problems associated with laborious in-situ observations over large spatial domains. An area where soil moisture observation and assimilation is receiving increasing attention is agricultural crop modeling. This study revolves around the use of the Decision Support System for Agrotechnology Transfer (DSSAT) crop model to simulate corn yields under various forcing scenarios. First, the model was run and calibrated using observed precipitation and model generated soil moisture dynamics. Next, the modeled soil moisture was updated using estimates derived from satellite based TIR imagery and the Atmospheric Land Exchange Inverse (ALEXI) model. We selected three climatically different locations to test the concept. Test Locations were selected to represent varied climatology. Bell Mina, Alabama - South Eastern United States, representing humid subtropical climate. Nabb, Indiana - Mid Western United States, representing humid continental climate. Lubbok, Texas - Southern United States, representing semiarid steppe climate. A temporal (2000-2009) correlation analysis of the soil moisture values from both DSSAT and ALEXI were performed and validated against the Land Information System (LIS) soil moisture dataset. The results clearly show strong correlation (R = 73%) between ALEXI and DSSAT at Bell Mina. At Nabb and Lubbock the correlation was 50-60%. Further, multiple experiments were conducted for each location: a) a DSSAT rain-fed 10 year sequential run forced with daymet precipitation; b) a DSSAT sequential run with no precipitation data; and c) a DSSAT run forced with ALEXI soil moisture estimates alone. The preliminary results of all the experiments are quantified through soil moisture correlations and yield comparisons. In general, the preliminary results strongly suggest that DSSAT forced with ALEXI can provide significant information especially at locations where no significant precipitation data exists.

Benchmark Comparison of Dual- and Quad-Core Processor Linux Clusters with Two Global Climate Modeling Workloads

NASA Technical Reports Server (NTRS)

McGalliard, James

2008-01-01

This viewgraph presentation details the science and systems environments that NASA High End computing program serves. Included is a discussion of the workload that is involved in the processing for the Global Climate Modeling. The Goddard Earth Observing System Model, Version 5 (GEOS-5) is a system of models integrated using the Earth System Modeling Framework (ESMF). The GEOS-5 system was used for the Benchmark tests, and the results of the tests are shown and discussed. Tests were also run for the Cubed Sphere system, results for these test are also shown.

An Online Approach for Training International Climate Scientists to Use Computer Models

NASA Astrophysics Data System (ADS)

Yarker, M. B.; Mesquita, M. D.; Veldore, V.

2013-12-01

With the mounting evidence by the work of IPCC (2007), climate change has been acknowledged as a significant challenge to Sustainable Development by the international community. It is important that scientists in developing countries have access to knowledge and tools so that well-informed decisions can be made about the mitigation and adaptation of climate change. However, training researchers to use climate modeling techniques and data analysis has become a challenge, because current capacity building approaches train researchers to use climate models through short-term workshops, which requires a large amount of funding. It has also been observed that many participants who recently completed capacity building courses still view climate and weather models as a metaphorical 'black box', where data goes in and results comes out; and there is evidence that these participants lack a basic understanding of the climate system. Both of these issues limit the ability of some scientists to go beyond running a model based on rote memorization of the process. As a result, they are unable to solve problems regarding run-time errors, thus cannot determine whether or not their model simulation is reasonable. Current research in the field of science education indicates that there are effective strategies to teach learners about science models. They involve having the learner work with, experiment with, modify, and apply models in a way that is significant and informative to the learner. It has also been noted that in the case of computational models, the installation and set up process alone can be time consuming and confusing for new users, which can hinder their ability to concentrate on using, experimenting with, and applying the model to real-world scenarios. Therefore, developing an online version of capacity building is an alternative approach to the workshop training programs, which makes use of new technologies and it allows for a long-term educational process in a way that engages the learners with the subject matter, in a way that is meaningful for their region. A number of science-education courses are being conducted online within a capacity building project called 'The Future of Climate Extremes in the Caribbean (XCUBE)'. If accepted, this presentation will explore a case study related to the online training courses provided via the website m2lab.org for the XCUBE project: 'Regional Climate Modeling using WRF'. The course relates to teaching participants how to run WRF for climate simulations using a special version of the model called e-WRF (WRF for Educational purposes). This version of WRF does not require installation so that student learning can be focused on using the model itself. In order to explore the effectiveness of the course, data will be collected from the participants as they complete it. There are currently over 200 participants registered for the course and are made up of graduate students, professors, and researchers from many different science fields. Preliminary results indicate that many students enrolled in this course have previously taken a WRF tutorial, but do not feel confident enough to use it. Despite having taken a tutorial previously, for some participants the basic design of the model was a new concept to them. If accepted, a statistical analysis will be performed as more students complete the course.

Current and future pluvial flood hazard analysis for the city of Antwerp

NASA Astrophysics Data System (ADS)

Willems, Patrick; Tabari, Hossein; De Niel, Jan; Van Uytven, Els; Lambrechts, Griet; Wellens, Geert

2016-04-01

For the city of Antwerp in Belgium, higher rainfall extremes were observed in comparison with surrounding areas. The differences were found statistically significant for some areas and may be the result of the heat island effect in combination with the higher concentrations of aerosols. A network of 19 rain gauges but with varying records length (the longest since the 1960s) and continuous radar data for 10 years were combined to map the spatial variability of rainfall extremes over the city at various durations from 15 minutes to 1 day together with the uncertainty. The improved spatial rainfall information was used as input in the sewer system model of the city to analyze the frequency of urban pluvial floods. Comparison with historical flood observations from various sources (fire brigade and media) confirmed that the improved spatial rainfall information also improved sewer impact results on both the magnitude and frequency of the sewer floods. Next to these improved urban flood impact results for recent and current climatological conditions, the new insights on the local rainfall microclimate were also helpful to enhance future projections on rainfall extremes and pluvial floods in the city. This was done by improved statistical downscaling of all available CMIP5 global climate model runs (160 runs) for the 4 RCP scenarios, as well as the available EURO-CORDEX regional climate model runs. Two types of statistical downscaling methods were applied for that purpose (a weather typing based method, and a quantile perturbation approach), making use of the microclimate results and its dependency on specific weather types. Changes in extreme rainfall intensities were analyzed and mapped as a function of the RCP scenario, together with the uncertainty, decomposed in the uncertainties related to the climate models, the climate model initialization or limited length of the 30-year time series (natural climate variability) and the statistical downscaling (albeit limited to two types of methods). These were finally transferred into future pluvial flash flood hazard maps for the city together with the uncertainties, and are considered as basis for spatial planning and adaptation.

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.

Clouds in ECMWF's 30 KM Resolution Global Atmospheric Forecast Model (TL639)

NASA Technical Reports Server (NTRS)

Cahalan, R. F.; Morcrette, J. J.

1999-01-01

Global models of the general circulation of the atmosphere resolve a wide range of length scales, and in particular cloud structures extend from planetary scales to the smallest scales resolvable, now down to 30 km in state-of-the-art models. Even the highest resolution models do not resolve small-scale cloud phenomena seen, for example, in Landsat and other high-resolution satellite images of clouds. Unresolved small-scale disturbances often grow into larger ones through non-linear processes that transfer energy upscale. Understanding upscale cascades is of crucial importance in predicting current weather, and in parameterizing cloud-radiative processes that control long term climate. Several movie animations provide examples of the temporal and spatial variation of cloud fields produced in 4-day runs of the forecast model at the European Centre for Medium-Range Weather Forecasts (ECMWF) in Reading, England, at particular times and locations of simultaneous measurement field campaigns. model resolution is approximately 30 km horizontally (triangular truncation TL639) with 31 vertical levels from surface to stratosphere. Timestep of the model is about 10 minutes, but animation frames are 3 hours apart, at timesteps when the radiation is computed. The animations were prepared from an archive of several 4-day runs at the highest available model resolution, and archived at ECMWF. Cloud, wind and temperature fields in an approximately 1000 km X 1000 km box were retrieved from the archive, then approximately 60 Mb Vis5d files were prepared with the help of Graeme Kelly of ECMWF, and were compressed into MPEG files each less than 3 Mb. We discuss the interaction of clouds and radiation in the model, and compare the variability of cloud liquid as a function of scale to that seen in cloud observations made in intensive field campaigns. Comparison of high-resolution global runs to cloud-resolving models, and to lower resolution climate models is leading to better understanding of the upscale cascade and suggesting new cloud-radiation parameterizations for climate models.

Incorporating realistic surface longwave spectral emissivity in the CESM and the impact on simulated current climate and climate changes

NASA Astrophysics Data System (ADS)

Chen, X.; Huang, X.; Flanner, M.; Yang, P.; Feldman, D.; Kuo, C.

2016-12-01

As of today, most state-of-the-art GCMs still assumes blackbody surface in their longwave radiation scheme. Recent works by Chen et al. (2014) and Feldman et al. (2014) have suggested that the surface spectral emissivity can impact the simulated radiation budget and climate change in a discernible way, especially in high latitudes. Using a recently developed global emissivity database that covers both far-IR and mid-IR, we incorporated the LW surface spectral emissivity into the radiation scheme of the CESM. Effort has been made to ensure a consistent treatment of surface upward LW broadband flux in both the land module and the atmospheric module of the CESM, an important aspect overlooked by the previous study. Then we assess impacts of the inclusion of surface spectral emissivity on simulated mean-state climate and climate changes by carrying out two sets of parallel runs. The first pair of experiments uses the standard slab-ocean CESM v1.1.1 to run two experiments: one control run using forcings at year 2000 level and one sensitivity run abruptly doubling the CO2. The second pair of experiment setup is identical to the first one but using the CESM that we have modified (Surface emissivity is a prognostic variable in our second pair of experiments). The current climate simulation results show that the Sahara desert region in the modified CESM has a warmer surface temperature than in the standard CESM by 2-3K. Over the high-latitude regions, the modified CESM tends to have a colder surface temperature than the standard CESM by 1-2.5K. As a result, the climatological sea ice coverage in the modified CESM is 8% more than it in the standard CESM in both Polar Regions. All these differences are statistically significant. As for simulated climate change in response to a doubling of CO2, the Arctic region in the modified CESM warms consistently faster than in the standard CESM by 1-2K while the Antarctic region shows a non-uniform pattern of differences between two models. Differences in the changes of sea ice coverage between two models show a zonally-uniform dipole pattern over both polar oceans. The reasons for such differences and its linkage with the change of surface spectral emissivity are further explained.

Reducing EnergyPlus Run Time For Code Compliance Tools

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

Athalye, Rahul A.; Gowri, Krishnan; Schultz, Robert W.

2014-09-12

Integration of the EnergyPlus ™ simulation engine into performance-based code compliance software raises a concern about simulation run time, which impacts timely feedback of compliance results to the user. EnergyPlus annual simulations for proposed and code baseline building models, and mechanical equipment sizing result in simulation run times beyond acceptable limits. This paper presents a study that compares the results of a shortened simulation time period using 4 weeks of hourly weather data (one per quarter), to an annual simulation using full 52 weeks of hourly weather data. Three representative building types based on DOE Prototype Building Models and threemore » climate zones were used for determining the validity of using a shortened simulation run period. Further sensitivity analysis and run time comparisons were made to evaluate the robustness and run time savings of using this approach. The results of this analysis show that the shortened simulation run period provides compliance index calculations within 1% of those predicted using annual simulation results, and typically saves about 75% of simulation run time.« less

Pesticide exposure assessment for surface waters in the EU. Part 2: Determination of statistically based run-off and drainage scenarios for Germany.

PubMed

Bach, Martin; Diesner, Mirjam; Großmann, Dietlinde; Guerniche, Djamal; Hommen, Udo; Klein, Michael; Kubiak, Roland; Müller, Alexandra; Preuss, Thomas G; Priegnitz, Jan; Reichenberger, Stefan; Thomas, Kai; Trapp, Matthias

2017-05-01

In order to assess surface water exposure to active substances of plant protection products (PPPs) in the European Union (EU), the FOCUS (FOrum for the Co-ordination of pesticide fate models and their USe) surface water workgroup introduced four run-off and six drainage scenarios for Step 3 of the tiered FOCUSsw approach. These scenarios may not necessarily represent realistic worst-case situations for the different Member States of the EU. Hence, the suitability of the scenarios for risk assessment in the national authorisation procedures is not known. Using Germany as an example, the paper illustrates how national soil-climate scenarios can be developed to model entries of active substances into surface waters from run-off and erosion (using the model PRZM) and from drainage (using the model MACRO). In the authorisation procedure for PPPs on Member State level, such soil-climate scenarios can be used to determine exposure endpoints with a defined overall percentile. The approach allows the development of national specific soil-climate scenarios and to calculate percentile-based exposure endpoints. The scenarios have been integrated into a software tool analogous to FOCUS-SWASH which can be used in the future to assess surface water exposure in authorisation procedures of PPPs in Germany. © 2017 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. © 2017 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Dynamical Downscaling of Seasonal Climate Prediction over Nordeste Brazil with ECHAM3 and NCEP's Regional Spectral Models at IRI.

NASA Astrophysics Data System (ADS)

Nobre, Paulo; Moura, Antonio D.; Sun, Liqiang

2001-12-01

This study presents an evaluation of a seasonal climate forecast done with the International Research Institute for Climate Prediction (IRI) dynamical forecast system (regional model nested into a general circulation model) over northern South America for January-April 1999, encompassing the rainy season over Brazil's Nordeste. The one-way nesting is one in two tiers: first the NCEP's Regional Spectral Model (RSM) runs with an 80-km grid mesh forced by the ECHAM3 atmospheric general circulation model (AGCM) outputs; then the RSM runs with a finer grid mesh (20 km) forced by the forecasts generated by the RSM-80. An ensemble of three realizations is done. Lower boundary conditions over the oceans for both ECHAM and RSM model runs are sea surface temperature forecasts over the tropical oceans. Soil moisture is initialized by ECHAM's inputs. The rainfall forecasts generated by the regional model are compared with those of the AGCM and observations. It is shown that the regional model at 80-km resolution improves upon the AGCM rainfall forecast, reducing both seasonal bias and root-mean-square error. On the other hand, the RSM-20 forecasts presented larger errors, with spatial patterns that resemble those of local topography. The better forecast of the position and width of the intertropical convergence zone (ITCZ) over the tropical Atlantic by the RSM-80 model is one of the principal reasons for better-forecast scores of the RSM-80 relative to the AGCM. The regional model improved the spatial as well as the temporal details of rainfall distribution, and also presenting the minimum spread among the ensemble members. The statistics of synoptic-scale weather variability on seasonal timescales were best forecast with the regional 80-km model over the Nordeste. The possibility of forecasting the frequency distribution of dry and wet spells within the rainy season is encouraging.

Evaluating the ClimEx Single Model large ensemble in comparison with EURO-CORDEX results of heatwave and drought indicators

NASA Astrophysics Data System (ADS)

von Trentini, F.; Schmid, F. J.; Braun, M.; Frigon, A.; Leduc, M.; Martel, J. L.; Willkofer, F.; Wood, R. R.; Ludwig, R.

2017-12-01

Meteorological extreme events seem to become more frequent in the present and future, and a seperation of natural climate variability and a clear climate change effect on these extreme events gains more and more interest. Since there is only one realisation of historical events, natural variability in terms of very long timeseries for a robust statistical analysis is not possible with observation data. A new single model large ensemble (SMLE), developed for the ClimEx project (Climate change and hydrological extreme events - risks and perspectives for water management in Bavaria and Québec) is supposed to overcome this lack of data by downscaling 50 members of the CanESM2 (RCP 8.5) with the Canadian CRCM5 regional model (using the EURO-CORDEX grid specifications) for timeseries of 1950-2099 each, resulting in 7500 years of simulated climate. This allows for a better probabilistic analysis of rare and extreme events than any preceding dataset. Besides seasonal sums, several indicators concerning heatwave frequency, duration and mean temperature a well as number and maximum length of dry periods (cons. days <1mm) are calculated for the ClimEx ensemble and several EURO-CORDEX runs. This enables us to investigate the interaction between natural variability (as it appears in the CanESM2-CRCM5 members) and a climate change signal of those members for past, present and future conditions. Adding the EURO-CORDEX results to this, we can also assess the role of internal model variability (or natural variability) in climate change simulations. A first comparison shows similar magnitudes of variability of climate change signals between the ClimEx large ensemble and the CORDEX runs for some indicators, while for most indicators the spread of the SMLE is smaller than the spread of different CORDEX models.

The UPSCALE project: a large simulation campaign

NASA Astrophysics Data System (ADS)

Mizielinski, Matthew; Roberts, Malcolm; Vidale, Pier Luigi; Schiemann, Reinhard; Demory, Marie-Estelle; Strachan, Jane

2014-05-01

The development of a traceable hierarchy of HadGEM3 global climate models, based upon the Met Office Unified Model, at resolutions from 135 km to 25 km, now allows the impact of resolution on the mean state, variability and extremes of climate to be studied in a robust fashion. In 2011 we successfully obtained a single-year grant of 144 million core hours of supercomputing time from the PRACE organization to run ensembles of 27 year atmosphere-only (HadGEM3-A GA3.0) climate simulations at 25km resolution, as used in present global weather forecasting, on HERMIT at HLRS. Through 2012 the UPSCALE project (UK on PRACE: weather-resolving Simulations of Climate for globAL Environmental risk) ran over 650 years of simulation at resolutions of 25 km (N512), 60 km (N216) and 135 km (N96) to look at the value of high resolution climate models in the study of both present climate and a potential future climate scenario based on RCP8.5. Over 400 TB of data was produced using HERMIT, with additional simulations run on HECToR (UK supercomputer) and MONSooN (Met Office NERC Supercomputing Node). The data generated was transferred to the JASMIN super-data cluster, hosted by STFC CEDA in the UK, where analysis facilities are allowing rapid scientific exploitation of the data set. Many groups across the UK and Europe are already taking advantage of these facilities and we welcome approaches from other interested scientists. This presentation will briefly cover the following points; Purpose and requirements of the UPSCALE project and facilities used. Technical implementation and hurdles (model porting and optimisation, automation, numerical failures, data transfer). Ensemble specification. Current analysis projects and access to the data set. A full description of UPSCALE and the data set generated has been submitted to Geoscientific Model development, with overview information available from http://proj.badc.rl.ac.uk/upscale .

The effectiveness and resilience of phosphorus management practices in the Lake Simcoe watershed, Ontario, Canada

NASA Astrophysics Data System (ADS)

Crossman, J.; Futter, M. N.; Palmer, M.; Whitehead, P. G.; Baulch, H. M.; Woods, D.; Jin, L.; Oni, S. K.; Dillon, P. J.

2016-09-01

Uncertainty surrounding future climate makes it difficult to have confidence that current nutrient management strategies will remain effective. This study used monitoring and modeling to assess current effectiveness (% phosphorus reduction) and resilience (defined as continued effectiveness under a changing climate) of best management practices (BMPs) within five catchments of the Lake Simcoe watershed, Ontario. The Integrated Catchment Phosphorus model (INCA-P) was used, and monitoring data were used to calibrate and validate a series of management scenarios. To assess current BMP effectiveness, models were run over a baseline period 1985-2014 with and without management scenarios. Climate simulations were run (2070-2099), and BMP resilience was calculated as the percent change in effectiveness between the baseline and future period. Results demonstrated that livestock removal from water courses was the most effective BMP, while manure storage adjustments were the least. Effectiveness varied between catchments, influenced by the dominant hydrological and nutrient transport pathways. Resilience of individual BMPs was associated with catchment sensitivity to climate change. BMPs were most resilient in catchments with high soil water storage capacity and small projected changes in frozen-water availability and in soil moisture deficits. Conversely, BMPs were less resilient in catchments with larger changes in spring melt magnitude and in overland flow proportions. Results indicated that BMPs implemented are not always those most suited to catchment flow pathways, and a more site-specific approach would enhance prospects for maintaining P reduction targets. Furthermore, BMP resilience to climate change can be predicted from catchment physical properties and present-day hydrochemical sensitivity to climate forcing.

Climate change and the middle atmosphere. I - The doubled CO2 climate

NASA Technical Reports Server (NTRS)

Rind, D.; Prather, M. J.; Suozzo, R.; Balachandran, N. K.

1990-01-01

The effect of doubling the atmospheric content of CO2 on the middle-atmosphere climate is investigated using the GISS global climate model. In the standard experiment, the CO2 concentration is doubled both in the stratosphere and troposphere, and the SSTs are increased to match those of the doubled CO2 run of the GISS model. Results show that the doubling of CO2 leads to higher temperatures in the troposphere, and lower temperatures in the stratosphere, with a net result being a decrease of static stability for the atmosphere as a whole. The middle atmosphere dynamical differences found were on the order of 10-20 percent of the model values for the current climate. These differences, along with the calculated temperature differences of up to about 10 C, may have a significant impact on the chemistry of the future atmosphere, including that of stratospheric ozone, the polar ozone 'hole', and basic atmospheric composition.

Clouds and ocean-atmosphere interactions. Final report, September 15, 1992--September 14, 1995

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

Randall, D.A.; Jensen, T.G.

1995-10-01

Predictions of global change based on climate models are influencing both national and international policies on energy and the environment. Existing climate models show some skill in simulating the present climate, but suffer from many widely acknowledged deficiencies. Among the most serious problems is the need to apply ``flux corrections`` to prevent the models from drifting away from the observed climate in control runs that do not include external perturbing influences such as increased carbon dioxide (CO{sub 2}) concentrations. The flux corrections required to prevent climate drift are typically comparable in magnitude to the observed fluxes themselves. Although there canmore » be many contributing reasons for the climate drift problem, clouds and their effects on the surface energy budget are among the prime suspects. The authors have conducted a research program designed to investigate global air-sea interaction as it relates to the global warming problem, with special emphasis on the role of clouds. Their research includes model development efforts; application of models to simulation of present and future climates, with comparison to observations wherever possible; and vigorous participation in ongoing efforts to intercompare the present generation of atmospheric general circulation models.« less

Northern Plains Blizzards in Past and Future Climates

NASA Astrophysics Data System (ADS)

Trellinger, A.; Kennedy, A. D.

2017-12-01

High-latitude regions of the globe including the northern tier of the United States are subject to adverse conditions during the winter such as snowstorms. When snowfall combines with strong winds, blizzards can result and these events have significant personal, societal, and economic impacts for the Northern Plains. Although the climatology of wintertime extremes such as blizzards is reasonably understood, it is not known how the frequency and intensity of these events may change in a warming climate. Complicating factors include competing trends that suggest winter will have more snow over this region, but over a shorter seasonal duration. Identifying blizzards in climate models is difficult due to the horizontal and vertical grid spacing used. Additionally, blowing snow is not considered in these models, so it cannot be directly diagnosed. Instead, alternative ways must be developed to identify these events. The presented work will use a competitive neural network known as the Self-Organizing Map (SOM) to identify meteorological patterns associated with blizzard events over the Northern Plains from 1979-2016. Once these large-scale patterns are identified from observations, they will be identified in Community Climate System Model (CESM) 4.0 20th Century forcing climate simulations run in support for the Coupled Model Intercomparison Project Phase 5 (CMIP-5). In specific, the methodology will rely on the `Mother of All Runs' (MOAR) ensemble member. Because this member provides subdaily output for many variables, specific meteorological patterns can be identified. Blizzard events will be identified during historical time periods to determine biases, and then under future emissions scenarios.

Reproduction of 20th century inter- to multi-decadel surface temperature variablilty in radiatively forced coupled climate models

USDA-ARS?s Scientific Manuscript database

Coupled Model Intercomparison Project 3 simulations of surface temperature were evaluated over the period 1902-1999 to assess their ability to reproduce historical temperature variability at 211 global locations. Model performance was evaluated using the running Mann Whitney-Z method, a technique th...

Toward a chemical reanalysis in a coupled chemistry-climate model: An evaluation of MOPITT CO assimilation and its impact on tropospheric composition

NASA Astrophysics Data System (ADS)

Gaubert, B.; Arellano, A. F.; Barré, J.; Worden, H. M.; Emmons, L. K.; Tilmes, S.; Buchholz, R. R.; Vitt, F.; Raeder, K.; Collins, N.; Anderson, J. L.; Wiedinmyer, C.; Martinez Alonso, S.; Edwards, D. P.; Andreae, M. O.; Hannigan, J. W.; Petri, C.; Strong, K.; Jones, N.

2016-06-01

We examine in detail a 1 year global reanalysis of carbon monoxide (CO) that is based on joint assimilation of conventional meteorological observations and Measurement of Pollution in The Troposphere (MOPITT) multispectral CO retrievals in the Community Earth System Model (CESM). Our focus is to assess the impact to the chemical system when CO distribution is constrained in a coupled full chemistry-climate model like CESM. To do this, we first evaluate the joint reanalysis (MOPITT Reanalysis) against four sets of independent observations and compare its performance against a reanalysis with no MOPITT assimilation (Control Run). We then investigate the CO burden and chemical response with the aid of tagged sectoral CO tracers. We estimate the total tropospheric CO burden in 2002 (from ensemble mean and spread) to be 371 ± 12% Tg for MOPITT Reanalysis and 291 ± 9% Tg for Control Run. Our multispecies analysis of this difference suggests that (a) direct emissions of CO and hydrocarbons are too low in the inventory used in this study and (b) chemical oxidation, transport, and deposition processes are not accurately and consistently represented in the model. Increases in CO led to net reduction of OH and subsequent longer lifetime of CH4 (Control Run: 8.7 years versus MOPITT Reanalysis: 9.3 years). Yet at the same time, this increase led to 5-10% enhancement of Northern Hemisphere O3 and overall photochemical activity via HOx recycling. Such nonlinear effects further complicate the attribution to uncertainties in direct emissions alone. This has implications to chemistry-climate modeling and inversion studies of longer-lived species.

High Resolution Nature Runs and the Big Data Challenge

NASA Technical Reports Server (NTRS)

Webster, W. Phillip; Duffy, Daniel Q.

2015-01-01

NASA's Global Modeling and Assimilation Office at Goddard Space Flight Center is undertaking a series of very computationally intensive Nature Runs and a downscaled reanalysis. The nature runs use the GEOS-5 as an Atmospheric General Circulation Model (AGCM) while the reanalysis uses the GEOS-5 in Data Assimilation mode. This paper will present computational challenges from three runs, two of which are AGCM and one is downscaled reanalysis using the full DAS. The nature runs will be completed at two surface grid resolutions, 7 and 3 kilometers and 72 vertical levels. The 7 km run spanned 2 years (2005-2006) and produced 4 PB of data while the 3 km run will span one year and generate 4 BP of data. The downscaled reanalysis (MERRA-II Modern-Era Reanalysis for Research and Applications) will cover 15 years and generate 1 PB of data. Our efforts to address the big data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS), a specialization of the concept of business process-as-a-service that is an evolving extension of IaaS, PaaS, and SaaS enabled by cloud computing. In this presentation, we will describe two projects that demonstrate this shift. MERRA Analytic Services (MERRA/AS) is an example of cloud-enabled CAaaS. MERRA/AS enables MapReduce analytics over MERRA reanalysis data collection by bringing together the high-performance computing, scalable data management, and a domain-specific climate data services API. NASA's High-Performance Science Cloud (HPSC) is an example of the type of compute-storage fabric required to support CAaaS. The HPSC comprises a high speed Infinib and network, high performance file systems and object storage, and a virtual system environments specific for data intensive, science applications. These technologies are providing a new tier in the data and analytic services stack that helps connect earthbound, enterprise-level data and computational resources to new customers and new mobility-driven applications and modes of work. In our experience, CAaaS lowers the barriers and risk to organizational change, fosters innovation and experimentation, and provides the agility required to meet our customers' increasing and changing needs

Technical Note: On the use of nudging for aerosol–climate model intercomparison studies

DOE PAGES

Zhang, K.; Wan, H.; Liu, X.; ...

2014-08-26

Nudging as an assimilation technique has seen increased use in recent years in the development and evaluation of climate models. Constraining the simulated wind and temperature fields using global weather reanalysis facilitates more straightforward comparison between simulation and observation, and reduces uncertainties associated with natural variabilities of the large-scale circulation. On the other hand, the forcing introduced by nudging can be strong enough to change the basic characteristics of the model climate. In the paper we show that for the Community Atmosphere Model version 5 (CAM5), due to the systematic temperature bias in the standard model and the sensitivity ofmore » simulated ice formation to anthropogenic aerosol concentration, nudging towards reanalysis results in substantial reductions in the ice cloud amount and the impact of anthropogenic aerosols on long-wave cloud forcing. In order to reduce discrepancies between the nudged and unconstrained simulations, and meanwhile take the advantages of nudging, two alternative experimentation methods are evaluated. The first one constrains only the horizontal winds. The second method nudges both winds and temperature, but replaces the long-term climatology of the reanalysis by that of the model. Results show that both methods lead to substantially improved agreement with the free-running model in terms of the top-of-atmosphere radiation budget and cloud ice amount. The wind-only nudging is more convenient to apply, and provides higher correlations of the wind fields, geopotential height and specific humidity between simulation and reanalysis. Results from both CAM5 and a second aerosol–climate model ECHAM6-HAM2 also indicate that compared to the wind-and-temperature nudging, constraining only winds leads to better agreement with the free-running model in terms of the estimated shortwave cloud forcing and the simulated convective activities. This suggests nudging the horizontal winds but not temperature is a good strategy for the investigation of aerosol indirect effects since it provides well-constrained meteorology without strongly perturbing the model's mean climate.« less

Technical Note: On the use of nudging for aerosol-climate model intercomparison studies

NASA Astrophysics Data System (ADS)

Zhang, K.; Wan, H.; Liu, X.; Ghan, S. J.; Kooperman, G. J.; Ma, P.-L.; Rasch, P. J.; Neubauer, D.; Lohmann, U.

2014-08-01

Nudging as an assimilation technique has seen increased use in recent years in the development and evaluation of climate models. Constraining the simulated wind and temperature fields using global weather reanalysis facilitates more straightforward comparison between simulation and observation, and reduces uncertainties associated with natural variabilities of the large-scale circulation. On the other hand, the forcing introduced by nudging can be strong enough to change the basic characteristics of the model climate. In the paper we show that for the Community Atmosphere Model version 5 (CAM5), due to the systematic temperature bias in the standard model and the sensitivity of simulated ice formation to anthropogenic aerosol concentration, nudging towards reanalysis results in substantial reductions in the ice cloud amount and the impact of anthropogenic aerosols on long-wave cloud forcing. In order to reduce discrepancies between the nudged and unconstrained simulations, and meanwhile take the advantages of nudging, two alternative experimentation methods are evaluated. The first one constrains only the horizontal winds. The second method nudges both winds and temperature, but replaces the long-term climatology of the reanalysis by that of the model. Results show that both methods lead to substantially improved agreement with the free-running model in terms of the top-of-atmosphere radiation budget and cloud ice amount. The wind-only nudging is more convenient to apply, and provides higher correlations of the wind fields, geopotential height and specific humidity between simulation and reanalysis. Results from both CAM5 and a second aerosol-climate model ECHAM6-HAM2 also indicate that compared to the wind-and-temperature nudging, constraining only winds leads to better agreement with the free-running model in terms of the estimated shortwave cloud forcing and the simulated convective activities. This suggests nudging the horizontal winds but not temperature is a good strategy for the investigation of aerosol indirect effects since it provides well-constrained meteorology without strongly perturbing the model's mean climate.

Simulated trends of extreme climate indices for the Carpathian basin using outputs of different regional climate models

NASA Astrophysics Data System (ADS)

Pongracz, R.; Bartholy, J.; Szabo, P.; Pieczka, I.; Torma, C. S.

2009-04-01

Regional climatological effects of global warming may be recognized not only in shifts of mean temperature and precipitation, but in the frequency or intensity changes of different climate extremes. Several climate extreme indices are analyzed and compared for the Carpathian basin (located in Central/Eastern Europe) following the guidelines suggested by the joint WMO-CCl/CLIVAR Working Group on climate change detection. Our statistical trend analysis includes the evaluation of several extreme temperature and precipitation indices, e.g., the numbers of severe cold days, winter days, frost days, cold days, warm days, summer days, hot days, extremely hot days, cold nights, warm nights, the intra-annual extreme temperature range, the heat wave duration, the growing season length, the number of wet days (using several threshold values defining extremes), the maximum number of consecutive dry days, the highest 1-day precipitation amount, the greatest 5-day rainfall total, the annual fraction due to extreme precipitation events, etc. In order to evaluate the future trends (2071-2100) in the Carpathian basin, daily values of meteorological variables are obtained from the outputs of various regional climate model (RCM) experiments accomplished in the frame of the completed EU-project PRUDENCE (Prediction of Regional scenarios and Uncertainties for Defining EuropeaN Climate change risks and Effects). Horizontal resolution of the applied RCMs is 50 km. Both scenarios A2 and B2 are used to compare past and future trends of the extreme climate indices for the Carpathian basin. Furthermore, fine-resolution climate experiments of two additional RCMs adapted and run at the Department of Meteorology, Eotvos Lorand University are used to extend the trend analysis of climate extremes for the Carpathian basin. (1) Model PRECIS (run at 25 km horizontal resolution) was developed at the UK Met Office, Hadley Centre, and it uses the boundary conditions from the HadCM3 GCM. (2) Model RegCM3 (run at 10 km horizontal resolution) was developed by Giorgi et al. and it is available from the ICTP (International Centre for Theoretical Physics). Analysis of the simulated daily temperature datasets suggests that the detected regional warming is expected to continue in the 21st century. Cold temperature extremes are projected to decrease while warm extremes tend to increase significantly. Expected changes of annual precipitation indices are small, but generally consistent with the detected trends of the 20th century. Based on the simulations, extreme precipitation events are expected to become more intense and more frequent in winter, while a general decrease of extreme precipitation indices is expected in summer.

Pinatubo Emulation in Multiple Models (POEMs): co-ordinated experiments in the ISA-MIP model intercomparison activity component of the SPARC Stratospheric Sulphur and it's Role in Climate initiative (SSiRC)

NASA Astrophysics Data System (ADS)

Lee, Lindsay; Mann, Graham; Carslaw, Ken; Toohey, Matthew; Aquila, Valentina

2016-04-01

The World Climate Research Program's SPARC initiative has a new international activity "Stratospheric Sulphur and its Role in Climate" (SSiRC) to better understand changes in stratospheric aerosol and precursor gaseous sulphur species. One component of SSiRC involves an intercomparison "ISA-MIP" of composition-climate models that simulate the stratospheric aerosol layer interactively. Within PoEMS each modelling group will run a "perturbed physics ensemble" (PPE) of interactive stratospheric aerosol (ISA) simulations of the Pinatubo eruption, varying several uncertain parameters associated with the eruption's SO2 emissions and model processes. A powerful new technique to quantify and attribute sources of uncertainty in complex global models is described by Lee et al. (2011, ACP). The analysis uses Gaussian emulation to derive a probability density function (pdf) of predicted quantities, essentially interpolating the PPE results in multi-dimensional parameter space. Once trained on the ensemble, a Monte Carlo simulation with the fast Gaussian emulator enabling a full variance-based sensitivity analysis. The approach has already been used effectively by Carslaw et al., (2013, Nature) to quantify the uncertainty in the cloud albedo effect forcing from a 3D global aerosol-microphysics model allowing to compare the sensitivy of different predicted quantities to uncertainties in natural and anthropogenic emissions types, and structural parameters in the models. Within ISA-MIP, each group will carry out a PPE of runs, with the subsequent analysis with the emulator assessing the uncertainty in the volcanic forcings predicted by each model. In this poster presentation we will give an outline of the "PoEMS" analysis, describing the uncertain parameters to be varied and the relevance to further understanding differences identified in previous international stratospheric aerosol assessments.

JPL-20171130-EARTHf-0001-DIY Glacier Modeling with Virtual Earth System Laboratory

NASA Image and Video Library

2017-11-30

Eric Larour, JPL Climate Scientist, explains the NASA research tool "VESL" -- Virtual Earth System Laboratory -- that allows anyone to run their own climate experiment. The user can use a slider to simulate and increase or decrease in the amount of snowfall on a particular glacier then see a video of the results, including the glacier melting's effect on sea level.

How important is interannual variability in the climatic interpretation of moraine sequences?

NASA Astrophysics Data System (ADS)

Leonard, E. M.; Laabs, B. J. C.; Plummer, M. A.

2017-12-01

Mountain glaciers respond to both long-term climate and interannual forcing. Anderson et al. (2014) pointed out that kilometer-scale fluctuations in glacier length may result from interannual variability in temperature and precipitation given a "steady" climate with no long-term trends in mean or variability of temperature and precipitation. They cautioned that use of outermost moraines from the Last Glacial Maximum (LGM) as indicators of LGM climate will, because of the role of interannual forcing, result in overestimation of the magnitude of long-term temperature depression and/or precipitation enhancement. Here we assess the implications of these ideas, by examining the effect of interannual variability on glacier length and inferred magnitude of LGM climate change from present under both an assumed steady LGM climate and an LGM climate with low-magnitude, long-period variation in summer temperature and annual precipitation. We employ both the original 1-stage linear glacier model (Roe and O'Neal, 2009) used by Anderson et al. (2014) and a newer 3-stage linear model (Roe and Baker, 2014). We apply the models to two reconstructed LGM glaciers in the Colorado Sangre de Cristo Mountains. Three-stage-model results indicate that, absent long-term variations through a 7500-year-long LGM, interannual variability would result in overestimation of mean LGM temperature depression from the outermost moraine of 0.2-0.6°C. If small long-term cyclic variations of temperature (±0.5°C) and precipitation (±5%) are introduced, the overestimation of LGM temperature depression reduces to less than 0.4°C, and if slightly greater long-term variation (±1.0°C and ±10% precipitation) is introduced, the magnitude of overestimation is 0.3°C or less. Interannual variability may produce a moraine sequence that differs from the sequence that would be expected were glacier length forced only by long-term climate. With small amplitude (±0.5°C and ±5% precipitation) long-term variation, the moraine sequence expected if forced by a combination of interannual variability and long-term climate differs from that expected based on long-term climate forcing alone in 38% of model runs. With the larger amplitude long-term forcing (±1.0°C and ±10% precipitation) this difference occurs in 20% of model runs.

Global Warming and Northern Hemisphere Sea Ice Extent.

PubMed

Vinnikov; Robock; Stouffer; Walsh; Parkinson; Cavalieri; Mitchell; Garrett; Zakharov

1999-12-03

Surface and satellite-based observations show a decrease in Northern Hemisphere sea ice extent during the past 46 years. A comparison of these trends to control and transient integrations (forced by observed greenhouse gases and tropospheric sulfate aerosols) from the Geophysical Fluid Dynamics Laboratory and Hadley Centre climate models reveals that the observed decrease in Northern Hemisphere sea ice extent agrees with the transient simulations, and both trends are much larger than would be expected from natural climate variations. From long-term control runs of climate models, it was found that the probability of the observed trends resulting from natural climate variability, assuming that the models' natural variability is similar to that found in nature, is less than 2 percent for the 1978-98 sea ice trends and less than 0.1 percent for the 1953-98 sea ice trends. Both models used here project continued decreases in sea ice thickness and extent throughout the next century.

Do climate model predictions agree with long-term precipitation trends in the arid southwestern United States?

NASA Astrophysics Data System (ADS)

Elias, E.; Rango, A.; James, D.; Maxwell, C.; Anderson, J.; Abatzoglou, J. T.

2016-12-01

Researchers evaluating climate projections across southwestern North America observed a decreasing precipitation trend. Aridification was most pronounced in the cold (non-monsoonal) season, whereas downward trends in precipitation were smaller in the warm (monsoonal) season. In this region, based upon a multimodel mean of 20 Coupled Model Intercomparison Project 5 models using a business-as-usual (Representative Concentration Pathway 8.5) trajectory, midcentury precipitation is projected to increase slightly during the monsoonal time period (July-September; 6%) and decrease slightly during the remainder of the year (October-June; -4%). We use observed long-term (1915-2015) monthly precipitation records from 16 weather stations to investigate how well measured trends corroborate climate model predictions during the monsoonal and non-monsoonal timeframe. Running trend analysis using the Mann-Kendall test for 15 to 101 year moving windows reveals that half the stations showed significant (p≤0.1), albeit small, increasing trends based on the longest term record. Trends based on shorter-term records reveal a period of significant precipitation decline at all stations representing the 1950s drought. Trends from 1930 to 2015 reveal significant annual, monsoonal and non-monsoonal increases in precipitation (Fig 1). The 1960 to 2015 time window shows no significant precipitation trends. The more recent time window (1980 to 2015) shows a slight, but not significant, increase in monsoonal precipitation and a larger, significant decline in non-monsoonal precipitation. GCM precipitation projections are consistent with more recent trends for the region. Running trends from the most recent time window (mid-1990s to 2015) at all stations show increasing monsoonal precipitation and decreasing Oct-Jun precipitation, with significant trends at 6 of 16 stations. Running trend analysis revealed that the long-term trends were not persistent throughout the series length, but depended on the period examined. Recent trends in Southwest precipitation are directionally consistent with anthropogenic climate change.

An Assessment of IPCC 20th Century Climate Simulations Using the 15-year Sea Level Record from Altimetry

NASA Astrophysics Data System (ADS)

Leuliette, E.; Nerem, S.; Jakub, T.

2006-07-01

Recen tly, multiple ensemble climate simulations h ave been produced for th e forthco ming Fourth A ssessment Report of the Intergovernmental Panel on Climate Change (IPCC). N early two dozen coupled ocean- atmo sphere models have contr ibuted output for a variety of climate scen arios. One scenar io, the climate of the 20th century exper imen t (20C3 M), produces model output that can be comp ared to th e long record of sea level provided by altimetry . Generally , the output from the 20C3M runs is used to initialize simulations of future climate scenar ios. Hence, v alidation of the 20 C3 M experiment resu lts is crucial to the goals of th e IPCC. We present compar isons of global mean sea level (G MSL) , global mean steric sea level change, and regional patterns of sea lev el chang e from these models to r esults from altimetry, tide gauge measurements, and reconstructions.

Climate Science Performance, Data and Productivity on Titan

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

Mayer, Benjamin W; Worley, Patrick H; Gaddis, Abigail L

2015-01-01

Climate Science models are flagship codes for the largest of high performance computing (HPC) resources, both in visibility, with the newly launched Department of Energy (DOE) Accelerated Climate Model for Energy (ACME) effort, and in terms of significant fractions of system usage. The performance of the DOE ACME model is captured with application level timers and examined through a sizeable run archive. Performance and variability of compute, queue time and ancillary services are examined. As Climate Science advances in the use of HPC resources there has been an increase in the required human and data systems to achieve programs goals.more » A description of current workflow processes (hardware, software, human) and planned automation of the workflow, along with historical and projected data in motion and at rest data usage, are detailed. The combination of these two topics motivates a description of future systems requirements for DOE Climate Modeling efforts, focusing on the growth of data storage and network and disk bandwidth required to handle data at an acceptable rate.« less

Climate change impact modelling needs to include cross-sectoral interactions

NASA Astrophysics Data System (ADS)

Harrison, Paula A.; Dunford, Robert W.; Holman, Ian P.; Rounsevell, Mark D. A.

2016-09-01

Climate change impact assessments often apply models of individual sectors such as agriculture, forestry and water use without considering interactions between these sectors. This is likely to lead to misrepresentation of impacts, and consequently to poor decisions about climate adaptation. However, no published research assesses the differences between impacts simulated by single-sector and integrated models. Here we compare 14 indicators derived from a set of impact models run within single-sector and integrated frameworks across a range of climate and socio-economic scenarios in Europe. We show that single-sector studies misrepresent the spatial pattern, direction and magnitude of most impacts because they omit the complex interdependencies within human and environmental systems. The discrepancies are particularly pronounced for indicators such as food production and water exploitation, which are highly influenced by other sectors through changes in demand, land suitability and resource competition. Furthermore, the discrepancies are greater under different socio-economic scenarios than different climate scenarios, and at the sub-regional rather than Europe-wide scale.

Future Projections of Air Temperature and Precipitation for the CORDEX-MENA Domain by Using RegCM4.3.5

NASA Astrophysics Data System (ADS)

Ozturk, Tugba; Turp, M. Tufan; Türkeş, Murat; Kurnaz, M. Levent

2015-04-01

In this study, the projected changes for the periods of 2016 - 2035, 2046 - 2065, and 2081 - 2100 in the seasonal averages of air temperature and precipitation variables with respect to the reference period of 1981 - 2000 were examined for the Middle East and North Africa region. In this context, Regional Climate Model (RegCM4.3.5) of ICTP (International Centre for Theoretical Physics) was run by using two different global climate models. MPI-ESM-MR global climate model of the Max Planck Institute for Meteorology and HadGEM2 of the Met Office Hadley Centre were dynamically downscaled to 50 km for the CORDEX-MENA domain. The projections were realized according to the RCP4.5 and the RCP8.5 emission scenarios of the IPCC (Intergovernmental Panel of Climate Change).

Hydroclimatic trends in simulations over the CORDEX North America region

NASA Astrophysics Data System (ADS)

Arritt, Raymond; Groisman, Pavel; Daniel, Ariele; Schillerberg, Tayler

2015-04-01

An increase in the occurrence of heavy precipitation has been one of the most pronounced climate change signals for the central United States. We study this trend by using the RegCM4 regional climate model to dynamically downscale CMIP5 global projections for 1950-2099 over the CORDEX North America domain. We examine the robustness of the results by driving the regional model with two different global models, by performing simulations at both 50 km and 25 km grid spacing, and by using different convective parameterizations in RegCM4. The global models sample the range of climate sensitivity in CMIP5: HadGEM2-ES has the highest equilibrium climate sensitivity of the CMIP5 models, while GFDL-ESM2M has one of the lowest sensitivities. RegCM4 results show increases in heavy precipitation (> 50 mm/day) over the central United States for the period 1951-2005 similar to observed trends. This trend is predicted to accelerate so that by the end of the 21st century incidence of heavy precipitation increases by a factor of 2 to 3. The trend is robust in that it is produced regardless of the driving global model or the configuration of the regional model. Results also show a modest increase in the number of dry days and a marked increase in the number of long runs of dry days (16 or more consecutive dry days). The combination of heavier events and longer runs of dry days has implications for sectors such as agriculture and water quality. This research was sponsored by USDA NIFA under the Earth System Modeling program and as part of a regional collaborative project.

Will climate change increase irrigation requirements in agriculture of Central Europe? A simulation study for Northern Germany.

PubMed

Riediger, Jan; Breckling, Broder; Nuske, Robert S; Schröder, Winfried

2014-01-01

By example of a region in Northern Germany (County of Uelzen), this study investigates whether climate change is likely to require adaption of agricultural practices such as irrigation in Central Europe. Due to sandy soils with low water retention capacity and occasional insufficient rainfall, irrigation is a basic condition for agricultural production in the county of Uelzen. Thus, in the framework of the comprehensive research cluster Nachhaltiges Landmanagement im Norddeutschen Tiefland ( NaLaMa-nT ), we investigated whether irrigation might need to be adapted to changing climatic conditions. To this end, results from regionalised climate change modelling were coupled with soil- and crop-specific evapotranspiration models to calculate potential amounts of irrigation to prevent crop failures. Three different runs of the climate change scenario RCP 8.5 were used for the time period until 2070. The results show that the extent of probable necessary irrigation will likely increase in the future. For the scenario run with the highest temperature rise, the results suggest that the amount of ground water presently allowed to be extracted for irrigation might not be sufficient in the future to retain common agricultural pattern. The investigation at hand exemplifies data requirements and methods to estimate irrigation needs under climate change conditions. Restriction of ground water withdrawal by German environmental regulation may require an adaptation of crop selection and alterations in agricultural practice also in regions with comparable conditions.

Climate Sensitivity Runs and Regional Hydrologic Modeling for Predicting the Response of the Greater Florida Everglades Ecosystem to Climate Change

NASA Astrophysics Data System (ADS)

Obeysekera, Jayantha; Barnes, Jenifer; Nungesser, Martha

2015-04-01

It is important to understand the vulnerability of the water management system in south Florida and to determine the resilience and robustness of greater Everglades restoration plans under future climate change. The current climate models, at both global and regional scales, are not ready to deliver specific climatic datasets for water resources investigations involving future plans and therefore a scenario based approach was adopted for this first study in restoration planning. We focused on the general implications of potential changes in future temperature and associated changes in evapotranspiration, precipitation, and sea levels at the regional boundary. From these, we developed a set of six climate and sea level scenarios, used them to simulate the hydrologic response of the greater Everglades region including agricultural, urban, and natural areas, and compared the results to those from a base run of current conditions. The scenarios included a 1.5 °C increase in temperature, ±10 % change in precipitation, and a 0.46 m (1.5 feet) increase in sea level for the 50-year planning horizon. The results suggested that, depending on the rainfall and temperature scenario, there would be significant changes in water budgets, ecosystem performance, and in water supply demands met. The increased sea level scenarios also show that the ground water levels would increase significantly with associated implications for flood protection in the urbanized areas of southeastern Florida.

A simple object-oriented and open-source model for scientific and policy analyses of the global climate system – Hector v1.0

DOE PAGES

Hartin, Corinne A.; Patel, Pralit L.; Schwarber, Adria; ...

2015-04-01

Simple climate models play an integral role in the policy and scientific communities. They are used for climate mitigation scenarios within integrated assessment models, complex climate model emulation, and uncertainty analyses. Here we describe Hector v1.0, an open source, object-oriented, simple global climate carbon-cycle model. This model runs essentially instantaneously while still representing the most critical global-scale earth system processes. Hector has a three-part main carbon cycle: a one-pool atmosphere, land, and ocean. The model's terrestrial carbon cycle includes primary production and respiration fluxes, accommodating arbitrary geographic divisions into, e.g., ecological biomes or political units. Hector actively solves the inorganicmore » carbon system in the surface ocean, directly calculating air–sea fluxes of carbon and ocean pH. Hector reproduces the global historical trends of atmospheric [CO 2], radiative forcing, and surface temperatures. The model simulates all four Representative Concentration Pathways (RCPs) with equivalent rates of change of key variables over time compared to current observations, MAGICC (a well-known simple climate model), and models from the 5th Coupled Model Intercomparison Project. Hector's flexibility, open-source nature, and modular design will facilitate a broad range of research in various areas.« less

Using MERRA, AMIP II, CMIP5 Outputs to Assess Actual and Potential Building Climate Zone Change and Variability From the Last 30 Years Through 2100

NASA Astrophysics Data System (ADS)

Stackhouse, P. W.; Westberg, D. J.; Hoell, J. M., Jr.; Chandler, W.; Zhang, T.

2014-12-01

In the US, residential and commercial building infrastructure combined consumes about 40% of total energy usage and emits about 39% of total CO2emission (DOE/EIA "Annual Energy Outlook 2013"). Thus, increasing the energy efficiency of buildings is paramount to reducing energy costs and emissions. 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 climates zones are based upon a 30-year average of local surface observations and are developed by DOE and ASHRAE (formerly known as the American Society of Hearting, Refrigeration and Air-Conditioning Engineers). A significant shortcoming of the methodology used in constructing such maps is the use of surface observations (located mainly near airports) that are unequally distributed and frequently have periods of missing data that need to be filled by various approximation schemes. This paper demonstrates the usefulness of using NASA's Modern Era Retrospective-analysis for Research and Applications (MERRA) atmospheric data assimilation to derive the ASHRAE climate zone maps and then using MERRA to define the last 30 years of variability in climate zones. These results show that there is a statistically significant increase in the area covered by warmer climate zones and some tendency for a reduction of area in colder climate zones that require longer time series to confirm. Using the uncertainties of the basic surface temperature and precipitation parameters from MERRA as determined by comparison to surface measurements, we first compare patterns and variability of ASHRAE climate zones from MERRA relative to present day climate model runs from AMIP simulations to establish baseline sensitivity. Based upon these results, we assess the variability of the ASHRAE climate zones according to CMIP runs through 2100 using an ensemble analysis that classifies model output changes by percentiles. Estimates of statistical significance are then compared to original model variability during the AMIP period. This work quantifies and tests for significance the changes seen in the various US regions that represent a potential contribution by NASA to the ongoing National Climate Assessment.

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.

Seasonal-to-decadal predictability in the Nordic Seas and Arctic with the Norwegian Climate Prediction Model

NASA Astrophysics Data System (ADS)

Counillon, Francois; Kimmritz, Madlen; Keenlyside, Noel; Wang, Yiguo; Bethke, Ingo

2017-04-01

The Norwegian Climate Prediction Model combines the Norwegian Earth System Model and the Ensemble Kalman Filter data assimilation method. The prediction skills of different versions of the system (with 30 members) are tested in the Nordic Seas and the Arctic region. Comparing the hindcasts branched from a SST-only assimilation run with a free ensemble run of 30 members, we are able to dissociate the predictability rooted in the external forcing from the predictability harvest from SST derived initial conditions. The latter adds predictability in the North Atlantic subpolar gyre and the Nordic Seas regions and overall there is very little degradation or forecast drift. Combined assimilation of SST and T-S profiles further improves the prediction skill in the Nordic Seas and into the Arctic. These lead to multi-year predictability in the high-latitudes. Ongoing developments of strongly coupled assimilation (ocean and sea ice) of ice concentration in idealized twin experiment will be shown, as way to further enhance prediction skill in the Arctic.

Understanding climate variability and global climate change using high-resolution GCM simulations

NASA Astrophysics Data System (ADS)

Feng, Xuelei

In this study, three climate processes are examined using long-term simulations from multiple climate models with increasing horizontal resolutions. These simulations include the European Center for Medium-range Weather Forecasts (ECMWF) atmospheric general circulation model (AGCM) runs forced with observed sea surface temperatures (SST) (the Athena runs) and a set of coupled ocean-atmosphere seasonal hindcasts (the Minerva runs). Both sets of runs use different AGCM resolutions, the highest at 16 km. A pair of the Community Climate System Model (CCSM) simulations with ocean general circulation model (OGCM) resolutions at 100 and 10 km are also examined. The higher resolution CCSM run fully resolves oceanic mesoscale eddies. The resolution influence on the precipitation climatology over the Gulf Stream (GS) region is first investigated. In the Athena simulations, the resolution increase generates enhanced mean GS precipitation moderately in both large-scale and sub-scale rainfalls in the North Atlantic, with the latter more tightly confined near the oceanic front. However, the non-eddy resolving OGCM in the Minerva runs simulates a weaker oceanic front and weakens the mean GS precipitation response. On the other hand, an increase in CCSM oceanic resolutions from non-eddy-resolving to eddy resolving regimes greatly improves the model's GS precipitation climatology, resulting in both stronger intensity and more realistic structure. Further analyses show that the improvement of the GS precipitation climatology due to resolution increases is caused by the enhanced atmospheric response to an increased SST gradient near the oceanic front, which leads to stronger surface convergence and upper level divergence. Another focus of this study is on the global warming impacts on precipitation characteristic changes using the high-resolution Athena simulations under the SST forcing from the observations and a global warming scenario. As a comparison, results from the coarse resolution simulation are also analyzed to examine the dependence on resolution. The increasing rates of globally averaged precipitation amount for the high and low resolution simulations are 1.7%/K-1 and 1.8%/K-1, respectively. The sensitivities for heavy, moderate, light and drizzle rain are 6.8, -1.2, 0.0, 0.2%/K-1 for low and 6.3, -1.5, 0.4, -0.2%/K -1 for high resolution simulations. The number of rainy days decreases in a warming scenario, by 3.4 and 4.2 day/year-1, respectively. The most sensitive response of 6.3-6.8%/K-1 for the heavy rain approaches that of the 7%/K-1 for the Clausius-Clapeyron scaling limit. During the twenty-first century simulation, the increases in precipitation are larger over high latitude and wet regions in low and mid-latitudes. Over the dry regions, such as the subtropics, the precipitation amount and frequency decrease. There is a higher occurrence of low and heavy rain from the tropics to mid-latitudes at the expense of the decreases in the frequency of moderate rain. In the third part, the inter-annual variability of the northern hemisphere storm tracks is examined. In the Athena simulations, the leading modes of the observed storm track variability are reproduced realistically by all runs. In general, the fluctuations of the model storm tracks in the North Pacific and Atlantic basins are largely independent of each other. Within each basin, the variations are characterized by the intensity change near the climatological center and the meridional shift of the storm track location. These two modes are associated with major teleconnection patterns of the low frequency atmospheric variations. These model results are not sensitive to resolution. Using the Minerva hindcast initialized in November, it is shown that a portion of the winter (December-January) storm track variability is predictable, mainly due to the influences of the atmospheric wave trains induced by the El Nino and Southern Oscillation.

Influence of projected snow and sea-ice changes on future climate in heavy snowfall region

NASA Astrophysics Data System (ADS)

Matsumura, S.; Sato, T.

2011-12-01

Snow/ice albedo and cloud feedbacks are critical for climate change projection in cryosphere regions. However, future snow and sea-ice distributions are significantly different in each GCM. Thus, surface albedo in cryosphere regions is one of the causes of the uncertainty for climate change projection. Northern Japan is one of the heaviest snowfall regions in the world. In particular, Hokkaido is bounded on the north by the Okhotsk Sea, where is the southernmost ocean in the Northern Hemisphere that is covered with sea ice during winter. Wintertime climate around Hokkaido is highly sensitive to fluctuations in snow and sea-ice. The purpose of this study is to evaluate the influence of global warming on future climate around Hokkaido, using the Pseudo-Global-Warming method (PGW) by a regional climate model. The boundary conditions of the PGW run were obtained by adding the difference between the future (2090s) and past (1990s) climates simulated by coupled general circulation model (MIROC3.2 medres), which is from the CMIP3 multi-model dataset, into the 6-hourly NCEP reanalysis (R-2) and daily OISST data in the past climate (CTL) run. The PGW experiments show that snow depth significantly decreases over mountainous areas and snow cover mainly decreases over plain areas, contributing to higher surface warming due to the decreased snow albedo. Despite the snow reductions, precipitation mainly increases over the mountainous areas because of enhanced water vapor content. However, precipitation decreases over the Japan Sea and the coastal areas, indicating the weakening of a convergent cloud band, which is formed by convergence between cold northwesteries from the Eurasian continent and anticyclonic circulation over the Okhotsk Sea. These results suggest that Okhotsk sea-ice decline may change the atmospheric circulation and the resulting effect on cloud formation, resulting in changes in winter snow or precipitation. We will also examine another CMIP3 model (MRI-CGCM2.3.2), which sensitivity of surface albedo to surface air temperature is the lowest in the CMIP3 models.

GEOSS AIP-2 Climate Change and Biodiversity Use Scenarios: Interoperability Infrastructures (Invited)

NASA Astrophysics Data System (ADS)

Nativi, S.; Santoro, M.

2009-12-01

Currently, one of the major challenges for scientific community is the study of climate change effects on life on Earth. To achieve this, it is crucial to understand how climate change will impact on biodiversity and, in this context, several application scenarios require modeling the impact of climate change on distribution of individual species. In the context of GEOSS AIP-2 (Global Earth Observation System of Systems, Architecture Implementation Pilot- Phase 2), the Climate Change & Biodiversity thematic Working Group developed three significant user scenarios. A couple of them make use of a GEOSS-based framework to study the impact of climate change factors on regional species distribution. The presentation introduces and discusses this framework which provides an interoperability infrastructures to loosely couple standard services and components to discover and access climate and biodiversity data, and run forecast and processing models. The framework is comprised of the following main components and services: a)GEO Portal: through this component end user is able to search, find and access the needed services for the scenario execution; b)Graphical User Interface (GUI): this component provides user interaction functionalities. It controls the workflow manager to perform the required operations for the scenario implementation; c)Use Scenario controller: this component acts as a workflow controller implementing the scenario business process -i.e. a typical climate change & biodiversity projection scenario; d)Service Broker implementing Mediation Services: this component realizes a distributed catalogue which federates several discovery and access components (exposing them through a unique CSW standard interface). Federated components publish climate, environmental and biodiversity datasets; e)Ecological Niche Model Server: this component is able to run one or more Ecological Niche Models (ENM) on selected biodiversity and climate datasets; f)Data Access Transaction server: this component publishes the model outputs. The framework was successfully tested in two use scenarios of the GEOSS AIP-2 Climate Change and Biodiversity WG aiming to predict species distribution changes due to Climate Change factors, with the scientific patronage of the University of Colorado and the University of Alaska. The first scenario dealt with the Pikas specie regional distribution in the Great Basin area (North America). While, the second one concerned the modeling of the Arctic Food Chain species in the North Pole area -the relationships between different environmental parameters and Polar Bears distribution was analyzed. Results are published in the GEOSS AIP-2 web site: http://www.ogcnetwork.net/AIP2develop .

Modeling Carbon Exchange

NASA Technical Reports Server (NTRS)

Sellers, Piers

2012-01-01

Model results will be reviewed to assess different methods for bounding the terrestrial role in the global carbon cycle. It is proposed that a series of climate model runs could be scoped that would tighten the limits on the "missing sink" of terrestrial carbon and could also direct future satellite image analyses to search for its geographical location and understand its seasonal dynamics.

Integrating invasive grasses into carbon cycle projections: Cogongrass spread in southern pine forests

NASA Astrophysics Data System (ADS)

McCabe, T. D.; Flory, S. L.; Wiesner, S.; Dietze, M.

2017-12-01

Forested ecosystems are currently being disrupted by invasive species. One example is the invasive grass Imperata cylindrica (cogongrass), which is widespread in southeastern US pine forests. Pines forests dominate the forest cover of the southeast, and contribute to making the Southeast the United States' largest carbon sink. Cogongrass decreases the colonization of loblolly pine fine roots. If cogongrass continues to invade,this sink could be jeopardized. However, the effects of cogongrass invasion on carbon sequestration are largely unknown. We have projected the effects of elevated CO2 and changing climate on future cogongrass invasion. To test how pine stands are affected by cogongrass, cogongrass invasions were modeled using the Ecosystem Demography 2 (ED2) model, and parameterized using the Predictive Ecosystem Analyzer (PEcAn). ED2 takes into account local meteorological data, stand populations and succession, disturbance, and geochemical pools. PEcAn is a workflow that uses Bayesian sensitivity analyses and variance decomposition to quantify the uncertainty that each parameter contributes to overall model uncertainty. ED2 was run for four NEON and Ameriflux sites in the Southeast from the earliest available census of the site into 2010. These model results were compared to site measures to test for model accuracy and bias. To project the effect of elevated CO2 on cogongrass invasions, ED was run from 2006-2100 at four sites under four separate scenarios: 1) RPC4.5 CO2 and climate, 2) RPC4.5 climate only, with constant CO2 concentrations, 3) RPC4.5 Elevated CO2 only, with climate randomly selected from 2006-2026, 4) Present Day, made from randomly selected measures of CO2 and radiation from 2006-2026. Each scenario was run three times; once with cogongrass absent, once with a low cogongrass abundance, and once with a high cogongrass abundance. Model results suggest that many relevant parameters have high uncertainty due to lack of measurement. Further field work quantifying the carbon cycle, particularly belowground processes and respiration, could help constrain parameter uncertainty.

Impacts of Climate Change on Electricity Consumption in Baden-Wuerttemberg

NASA Astrophysics Data System (ADS)

Mimler, S.

2009-04-01

Changes in electricity consumption due to changes in mean air temperatures were examined for the German federal state Baden-Wuerttemberg. Unlike in most recent studies on future electricity demand variations due to climate change, other load influencing factors like the economic, technological and demographic situation were fixed to the state of 2006. This allows isolating the climate change effect on electricity demand. The analysis was realised in two major steps. Firstly, an electricity forecast model based on multiple regressions was estimated on the region of Baden-Wuerttemberg by using historical load and temperature data. The estimation of the forecast model provides information on the temperature sensitivity of electricity demand in the given region. The overall heating and cooling gradients are estimated with -59 and 84 MW / °C respectively. These results already point out a low temperature sensitivity of demand in the region of Baden-Wuerttemberg mostly due to a low share of households equipped with electric heating and air conditioning systems. Secondly, near surface air temperature data of the regional climate model REMO [1] was used to simulate load curves for the control period 1971 to 2000 and for three future scenarios 2006 to 2035, 2036 to 2065 and 2066 to 2095. The results show that the overall load decreases throughout all future scenario periods in comparison to the control period. This is due to a higher decrease in heating than increase in cooling load. Nevertheless, the weather dependent part in Baden-Wuerttemberg loads only accounts for 0.05 % of the average load level. Within this weather dependent part, the heating load decreases are highest in June to September concentrated on the day times evening and afternoon. The cooling period broadens from May to September in the control period to April to October by 2095. The highest relative increases occur in October. Regarding day times, the increase in cooling load is concentrated on afternoons, evenings and nights. [1] Jacob, D. (2005a), "REMO A1B Scenario run, UBA project, 0.088 degree resolution, run no.006211, 1H data", World Data Center for Climate, CERA-DB "REMO_UBA_A1B_1_R006211_1H", http://cera-www.dkrz.de/WDCC/ui/Compact.jsp? acronym=REMO_UBA_A1B_1_R006211_1H Jacob, D. (2005b), "REMO climate of the 20th century run, UBA project, 0.088 degree resolution, run no. 006210, 1H data", World Data Center for Climate, CERA-DB "REMO_UBA_C20_1_R006210_1H", http://cera-www.dkrz.de/WDCC/ui/Compact. jsp?acronym=REMO_UBA_C20_1_R006210_1H

Adaptation to Climate Change: A Comparative Analysis of Modeling Methods for Heat-Related Mortality.

PubMed

Gosling, Simon N; Hondula, David M; Bunker, Aditi; Ibarreta, Dolores; Liu, Junguo; Zhang, Xinxin; Sauerborn, Rainer

2017-08-16

Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to "adaptation uncertainty" (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. This study had three aims: a ) Compare the range in projected impacts that arises from using different adaptation modeling methods; b ) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c ) recommend modeling method(s) to use in future impact assessments. We estimated impacts for 2070-2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634.

Numerical modelling of climate change impacts on freshwater lenses on the North Sea Island of Borkum

NASA Astrophysics Data System (ADS)

Sulzbacher, H.; Wiederhold, H.; Siemon, B.; Grinat, M.; Igel, J.; Burschil, T.; Günther, T.; Hinsby, K.

2012-03-01

A numerical variable-density groundwater model is set up for the North Sea Island of Borkum to estimate climate change impacts on coastal aquifers and especially the situation of barrier islands in the Wadden Sea. The database includes information from boreholes, a seismic survey, a helicopter-borne electromagnetic survey (HEM), monitoring of the freshwater-saltwater boundary by vertical electrode chains in two boreholes, measurements of groundwater table, pumping and slug tests, as well as water samples. Based on a statistical analysis of borehole columns, seismic sections and HEM, a hydrogeological model is set up. The groundwater model is developed using the finite-element programme FEFLOW. The variable-density groundwater model is calibrated on the basis of hydraulic, hydrological and geophysical data, in particular spatial HEM and local monitoring data. Verification runs with the calibrated model show good agreement between measured and computed hydraulic heads. A good agreement is also obtained between measured and computed density or total dissolved solids data for both the entire freshwater lens on a large scale and in the area of the well fields on a small scale. For simulating future changes in this coastal groundwater system until the end of the current century we use the climate scenario A2, specified by the Intergovernmental Panel on Climate Change and in particular the data for the German North Sea coast. Simulation runs show proceeding salinization with time beneath the well fields of the two waterworks Waterdelle and Ostland. The modelling study shows that spreading of well fields is an appropriate protection measure against excessive salinization of the water supply until the end of the current century.

Numerical modelling of climate change impacts on freshwater lenses on the North Sea Island of Borkum using hydrological and geophysical methods

NASA Astrophysics Data System (ADS)

Sulzbacher, H.; Wiederhold, H.; Siemon, B.; Grinat, M.; Igel, J.; Burschil, T.; Günther, T.; Hinsby, K.

2012-10-01

A numerical, density dependent groundwater model is set up for the North Sea Island of Borkum to estimate climate change impacts on coastal aquifers and especially the situation of barrier islands in the Wadden Sea. The database includes information from boreholes, a seismic survey, a helicopter-borne electromagnetic (HEM) survey, monitoring of the freshwater-saltwater boundary by vertical electrode chains in two boreholes, measurements of groundwater table, pumping and slug tests, as well as water samples. Based on a statistical analysis of borehole columns, seismic sections and HEM, a hydrogeological model is set up. The groundwater model is developed using the finite-element programme FEFLOW. The density dependent groundwater model is calibrated on the basis of hydraulic, hydrological and geophysical data, in particular spatial HEM and local monitoring data. Verification runs with the calibrated model show good agreement between measured and computed hydraulic heads. A good agreement is also obtained between measured and computed density or total dissolved solids data for both the entire freshwater lens on a large scale and in the area of the well fields on a small scale. For simulating future changes in this coastal groundwater system until the end of the current century, we use the climate scenario A2, specified by the Intergovernmental Panel on Climate Change and, in particular, the data for the German North Sea coast. Simulation runs show proceeding salinisation with time beneath the well fields of the two waterworks Waterdelle and Ostland. The modelling study shows that the spreading of well fields is an appropriate protection measure against excessive salinisation of the water supply until the end of the current century.

Evaluation of cool season precipitation event characteristics over the Northeast US in a suite of downscaled climate model hindcasts

NASA Astrophysics Data System (ADS)

Loikith, Paul C.; Waliser, Duane E.; Kim, Jinwon; Ferraro, Robert

2017-08-01

Cool season precipitation event characteristics are evaluated across a suite of downscaled climate models over the northeastern US. Downscaled hindcast simulations are produced by dynamically downscaling the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) using the National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (WRF) regional climate model (RCM) and the Goddard Earth Observing System Model, Version 5 (GEOS-5) global climate model. NU-WRF RCM simulations are produced at 24, 12, and 4-km horizontal resolutions using a range of spectral nudging schemes while the MERRA2 global downscaled run is provided at 12.5-km. All model runs are evaluated using four metrics designed to capture key features of precipitation events: event frequency, event intensity, even total, and event duration. Overall, the downscaling approaches result in a reasonable representation of many of the key features of precipitation events over the region, however considerable biases exist in the magnitude of each metric. Based on this evaluation there is no clear indication that higher resolution simulations result in more realistic results in general, however many small-scale features such as orographic enhancement of precipitation are only captured at higher resolutions suggesting some added value over coarser resolution. While the differences between simulations produced using nudging and no nudging are small, there is some improvement in model fidelity when nudging is introduced, especially at a cutoff wavelength of 600 km compared to 2000 km. Based on the results of this evaluation, dynamical regional downscaling using NU-WRF results in a more realistic representation of precipitation event climatology than the global downscaling of MERRA2 using GEOS-5.

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.

Climate mitigation: sustainable preferences and cumulative carbon

NASA Astrophysics Data System (ADS)

Buckle, Simon

2010-05-01

We develop a stylized AK growth model with both climate damages to ecosystem goods and services and sustainable preferences that allow trade-offs between present discounted utility and long-run climate damages. The simplicity of the model permits analytical solutions. Concern for the long-term provides a strong driver for mitigation action. One plausible specification of sustainable preferences leads to the result that, for a range of initial parameter values, an optimizing agent would choose a level of cumulative carbon dioxide (CO2) emissions independent of initial production capital endowment and CO2 levels. There is no technological change so, for economies with sufficiently high initial capital and CO2 endowments, optimal mitigation will lead to disinvestment. For lower values of initial capital and/or CO2 levels, positive investment can be optimal, but still within the same overall level of cumulative emissions. One striking aspect of the model is the complexity of possible outcomes, in addition to these optimal solutions. We also identify a resource constrained region and several regions where climate damages exceed resources available for consumption. Other specifications of sustainable preferences are discussed, as is the case of a hard constraint on long-run damages. Scientists are currently highlighting the potential importance of the cumulative carbon emissions concept as a robust yet flexible target for climate policymakers. This paper shows that it also has an ethical interpretation: it embodies an implicit trade off in global welfare between present discounted welfare and long-term climate damages. We hope that further development of the ideas presented here might contribute to the research and policy debate on the critical areas of intra- and intergenerational welfare.

Quantifying the importance of model-to-model variability in integrated assessments of 21st century climate

NASA Astrophysics Data System (ADS)

Bond-Lamberty, B. P.; Jones, A. D.; Shi, X.; Calvin, K. V.

2016-12-01

The C4MIP and CMIP5 model intercomparison projects (MIPs) highlighted uncertainties in climate projections, driven to a large extent by interactions between the terrestrial carbon cycle and climate feedbacks. In addition, the importance of feedbacks between human (energy and economic) systems and natural (carbon and climate) systems is poorly understood, and not considered in the previous MIP protocols. The experiments conducted under the previous Integrated Earth System Model (iESM) project, which coupled a earth system model with an integrated assessment model (GCAM), found that the inclusion of climate feedbacks on the terrestrial system in an RCP4.5 scenario increased ecosystem productivity, resulting in declines in cropland extent and increases in bioenergy production and forest cover. As a follow-up to these studies and to further understand climate-carbon cycle interactions and feedbacks, we examined the robustness of these results by running a suite of GCAM-only experiments using changes in ecosystem productivity derived from both the CMIP5 archive and the Agricultural Model Intercomparison Project. In our results, the effects of climate on yield in an RCP8.5 scenario tended to be more positive than those of AgMIP, but more negative than those of the other CMIP models. We discuss these results and the implications of model-to-model variability for integrated coupling studies of the future earth system.

Morphological Inheritance in Sandy Coastline Morphologies Subject to Long-Term Changes in Wave Climate: Surprising Insights from a Coastline Evolution Model

NASA Astrophysics Data System (ADS)

Murray, A. B.; Thomas, C.; Hurst, M. D.; Barkwith, A.; Ashton, A. D.; Ellis, M. A.

2014-12-01

Recent numerical modelling demonstrates that when sandy coastlines are affected predominantly by waves approaching from "high" angles (> ~45° between the coastline and wave crests at the offshore limit of shore-parallel contours), large-scale (kms to 100 kms) morphodynamic instabilities and finite-amplitude interactions can lead to the emergence of striking coastline features, including sand waves, capes and spits. The type of feature that emerges depends on the wave climate, defined as the angular distribution of wave influences on alongshore sediment transport. Under a constant wave climate, coastline morphology reaches a dynamical steady state; the cross-shore/alongshore aspect ratio and the general appearance of the features remains constant. In previous modelling involving wave-climate change, as well as comparisons between observed coastline morphologies and wave climates, it has been implicitly assumed that the morphology adjusts in a quasi-equilibrium fashion, so that at any time the coastline shape reflects the current forcing. However, here we present new model results showing pronounced path dependence in coastline morphodynamics. In experiments with a period of constant wave climate followed by a period of transition to a new wave climate and then a run-on phase, the features that exist during the run-on phase can be qualitatively and quantitatively different from those that would develop initially under the final wave climate. Although the features inherited from the past wave-climate history may in some case be true alternate stable states, in other cases the inherited features gradually decay toward the morphology that would be expected given the final wave climate. A suite of such experiments allows us to characterize how the e-folding timescale of this decay depends on 1) the initial wave climate, 2) the path through wave-climate space, and 3) the rate of transition. When the initial features are flying spits with cross-shore amplitudes of 6 - 8 km, e-folding times can be on the order of millennia or longer. These results could provide a new perspective when interpreting current and past coastline features. In addition, the complex paleo-coastline structure that develops in the coastal hinterlands in these experiments could be relevant to the structures observed in some coastal environments.

Investigating the relationship between climate teleconnection patterns and soil moisture variability in the Rio Grande/Río Bravo del Norte basin using the NOAH land surface model

NASA Astrophysics Data System (ADS)

Khedun, C. P.; Mishra, A. K.; Bolten, J. D.; Giardino, J. R.; Singh, V. P.

2010-12-01

Soil moisture is an important component of the hydrological cycle. Climate variability patterns, such as the Pacific Decadal Oscillation (PDO), El Niño Southern Oscillation (ENSO), and Atlantic Multidecadal Oscillation (AMO) are determining factors on surface water availability and soil moisture. Understanding this complex relationship and the phase and lag times between climate events and soil moisture variability is important for agricultural management and water planning. In this study we look at the effect of these climate teleconnection patterns on the soil moisture across the Rio Grande/Río Bravo del Norte basin. The basin is transboundary between the US and Mexico and has a varied climatology - ranging from snow dominated in its headwaters in Colorado, to an arid and semi-arid region in its middle reach and a tropical climate in the southern section before it discharges into the Gulf of Mexico. Agricultural activities in the US and in northern Mexico are highly dependent on the Rio Grande and are extremely vulnerable to climate extremes. The treaty between the two countries does not address climate related events. The soil moisture is generated using the community NOAH land surface model (LSM). The LSM is a 1-D column model that runs in coupled or uncoupled mode, and it simulates soil moisture, soil temperature, skin temperature, snowpack depth, snow water equivalent, canopy water content, and energy flux and water flux of the surface energy and water balance. The North American Land Data Assimilation Scheme 2 (NLDAS2) is used to drive the model. The model is run for the period 1979 to 2009. The soil moisture output is validated against measured values from the different Soil Climate Analysis Network (SCAN) sites within the basin. The spatial and temporal variability of the modeled soil moisture is then analyzed using marginal entropy to investigate monthly, seasonal, and annual variability. Wavelet transform is used to determine the relation, phase difference, and lag times between climate teleconnection events and soil moisture. The results from this study will help agricultural scientists and water planners in both the US and Mexico in better managing the dwindling water resources of this transboundary basin.

The Prodiguer Messaging Platform

NASA Astrophysics Data System (ADS)

Greenslade, Mark; Denvil, Sebastien; Raciazek, Jerome; Carenton, Nicolas; Levavasseur, Guillame

2014-05-01

CONVERGENCE is a French multi-partner national project designed to gather HPC and informatics expertise to innovate in the context of running French climate models with differing grids and at differing resolutions. Efficient and reliable execution of these models and the management and dissemination of model output (data and meta-data) are just some of the complexities that CONVERGENCE aims to resolve. The Institut Pierre Simon Laplace (IPSL) is responsible for running climate simulations upon a set of heterogenous HPC environments within France. With heterogeneity comes added complexity in terms of simulation instrumentation and control. Obtaining a global perspective upon the state of all simulations running upon all HPC environments has hitherto been problematic. In this presentation we detail how, within the context of CONVERGENCE, the implementation of the Prodiguer messaging platform resolves complexity and permits the development of real-time applications such as: 1. a simulation monitoring dashboard; 2. a simulation metrics visualizer; 3. an automated simulation runtime notifier; 4. an automated output data & meta-data publishing pipeline; The Prodiguer messaging platform leverages a widely used open source message broker software called RabbitMQ. RabbitMQ itself implements the Advanced Message Queue Protocol (AMPQ). Hence it will be demonstrated that the Prodiguer messaging platform is built upon both open source and open standards.

Chemistry-Climate Interactions in the Goddard Institute for Space Studies General Circulation Model. 2; New Insights into Modeling the Pre-Industrial Atmosphere

NASA Technical Reports Server (NTRS)

Grenfell, J. Lee; Shindell, D. T.; Koch, D.; Rind, D.; Hansen, James E. (Technical Monitor)

2002-01-01

We investigate the chemical (hydroxyl and ozone) and dynamical response to changing from present day to pre-industrial conditions in the Goddard Institute for Space Studies General Circulation Model (GISS GMC). We identify three main improvements not included by many other works. Firstly, our model includes interactive cloud calculations. Secondly we reduce sulfate aerosol which impacts NOx partitioning hence Ox distributions. Thirdly we reduce sea surface temperatures and increase ocean ice coverage which impact water vapor and ground albedo respectively. Changing the ocean data (hence water vapor and ozone) produces a potentially important feedback between the Hadley circulation and convective cloud cover. Our present day run (run 1, control run) global mean OH value was 9.8 x 10(exp 5) molecules/cc. For our best estimate of pre-industrial conditions run (run 2) which featured modified chemical emissions, sulfate aerosol and sea surface temperatures/ocean ice, this value changed to 10.2 x 10(exp 5) molecules/cc. Reducing only the chemical emissions to pre-industrial levels in run 1 (run 3) resulted in this value increasing to 10.6 x 10(exp 5) molecules/cc. Reducing the sulfate in run 3 to pre-industrial levels (run 4) resulted in a small increase in global mean OH (10.7 x 10(exp 5) molecules/cc). Changing the ocean data in run 4 to pre-industrial levels (run 5) led to a reduction in this value to 10.3 x 10(exp 5) molecules/cc. Mean tropospheric ozone burdens were 262, 181, 180, 180, and 182 Tg for runs 1-5 respectively.

Revisiting ocean carbon sequestration by direct injection: a global carbon budget perspective

NASA Astrophysics Data System (ADS)

Reith, Fabian; Keller, David P.; Oschlies, Andreas

2016-11-01

In this study we look beyond the previously studied effects of oceanic CO2 injections on atmospheric and oceanic reservoirs and also account for carbon cycle and climate feedbacks between the atmosphere and the terrestrial biosphere. Considering these additional feedbacks is important since backfluxes from the terrestrial biosphere to the atmosphere in response to reducing atmospheric CO2 can further offset the targeted reduction. To quantify these dynamics we use an Earth system model of intermediate complexity to simulate direct injection of CO2 into the deep ocean as a means of emissions mitigation during a high CO2 emission scenario. In three sets of experiments with different injection depths, we simulate a 100-year injection period of a total of 70 GtC and follow global carbon cycle dynamics over another 900 years. In additional parameter perturbation runs, we varied the default terrestrial photosynthesis CO2 fertilization parameterization by ±50 % in order to test the sensitivity of this uncertain carbon cycle feedback to the targeted atmospheric carbon reduction through direct CO2 injections. Simulated seawater chemistry changes and marine carbon storage effectiveness are similar to previous studies. As expected, by the end of the injection period avoided emissions fall short of the targeted 70 GtC by 16-30 % as a result of carbon cycle feedbacks and backfluxes in both land and ocean reservoirs. The target emissions reduction in the parameter perturbation simulations is about 0.2 and 2 % more at the end of the injection period and about 9 % less to 1 % more at the end of the simulations when compared to the unperturbed injection runs. An unexpected feature is the effect of the model's internal variability of deep-water formation in the Southern Ocean, which, in some model runs, causes additional oceanic carbon uptake after injection termination relative to a control run without injection and therefore with slightly different atmospheric CO2 and climate. These results of a model that has very low internal climate variability illustrate that the attribution of carbon fluxes and accounting for injected CO2 may be very challenging in the real climate system with its much larger internal variability.

Climate SPHINX: evaluating the impact of resolution and stochastic physics parameterisations in the EC-Earth global climate model

NASA Astrophysics Data System (ADS)

Davini, Paolo; von Hardenberg, Jost; Corti, Susanna; Christensen, Hannah M.; Juricke, Stephan; Subramanian, Aneesh; Watson, Peter A. G.; Weisheimer, Antje; Palmer, Tim N.

2017-03-01

The Climate SPHINX (Stochastic Physics HIgh resolutioN eXperiments) project is a comprehensive set of ensemble simulations aimed at evaluating the sensitivity of present and future climate to model resolution and stochastic parameterisation. The EC-Earth Earth system model is used to explore the impact of stochastic physics in a large ensemble of 30-year climate integrations at five different atmospheric horizontal resolutions (from 125 up to 16 km). The project includes more than 120 simulations in both a historical scenario (1979-2008) and a climate change projection (2039-2068), together with coupled transient runs (1850-2100). A total of 20.4 million core hours have been used, made available from a single year grant from PRACE (the Partnership for Advanced Computing in Europe), and close to 1.5 PB of output data have been produced on SuperMUC IBM Petascale System at the Leibniz Supercomputing Centre (LRZ) in Garching, Germany. About 140 TB of post-processed data are stored on the CINECA supercomputing centre archives and are freely accessible to the community thanks to an EUDAT data pilot project. This paper presents the technical and scientific set-up of the experiments, including the details on the forcing used for the simulations performed, defining the SPHINX v1.0 protocol. In addition, an overview of preliminary results is given. An improvement in the simulation of Euro-Atlantic atmospheric blocking following resolution increase is observed. It is also shown that including stochastic parameterisation in the low-resolution runs helps to improve some aspects of the tropical climate - specifically the Madden-Julian Oscillation and the tropical rainfall variability. These findings show the importance of representing the impact of small-scale processes on the large-scale climate variability either explicitly (with high-resolution simulations) or stochastically (in low-resolution simulations).

Tropical pacing of Antarctic sea ice increase

NASA Astrophysics Data System (ADS)

Schneider, D. P.

2015-12-01

One reason why coupled climate model simulations generally do not reproduce the observed increase in Antarctic sea ice extent may be that their internally generated climate variability does not sync with the observed phases of phenomena like the Pacific Decadal Oscillation (PDO) and ENSO. For example, it is unlikely for a free-running coupled model simulation to capture the shift of the PDO from its positive to negative phase during 1998, and the subsequent ~15 year duration of the negative PDO phase. In previously presented work based on atmospheric models forced by observed tropical SSTs and stratospheric ozone, we demonstrated that tropical variability is key to explaining the wind trends over the Southern Ocean during the past ~35 years, particularly in the Ross, Amundsen and Bellingshausen Seas, the regions of the largest trends in sea ice extent and ice season duration. Here, we extend this idea to coupled model simulations with the Community Earth System Model (CESM) in which the evolution of SST anomalies in the central and eastern tropical Pacific is constrained to match the observations. This ensemble of 10 "tropical pacemaker" simulations shows a more realistic evolution of Antarctic sea ice anomalies than does its unconstrained counterpart, the CESM Large Ensemble (both sets of runs include stratospheric ozone depletion and other time-dependent radiative forcings). In particular, the pacemaker runs show that increased sea ice in the eastern Ross Sea is associated with a deeper Amundsen Sea Low (ASL) and stronger westerlies over the south Pacific. These circulation patterns in turn are linked with the negative phase of the PDO, characterized by negative SST anomalies in the central and eastern Pacific. The timing of tropical decadal variability with respect to ozone depletion further suggests a strong role for tropical variability in the recent acceleration of the Antarctic sea ice trend, as ozone depletion stabilized by late 1990s, prior to the most recent major shift in tropical climate. In the pacemaker runs, the positive sea ice trend in the eastern Ross Sea is stronger during the most recent period (~2000-2014) than it is during period of rapid ozone depletion (~1980-1996).

The thermal influence of continents on a model-generated January climate

NASA Technical Reports Server (NTRS)

Spar, J.; Cohen, C.; Wu, P.

1981-01-01

Two climate simulations were compared. Both climate computations were initialized with the same horizontally uniform state of rest. However, one is carried out on a water planet (without continents), while the second is repeated on a planet with geographically realistic but flat (sea level) continents. The continents in this experiment have a uniform albedo of 0.14, except where snow accumulates, a uniform roughness height of 0.3 m, and zero water storage capacity. Both runs were carried out for a 'perpetual January' with solar declination fixed at January 15.

Ocean angular momentum signals in a climate model and implications for Earth rotation

NASA Astrophysics Data System (ADS)

Ponte, R. M.; Rajamony, J.; Gregory, J. M.

2002-03-01

Estimates of ocean angular momentum (OAM) provide an integrated measure of variability in ocean circulation and mass fields and can be directly related to observed changes in Earth rotation. We use output from a climate model to calculate 240 years of 3-monthly OAM values (two equatorial terms L1 and L2, related to polar motion or wobble, and axial term L3, related to length of day variations) representing the period 1860-2100. Control and forced runs permit the study of the effects of natural and anthropogenically forced climate variability on OAM. All OAM components exhibit a clear annual cycle, with large decadal modulations in amplitude, and also longer period fluctuations, all associated with natural climate variability in the model. Anthropogenically induced signals, inferred from the differences between forced and control runs, include an upward trend in L3, related to inhomogeneous ocean warming and increases in the transport of the Antarctic Circumpolar Current, and a significantly weaker seasonal cycle in L2 in the second half of the record, related primarily to changes in seasonal bottom pressure variability in the Southern Ocean and North Pacific. Variability in mass fields is in general more important to OAM signals than changes in circulation at the seasonal and longer periods analyzed. Relation of OAM signals to changes in surface atmospheric forcing are discussed. The important role of the oceans as an excitation source for the annual, Chandler and Markowitz wobbles, is confirmed. Natural climate variability in OAM and related excitation is likely to measurably affect the Earth rotation, but anthropogenically induced effects are comparatively weak.

Assessment of regional climate change and development of climate adaptation decision aids in the Southwestern US

NASA Astrophysics Data System (ADS)

Darmenova, K.; Higgins, G.; Kiley, H.; Apling, D.

2010-12-01

Current General Circulation Models (GCMs) provide a valuable estimate of both natural and anthropogenic climate changes and variability on global scales. At the same time, future climate projections calculated with GCMs are not of sufficient spatial resolution to address regional needs. Many climate impact models require information at scales of 50 km or less, so dynamical downscaling is often used to estimate the smaller-scale information based on larger scale GCM output. To address current deficiencies in local planning and decision making with respect to regional climate change, our research is focused on performing a dynamical downscaling with the Weather Research and Forecasting (WRF) model and developing decision aids that translate the regional climate data into actionable information for users. Our methodology involves development of climatological indices of extreme weather and heating/cooling degree days based on WRF ensemble runs initialized with the NCEP-NCAR reanalysis and the European Center/Hamburg Model (ECHAM5). Results indicate that the downscale simulations provide the necessary detailed output required by state and local governments and the private sector to develop climate adaptation plans. In addition we evaluated the WRF performance in long-term climate simulations over the Southwestern US and validated against observational datasets.

Devon Ice cap's future: results from climate and ice dynamics modelling via surface mass balance modelling

NASA Astrophysics Data System (ADS)

Rodehacke, C. B.; Mottram, R.; Boberg, F.

2017-12-01

The Devon Ice Cap is an example of a relatively well monitored small ice cap in the Canadian Arctic. Close to Greenland, it shows a similar surface mass balance signal to glaciers in western Greenland. Here we various boundary conditions, ranging from ERA-Interim reanalysis data via global climate model high resolution (5km) output from the regional climate model HIRHAM5, to determine the surface mass balance of the Devon ice cap. These SMB estimates are used to drive the PISM glacier model in order to model the present day and future prospects of this small Arctic ice cap. Observational data from the Devon Ice Cap in Arctic Canada is used to evaluate the surface mass balance (SMB) data output from the HIRHAM5 model for simulations forced with the ERA-Interim climate reanalysis data and the historical emissions scenario run by the EC-Earth global climate model. The RCP8.5 scenario simulated by EC-Earth is also downscaled by HIRHAM5 and this output is used to force the PISM model to simulate the likely future evolution of the Devon Ice Cap under a warming climate. We find that the Devon Ice Cap is likely to continue its present day retreat, though in the future increased precipitation partly offsets the enhanced melt rates caused by climate change.

Characterization and modeling of turbidity density plume induced into stratified reservoir by flood runoffs.

PubMed

Chung, S W; Lee, H S

2009-01-01

In monsoon climate area, turbidity flows typically induced by flood runoffs cause numerous environmental impacts such as impairment of fish habitat and river attraction, and degradation of water supply efficiency. This study was aimed to characterize the physical dynamics of turbidity plume induced into a stratified reservoir using field monitoring and numerical simulations, and to assess the effect of different withdrawal scenarios on the control of downstream water quality. Three different turbidity models (RUN1, RUN2, RUN3) were developed based on a two-dimensional laterally averaged hydrodynamic and transport model, and validated against field data. RUN1 assumed constant settling velocity of suspended sediment, while RUN2 estimated the settling velocity as a function of particle size, density, and water temperature to consider vertical stratification. RUN3 included a lumped first-order turbidity attenuation rate taking into account the effects of particles aggregation and degradable organic particles. RUN3 showed best performance in replicating the observed variations of in-reservoir and release turbidity. Numerical experiments implemented to assess the effectiveness of different withdrawal depths showed that the alterations of withdrawal depth can modify the pathway and flow regimes of the turbidity plume, but its effect on the control of release water quality could be trivial.

Appendix 2. Guide for Running AgMIP Climate Scenario Generation Tools with R in Windows, Version 2.3

NASA Technical Reports Server (NTRS)

Hudson, Nicholas; Ruane, Alexander Clark

2013-01-01

This Guide explains how to create climate series and climate change scenarios by using the AgMip Climate team's methodology as outlined in the AgMIP Guide for Regional Assessment: Handbook of Methods and Procedures. It details how to: install R and the required packages to run the AgMIP Climate Scenario Generation scripts, and create climate scenarios from CMIP5 GCMs using a 30-year baseline daily weather dataset. The Guide also outlines a workflow that can be modified for application to your own climate data.

A framework for process-based assessment of regional climate model experiments: applied to projections of southern African precipitation

NASA Astrophysics Data System (ADS)

James, Rachel; Washington, Richard; Jones, Richard

2015-04-01

There is a demand from adaptation planners for regional climate change projections, particularly the finer resolution data delivered by regional models. However, climate models are subject to important uncertainties, and their projections diverge substantially, particularly for precipitation. So how should decision makers know which futures to consider and which to disregard? Model evaluation is clearly a priority. The majority of studies seeking to assess the validity of projections are based on comparison of the models' twentieth century climatologies with observations or reanalysis. Whilst this work is very important, examination of the modelled mean state it is not sufficient to assess the credibility of modelled changes. Direct investigation of the mechanisms for change is also vital. In this study, a framework for process-based analysis of projections is presented, whereby circulation changes accompanying future responses are examined, and then compared to atmospheric dynamics during historical years in models and reanalyses. This framework has previously been applied to investigate a drying signal in West Africa, and will here be used to examine projected precipitation change in southern Africa. An ensemble of five global and regional model experiments will be employed, consisting of five perturbed versions of HadCM3 and five corresponding runs of HadRM3P (PRECIS), run over the CORDEX Africa domain. The global and regional model runs show contrasting future responses: there is a strong drying in the global models over southern Africa during the rainy season, but the regional models show drying over Madagascar and the south west Indian Ocean. Circulation changes associated with these projections will be presented as a first step towards understanding the mechanisms for change and the reasons for difference between the global and regional models. The interannual variability will also be examined and compared to reanalysis to explore how well the models represent the dipole between southern Africa and Madagascar in the twentieth century simulations. This analysis could shed light on the credibility of the projected changes, and the relative trustworthiness of the global and regional models. This research makes a valuable contribution to the understanding of mechanisms for change in southern Africa. It also has wider relevance for regional climate model studies, in highlighting the need to evaluate models on a case by case basis, and providing a framework for assessment which could be applied to other models and other regions.

An efficient climate model with water isotope physics: NEEMY

NASA Astrophysics Data System (ADS)

Hu, J.; Emile-Geay, J.

2015-12-01

This work describes the development of an isotope-enabled atmosphere-ocean global climate model, NEEMY. This is a model of intermediate complexity, which can run 100 model years in 30 hours using 33 CPUs. The atmospheric component is the SPEEDY-IER (Molteni et al. 2003; Dee et al. 2015a), which is a water isotope-enabled (with equilibrium and kinetic fractionation schemes in precipitation, evaporation and soil moisture) simplified atmospheric general circulation model, with T30 horizontal resolution and 8 vertical layers. The oceanic component is NEMO 3.4 (Madec 2008), a state-of-the-art oceanic model (~2° horizontal resolution and 31 vertical layers) with an oceanic isotope module (a passive tracer scheme). A 1000-year control run shows that NEEMY is stable and its energy is conserved. The mean state is comparable to that of CMIP3-era CGCMs, though much cheaper to run. Atmospheric teleconnections such as the NAO and PNA are simulated very well. NEEMY also simulates the oceanic meridional overturning circulation well. The tropical climate variability is weaker than observations, and the climatology exhibits a double ITCZ problem despite bias corrections. The standard deviation of the monthly mean Nino3.4 index is 0.61K, compared to 0.91K in observations (Reynolds et al. 2002). We document similarities and differences with a close cousin, SPEEDY-NEMO (Kucharski et al. 2015). With its fast speed and relatively complete physical processes, NEEMY is suitable for paleoclimate studies ; we will present some forced simulations of the past millennium and their use in forward-modeling climate proxies, via proxy system models (PSMs, Dee et al 2015b). References Dee, S., D. Noone, N. Buenning, J. Emile-Geay, and Y. Zhou, 2015a: SPEEDY-IER: A fast atmospheric GCM with water isotope physics. J. Geophys. Res. Atmos., 120: 73-91. doi:10.1002/2014JD022194. Dee, S. G., J. Emile-Geay, M. N. Evans, Allam, A., D. M. Thompson, and E. J. Steig, 2015b: PRYSM: an open-source framework for proxy system modeling, with applications to oxygen-isotope systems, J. Adv. Mod. Earth Sys., 07, doi:10.1002/2015MS000447. Kucharski et al., 2015: Atlantic forcing of Pacific decadal variability. Clim. Dyn., doi:10.1007/s00382-015-2705-z.

Long-run evolution of the global economy - Part 2: Hindcasts of innovation and growth

NASA Astrophysics Data System (ADS)

Garrett, T. J.

2015-10-01

Long-range climate forecasts use integrated assessment models to link the global economy to greenhouse gas emissions. This paper evaluates an alternative economic framework outlined in part 1 of this study (Garrett, 2014) that approaches the global economy using purely physical principles rather than explicitly resolved societal dynamics. If this model is initialized with economic data from the 1950s, it yields hindcasts for how fast global economic production and energy consumption grew between 2000 and 2010 with skill scores > 90 % relative to a model of persistence in trends. The model appears to attain high skill partly because there was a strong impulse of discovery of fossil fuel energy reserves in the mid-twentieth century that helped civilization to grow rapidly as a deterministic physical response. Forecasting the coming century may prove more of a challenge because the effect of the energy impulse appears to have nearly run its course. Nonetheless, an understanding of the external forces that drive civilization may help development of constrained futures for the coupled evolution of civilization and climate during the Anthropocene.

A 3PG-based Model to Simulate Delta-13C Content in Three Tree Species in The Mica Creek Experiment Watershed, Idaho

NASA Astrophysics Data System (ADS)

Wei, L.; Marshall, J. D.

2007-12-01

3PG (Physiological Principles in Predicting Growth), a process-based physiological model of forest productivity, has been widely used and well validated. Based on 3PG, a 3PG-δ13C model to simulate δ13C content in plant tissue is built in this research. 3PG calculates carbon assimilation from utilizable absorbed photosynthetically active radiation (PAR), and calculates stomatal conductance from maximum canopy conductance multiplied by physiological modifier which includes the effect of water vapor deficit and soil water. Then the equation of Farquhar and Sharkey (1982) was used to calculate δ13C content in plant. Five even-aged coniferous forest stands located near Clarkia, Idaho (47°15'N, 115°25'W) in Mica Creek Experimental Watershed, were chosen to test the model, (2 stands had been partial cut (50% canopy removal in 1990) and 3 were uncut). MCEW has been extensively investigated since 1990 and many necessary parameters needed for 3PG are readily available. Each of these sites is located near a UI Meteorological station, which recorded half-hourly climatic data since 2003. These site-specific climatic data were extend to 1991 by correlating with data from a nearby SNOTEL station (SNOwpack TELemetry, NRCS, 47°9' N, 116°16' W). Forest mensuration data were obtained form each stand using variable radius plots (VRP). Three tree species, which consist more than 95% of all trees, were parameterized for 3PG model, including: grand fir (Abies grandis Donn ex D. Don), western red cedar (Thuja plicat Donn ex D. Don a) and Douglas-fir (Pseudotsuga menziesii var. glauca (Beissn.) Franco). Because 4 out of 5 stands have mixed species, we also used parameters for mixed stands to run the model. To stabilize, the model was initially run under average climatic data for 20 years, and then run under the actual climatic data from 1991 to 2006. As 3PG runs in a monthly time step, monthly δ13C values were calculated first, and then yearly values were calculated by weighted averages. For testing the model, tree cores were collected from each stand and species. Ring-widths of tree cores were measured and cross-dated with a ring-width chronology obtained from MCEW. δ13C contents of tree- ring samples from known year were tested. Preliminary results indicate 3PG-δ13C simulated values are consistent with observed values in tree-rings. δ13C values of modeled species are different: western red cider has the highest delta13C values among the three species and western larch has the lowest.

Ecohydrologic process modeling of mountain block groundwater recharge.

PubMed

Magruder, Ian A; Woessner, William W; Running, Steve W

2009-01-01

Regional mountain block recharge (MBR) is a key component of alluvial basin aquifer systems typical of the western United States. Yet neither water scientists nor resource managers have a commonly available and reasonably invoked quantitative method to constrain MBR rates. Recent advances in landscape-scale ecohydrologic process modeling offer the possibility that meteorological data and land surface physical and vegetative conditions can be used to generate estimates of MBR. A water balance was generated for a temperate 24,600-ha mountain watershed, elevation 1565 to 3207 m, using the ecosystem process model Biome-BGC (BioGeochemical Cycles) (Running and Hunt 1993). Input data included remotely sensed landscape information and climate data generated with the Mountain Climate Simulator (MT-CLIM) (Running et al. 1987). Estimated mean annual MBR flux into the crystalline bedrock terrain is 99,000 m(3) /d, or approximately 19% of annual precipitation for the 2003 water year. Controls on MBR predictions include evapotranspiration (radiation limited in wet years and moisture limited in dry years), soil properties, vegetative ecotones (significant at lower elevations), and snowmelt (dominant recharge process). The ecohydrologic model is also used to investigate how climatic and vegetative controls influence recharge dynamics within three elevation zones. The ecohydrologic model proves useful for investigating controls on recharge to mountain blocks as a function of climate and vegetation. Future efforts will need to investigate the uncertainty in the modeled water balance by incorporating an advanced understanding of mountain recharge processes, an ability to simulate those processes at varying scales, and independent approaches to calibrating MBR estimates. Copyright © 2009 The Author(s). Journal compilation © 2009 National Ground Water Association.

What have we learned from the German consortium project STORM aiming at high-resolution climate simulations?

NASA Astrophysics Data System (ADS)

von Storch, Jin-Song

2014-05-01

The German consortium STORM was built to explore high-resolution climate simulations using the high-performance computer stored at the German Climate Computer Center (DKRZ). One of the primary goals is to quantify the effect of unresolved (and parametrized) processes on climate sensitivity. We use ECHAM6/MPIOM, the coupled atmosphere-ocean model developed at the Max-Planck Institute for Meteorology. The resolution is T255L95 for the atmosphere and 1/10 degree and 80 vertical levels for the ocean. We discuss results of stand-alone runs, i.e. the ocean-only simulation driven by the NCEP/NCAR renalaysis and the atmosphere-only AMIP-type of simulation. Increasing resolution leads to a redistribution of biases, even though some improvements, both in the atmosphere and in the ocean, can clearly be attributed to the increase in resolution. We represent also new insights on ocean meso-scale eddies, in particular their effects on the ocean's energetics. Finally, we discuss the status and problems of the coupled high-resolution runs.

Use of several Cloud Computing approaches for climate modelling: performance, costs and opportunities

NASA Astrophysics Data System (ADS)

Perez Montes, Diego A.; Añel Cabanelas, Juan A.; Wallom, David C. H.; Arribas, Alberto; Uhe, Peter; Caderno, Pablo V.; Pena, Tomas F.

2017-04-01

Cloud Computing is a technological option that offers great possibilities for modelling in geosciences. We have studied how two different climate models, HadAM3P-HadRM3P and CESM-WACCM, can be adapted in two different ways to run on Cloud Computing Environments from three different vendors: Amazon, Google and Microsoft. Also, we have evaluated qualitatively how the use of Cloud Computing can affect the allocation of resources by funding bodies and issues related to computing security, including scientific reproducibility. Our first experiments were developed using the well known ClimatePrediction.net (CPDN), that uses BOINC, over the infrastructure from two cloud providers, namely Microsoft Azure and Amazon Web Services (hereafter AWS). For this comparison we ran a set of thirteen month climate simulations for CPDN in Azure and AWS using a range of different virtual machines (VMs) for HadRM3P (50 km resolution over South America CORDEX region) nested in the global atmosphere-only model HadAM3P. These simulations were run on a single processor and took between 3 and 5 days to compute depending on the VM type. The last part of our simulation experiments was running WACCM over different VMS on the Google Compute Engine (GCE) and make a comparison with the supercomputer (SC) Finisterrae1 from the Centro de Supercomputacion de Galicia. It was shown that GCE gives better performance than the SC for smaller number of cores/MPI tasks but the model throughput shows clearly how the SC performance is better after approximately 100 cores (related with network speed and latency differences). From a cost point of view, Cloud Computing moves researchers from a traditional approach where experiments were limited by the available hardware resources to monetary resources (how many resources can be afforded). As there is an increasing movement and recommendation for budgeting HPC projects on this technology (budgets can be calculated in a more realistic way) we could see a shift on the trends over the next years to consolidate Cloud as the preferred solution.

A new framework for the analysis of continental-scale convection-resolving climate simulations

NASA Astrophysics Data System (ADS)

Leutwyler, D.; Charpilloz, C.; Arteaga, A.; Ban, N.; Di Girolamo, S.; Fuhrer, O.; Hoefler, T.; Schulthess, T. C.; Christoph, S.

2017-12-01

High-resolution climate simulations at horizontal resolution of O(1-4 km) allow explicit treatment of deep convection (thunderstorms and rain showers). Explicitly treating convection by the governing equations reduces uncertainties associated with parametrization schemes and allows a model formulation closer to physical first principles [1,2]. But kilometer-scale climate simulations with long integration periods and large computational domains are expensive and data storage becomes unbearably voluminous. Hence new approaches to perform analysis are required. In the crCLIM project we propose a new climate modeling framework that allows scientists to conduct analysis at high spatial and temporal resolution. We tackle the computational cost by using the largest available supercomputers such as hybrid CPU-GPU architectures. For this the COSMO model has been adapted to run on such architectures [2]. We then alleviate the I/O-bottleneck by employing a simulation data-virtualizer (SDaVi) that allows to trade-off storage (space) for computational effort (time). This is achieved by caching the simulation outputs and efficiently launching re-simulations in case of cache misses. All this is done transparently from the analysis applications [3]. For the re-runs this approach requires a bit-reproducible version of COSMO. That is to say a model that produces identical results on different architectures to ensure coherent recomputation of the requested data [4]. In this contribution we present a version of SDaVi, a first performance model, and a strategy to obtain bit-reproducibility across hardware architectures.[1] N. Ban, J. Schmidli, C. Schär. Evaluation of the convection-resolving regional climate modeling approach in decade-long simulations. J. Geophys. Res. Atmos., 7889-7907, 2014.[2] D. Leutwyler, O. Fuhrer, X. Lapillonne, D. Lüthi, C. Schär. Towards European-scale convection-resolving climate simulations with GPUs: a study with COSMO 4.19. Geosci. Model Dev, 3393-3412, 2016.[3] S. Di Girolamo, P. Schmid, T. Schulthess, T. Hoefler. Virtualized Big Data: Reproducing Simulation Output on Demand. Submit. to the 23rd ACM Symposium on PPoPP 18, Vienna, Austria.[4] A. Arteaga, O. Fuhrer, T. Hoefler. Designing Bit-Reproducible Portable High-Performance Applications. IEEE 28th IPDPS, 2014.

Comparison of Mean Climate Trends in the Northern Hemisphere Between N.C.E.P. and Two Atmosphere-Ocean Model Forced Runs

NASA Technical Reports Server (NTRS)

Lucarini, Valerio; Russell, Gary L.; Hansen, James E. (Technical Monitor)

2002-01-01

Results are presented for two greenhouse gas experiments of the Goddard Institute for Space Studies Atmosphere-Ocean Model (AOM). The computed trends of surface pressure, surface temperature, 850, 500 and 200 mb geopotential heights and related temperatures of the model for the time frame 1960-2000 are compared to those obtained from the National Centers for Environmental Prediction observations. A spatial correlation analysis and mean value comparison are performed, showing good agreement. A brief general discussion about the statistics of trend detection is presented. The domain of interest is the Northern Hemisphere (NH) because of the higher reliability of both the model results and the observations. The accuracy that this AOM has in describing the observed regional and NH climate trends makes it reliable in forecasting future climate changes.

Validation of the Regional Climate Model ALARO with different dynamical downscaling approaches and different horizontal resolutions

NASA Astrophysics Data System (ADS)

Berckmans, Julie; Hamdi, Rafiq; De Troch, Rozemien; Giot, Olivier

2015-04-01

At the Royal Meteorological Institute of Belgium (RMI), climate simulations are performed with the regional climate model (RCM) ALARO, a version of the ALADIN model with improved physical parameterizations. In order to obtain high-resolution information of the regional climate, lateral bounary conditions (LBC) are prescribed from the global climate model (GCM) ARPEGE. Dynamical downscaling is commonly done in a continuous long-term simulation, with the initialisation of the model at the start and driven by the regularly updated LBCs of the GCM. Recently, more interest exists in the dynamical downscaling approach of frequent reinitializations of the climate simulations. For these experiments, the model is initialised daily and driven for 24 hours by the GCM. However, the surface is either initialised daily together with the atmosphere or free to evolve continuously. The surface scheme implemented in ALARO is SURFEX, which can be either run in coupled mode or in stand-alone mode. The regional climate is simulated on different domains, on a 20km horizontal resolution over Western-Europe and a 4km horizontal resolution over Belgium. Besides, SURFEX allows to perform a stand-alone or offline simulation on 1km horizontal resolution over Belgium. This research is in the framework of the project MASC: "Modelling and Assessing Surface Change Impacts on Belgian and Western European Climate", a 4-year project funded by the Belgian Federal Government. The overall aim of the project is to study the feedbacks between climate changes and land surface changes in order to improve regional climate model projections at the decennial scale over Belgium and Western Europe and thus to provide better climate projections and climate change evaluation tools to policy makers, stakeholders and the scientific community.

Modeling the potential persistence of various ecological systems under CMIP5 future climate and land use scenarios throughout California, USA

NASA Astrophysics Data System (ADS)

Baker, B.; Ferschweiler, K.; Bachelet, D. M.; Sleeter, B. M.

2016-12-01

California's geographic location, topographic complexity and latitudinal climatic gradient give rise to great biological and ecological diversity. However, increased land use pressure, altered seasonal weather patterns, and changes in temperature and precipitation regimes are having pronounced effects on ecosystems and the multitude of services they provide for an increasing population. As a result, natural resource managers are faced with formidable challenges to maintain these critical services. The goals of this project were to better understand how projected 21st century climate and land-use change scenarios may alter ecosystem dynamics, the spatial distribution of various vegetation types and land-use patterns, and to provide a coarse scale "triage map" of where land managers may want to concentrate efforts to reduce ecological stress in order to mitigate the potential impacts of a changing climate. We used the MC2 dynamic global vegetation model and the LUCAS state-and-transition simulation model to simulate the potential effects of future climate and land-use change on ecological processes for the state of California. Historical climate data were obtained from the PRISM dataset and nine CMIP5 climate models were run for the RCP 8.5 scenario. Climate projections were combined with a business-as-usual land-use scenario based on local-scale land use histories. For ease of discussion, results from five simulation runs (historic, hot-dry, hot-wet, warm-dry, and warm-wet) are presented. Results showed large changes in the extent of urban and agricultural lands. In addition, several simulated potential vegetation types persisted in situ under all four future scenarios, although alterations in total area, total ecosystem carbon, and forest vigor (NPP/LAI) were noted. As might be expected, the majority of the forested types that persisted occurred on public lands. However, more than 78% of the simulated subtropical mixed forest and 26% of temperate evergreen needleleaf forest types persisted on private lands under all four future scenarios. Result suggest that building collaborations across management borders could be valuable tool to guide natural resource management actions into the future.

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

Impact of climate change on electricity systems and markets

NASA Astrophysics Data System (ADS)

Chandramowli, Shankar N.

Climate change poses a serious threat to human welfare. There is now unequivocal scientific evidence that human actions are the primary cause of climate change. The principal climate forcing factor is the increasing accumulation of atmospheric carbon dioxide (CO2) due to combustion of fossil fuels for transportation and electricity generation. Generation of electricity account for nearly one-third of the greenhouse (GHG) emissions globally (on a CO2-equivalent basis). Any kind of economy-wide mitigation or adaptation effort to climate change must have a prominent focus on the electric power sector. I have developed a capacity expansion model for the power sector called LP-CEM (Linear Programming based Capacity Expansion Model). LP-CEM incorporates both the long-term climate change effects and the state/regional-level macroeconomic trends. This modeling framework is demonstrated for the electric power system in the Northeast region of United States. Some of the methodological advances introduced in this research are: the use of high-resolution temperature projections in a power sector capacity expansion model; the incorporation of changes in sectoral composition of electricity demand over time; the incorporation of the effects of climate change and variability on both the demand and supply-side of power sector using parameters estimated in the literature; and an inter-model coupling link with a macroeconomic model to account for price elasticity of demand and other effects on the broader macro-economy. LP-CEM-type models can be of use to state/regional level policymakers to plan for future mitigation and adaptation measures for the electric power sector. From the simulation runs, it is shown that scenarios with climate change effects and with high economic growth rates have resulted in higher capacity addition, optimal supply costs, wholesale/retail prices and total ratepayers' costs. LP-CEM is also adapted to model the implications of the proposed Clean Power Plan (Section 111 (d)) rules for the U.S. Northeast region. This dissertation applies an analytical model and an optimization model to investigate the implications of co-implementing an emission cap and an RPS policy for this region. A simplified analytical model of LP-CEM is specified and the first order optimality conditions are derived. The results from this analytical model are corroborated by running LP-CEM simulations under different carbon cap and RPS policy assumptions. A combination of these policies is shown to have a long-term beneficial effect for the final ratepayers in the region. This research conceptually explores the future implications of climate change and extreme weather events on the regional electricity market framework. The significant findings from this research and future policy considerations are discussed in the conclusion chapter.

The Jormungand Global Climate State and Implications for the Neoproterozoic Snowball Paradox (Invited)

NASA Astrophysics Data System (ADS)

Abbot, D. S.; Voigt, A.; Koll, D.; Pierrehumbert, R. T.

2010-12-01

We present a previously undescribed global climate state, the Jormungand state, that is nearly ice-covered with a narrow (~10-15 degrees of latitude) strip of open ocean near the equator. This state is sustained by internal dynamics of the hydrological cycle and the cryosphere. There is a new bifurcation in global climate climate associated with the Jormungand state that leads to significant hysteresis. We investigate the Jormungand state in a coupled ocean-atmosphere GCM, in multiple atmospheric GCMs coupled to a mixed layer ocean run in an idealized configuration, and we make a simple modification to the Budyko-Sellers model so that it produces Jormungand states. We suggest that the Jormungand state may be a better model for the Neoproterozoic glaciations (~635 Ma and ~715 Ma) than either the hard Snowball or the Slushball models. A Jormungand state would have a large enough region of open ocean near the equator to explain the micropaleontological and molecular clock evidence that photosynthetic eukaryotes thrived both before and immediately after the Neoproterozoic episodes. Additionally, since there is significant hysteresis associated with the Jormungand state, it can explain the cap carbonate sequences, the oxygen isotopic evidence that suggests high CO2 values, and the various evidence that suggests lifetimes for the glaciations of 1 Myrs or more. Since there is not significant hysteresis associated with the Slushball model, the Slushball model cannot explain these observations. Finally, we note that although the Slushball and Jormungand models share the characteristic of open ocean in the tropics, the Jormungand state is produced by entirely different physics, is entered through a new bifurcation in global climate, and is associated with significant hysteresis. Bifurcation diagram of global climate in the CAM global climate model, run with no continents, a 50 m mixed layer with no ocean heat transport, an eccentricity of zero, and annually and diurnally-varying insolation with a solar constant of 94% of present value. Red diamonds denote simulations initiated from ice-free conditions, blue circles denote simulations initiated from the Jormungand state, and green squares denote simulations initiated from the Snowball state. The black curve shows model equilibria, with dotted unstable solution branches (separatrices) and bifurcations drawn schematically.

Adaptation to Climate Change: A Comparative Analysis of Modeling Methods for Heat-Related Mortality

PubMed Central

Hondula, David M.; Bunker, Aditi; Ibarreta, Dolores; Liu, Junguo; Zhang, Xinxin; Sauerborn, Rainer

2017-01-01

Background: Multiple methods are employed for modeling adaptation when projecting the impact of climate change on heat-related mortality. The sensitivity of impacts to each is unknown because they have never been systematically compared. In addition, little is known about the relative sensitivity of impacts to “adaptation uncertainty” (i.e., the inclusion/exclusion of adaptation modeling) relative to using multiple climate models and emissions scenarios. Objectives: This study had three aims: a) Compare the range in projected impacts that arises from using different adaptation modeling methods; b) compare the range in impacts that arises from adaptation uncertainty with ranges from using multiple climate models and emissions scenarios; c) recommend modeling method(s) to use in future impact assessments. Methods: We estimated impacts for 2070–2099 for 14 European cities, applying six different methods for modeling adaptation; we also estimated impacts with five climate models run under two emissions scenarios to explore the relative effects of climate modeling and emissions uncertainty. Results: The range of the difference (percent) in impacts between including and excluding adaptation, irrespective of climate modeling and emissions uncertainty, can be as low as 28% with one method and up to 103% with another (mean across 14 cities). In 13 of 14 cities, the ranges in projected impacts due to adaptation uncertainty are larger than those associated with climate modeling and emissions uncertainty. Conclusions: Researchers should carefully consider how to model adaptation because it is a source of uncertainty that can be greater than the uncertainty in emissions and climate modeling. We recommend absolute threshold shifts and reductions in slope. https://doi.org/10.1289/EHP634 PMID:28885979

A Storm Surge and Inundation Model of the Back River Watershed at NASA Langley Research Center

NASA Technical Reports Server (NTRS)

Loftis, Jon Derek; Wang, Harry V.; DeYoung, Russell J.

2013-01-01

This report on a Virginia Institute for Marine Science project demonstrates that the sub-grid modeling technology (now as part of Chesapeake Bay Inundation Prediction System, CIPS) can incorporate high-resolution Lidar measurements provided by NASA Langley Research Center into the sub-grid model framework to resolve detailed topographic features for use as a hydrological transport model for run-off simulations within NASA Langley and Langley Air Force Base. The rainfall over land accumulates in the ditches/channels resolved via the model sub-grid was tested to simulate the run-off induced by heavy precipitation. Possessing both the capabilities for storm surge and run-off simulations, the CIPS model was then applied to simulate real storm events starting with Hurricane Isabel in 2003. It will be shown that the model can generate highly accurate on-land inundation maps as demonstrated by excellent comparison of the Langley tidal gauge time series data (CAPABLE.larc.nasa.gov) and spatial patterns of real storm wrack line measurements with the model results simulated during Hurricanes Isabel (2003), Irene (2011), and a 2009 Nor'easter. With confidence built upon the model's performance, sea level rise scenarios from the ICCP (International Climate Change Partnership) were also included in the model scenario runs to simulate future inundation cases.

Cross - Scale Intercomparison of Climate Change Impacts Simulated by Regional and Global Hydrological Models in Eleven Large River Basins

NASA Technical Reports Server (NTRS)

Hattermann, F. F.; Krysanova, V.; Gosling, S. N.; Dankers, R.; Daggupati, P.; Donnelly, C.; Florke, M.; Huang, S.; Motovilov, Y.; Buda, S.;

Selecting global climate models for regional climate change studies

PubMed Central

Pierce, David W.; Barnett, Tim P.; Santer, Benjamin D.; Gleckler, Peter J.

2009-01-01

Regional or local climate change modeling studies currently require starting with a global climate model, then downscaling to the region of interest. How should global models be chosen for such studies, and what effect do such choices have? This question is addressed in the context of a regional climate detection and attribution (D&A) study of January-February-March (JFM) temperature over the western U.S. Models are often selected for a regional D&A analysis based on the quality of the simulated regional climate. Accordingly, 42 performance metrics based on seasonal temperature and precipitation, the El Nino/Southern Oscillation (ENSO), and the Pacific Decadal Oscillation are constructed and applied to 21 global models. However, no strong relationship is found between the score of the models on the metrics and results of the D&A analysis. Instead, the importance of having ensembles of runs with enough realizations to reduce the effects of natural internal climate variability is emphasized. Also, the superiority of the multimodel ensemble average (MM) to any 1 individual model, already found in global studies examining the mean climate, is true in this regional study that includes measures of variability as well. Evidence is shown that this superiority is largely caused by the cancellation of offsetting errors in the individual global models. Results with both the MM and models picked randomly confirm the original D&A results of anthropogenically forced JFM temperature changes in the western U.S. Future projections of temperature do not depend on model performance until the 2080s, after which the better performing models show warmer temperatures. PMID:19439652

Performance Analysis of GFDL's GCM Line-By-Line Radiative Transfer Model on GPU and MIC Architectures

NASA Astrophysics Data System (ADS)

Menzel, R.; Paynter, D.; Jones, A. L.

2017-12-01

Due to their relatively low computational cost, radiative transfer models in global climate models (GCMs) run on traditional CPU architectures generally consist of shortwave and longwave parameterizations over a small number of wavelength bands. With the rise of newer GPU and MIC architectures, however, the performance of high resolution line-by-line radiative transfer models may soon approach those of the physical parameterizations currently employed in GCMs. Here we present an analysis of the current performance of a new line-by-line radiative transfer model currently under development at GFDL. Although originally designed to specifically exploit GPU architectures through the use of CUDA, the radiative transfer model has recently been extended to include OpenMP in an effort to also effectively target MIC architectures such as Intel's Xeon Phi. Using input data provided by the upcoming Radiative Forcing Model Intercomparison Project (RFMIP, as part of CMIP 6), we compare model results and performance data for various model configurations and spectral resolutions run on both GPU and Intel Knights Landing architectures to analogous runs of the standard Oxford Reference Forward Model on traditional CPUs.

Assessment of six dissimilarity metrics for climate analogues

NASA Astrophysics Data System (ADS)

Grenier, Patrick; Parent, Annie-Claude; Huard, David; Anctil, François; Chaumont, Diane

2013-04-01

Spatial analogue techniques consist in identifying locations whose recent-past climate is similar in some aspects to the future climate anticipated at a reference location. When identifying analogues, one key step is the quantification of the dissimilarity between two climates separated in time and space, which involves the choice of a metric. In this communication, spatial analogues and their usefulness are briefly discussed. Next, six metrics are presented (the standardized Euclidean distance, the Kolmogorov-Smirnov statistic, the nearest-neighbor distance, the Zech-Aslan energy statistic, the Friedman-Rafsky runs statistic and the Kullback-Leibler divergence), along with a set of criteria used for their assessment. The related case study involves the use of numerical simulations performed with the Canadian Regional Climate Model (CRCM-v4.2.3), from which three annual indicators (total precipitation, heating degree-days and cooling degree-days) are calculated over 30-year periods (1971-2000 and 2041-2070). Results indicate that the six metrics identify comparable analogue regions at a relatively large scale, but best analogues may differ substantially. For best analogues, it is also shown that the uncertainty stemming from the metric choice does generally not exceed that stemming from the simulation or model choice. A synthesis of the advantages and drawbacks of each metric is finally presented, in which the Zech-Aslan energy statistic stands out as the most recommended metric for analogue studies, whereas the Friedman-Rafsky runs statistic is the least recommended, based on this case study.

A virtual climate library of surface temperature over North America for 1979-2015

NASA Astrophysics Data System (ADS)

Kravtsov, Sergey; Roebber, Paul; Brazauskas, Vytaras

2017-10-01

The most comprehensive continuous-coverage modern climatic data sets, known as reanalyses, come from combining state-of-the-art numerical weather prediction (NWP) models with diverse available observations. These reanalysis products estimate the path of climate evolution that actually happened, and their use in a probabilistic context—for example, to document trends in extreme events in response to climate change—is, therefore, limited. Free runs of NWP models without data assimilation can in principle be used for the latter purpose, but such simulations are computationally expensive and are prone to systematic biases. Here we produce a high-resolution, 100-member ensemble simulation of surface atmospheric temperature over North America for the 1979-2015 period using a comprehensive spatially extended non-stationary statistical model derived from the data based on the North American Regional Reanalysis. The surrogate climate realizations generated by this model are independent from, yet nearly statistically congruent with reality. This data set provides unique opportunities for the analysis of weather-related risk, with applications in agriculture, energy development, and protection of human life.

A virtual climate library of surface temperature over North America for 1979–2015

PubMed Central

Kravtsov, Sergey; Roebber, Paul; Brazauskas, Vytaras

2017-01-01

The most comprehensive continuous-coverage modern climatic data sets, known as reanalyses, come from combining state-of-the-art numerical weather prediction (NWP) models with diverse available observations. These reanalysis products estimate the path of climate evolution that actually happened, and their use in a probabilistic context—for example, to document trends in extreme events in response to climate change—is, therefore, limited. Free runs of NWP models without data assimilation can in principle be used for the latter purpose, but such simulations are computationally expensive and are prone to systematic biases. Here we produce a high-resolution, 100-member ensemble simulation of surface atmospheric temperature over North America for the 1979–2015 period using a comprehensive spatially extended non-stationary statistical model derived from the data based on the North American Regional Reanalysis. The surrogate climate realizations generated by this model are independent from, yet nearly statistically congruent with reality. This data set provides unique opportunities for the analysis of weather-related risk, with applications in agriculture, energy development, and protection of human life. PMID:29039842

A virtual climate library of surface temperature over North America for 1979-2015.

PubMed

Kravtsov, Sergey; Roebber, Paul; Brazauskas, Vytaras

2017-10-17

The most comprehensive continuous-coverage modern climatic data sets, known as reanalyses, come from combining state-of-the-art numerical weather prediction (NWP) models with diverse available observations. These reanalysis products estimate the path of climate evolution that actually happened, and their use in a probabilistic context-for example, to document trends in extreme events in response to climate change-is, therefore, limited. Free runs of NWP models without data assimilation can in principle be used for the latter purpose, but such simulations are computationally expensive and are prone to systematic biases. Here we produce a high-resolution, 100-member ensemble simulation of surface atmospheric temperature over North America for the 1979-2015 period using a comprehensive spatially extended non-stationary statistical model derived from the data based on the North American Regional Reanalysis. The surrogate climate realizations generated by this model are independent from, yet nearly statistically congruent with reality. This data set provides unique opportunities for the analysis of weather-related risk, with applications in agriculture, energy development, and protection of human life.

EnOI-IAU Initialization Scheme Designed for Decadal Climate Prediction System IAP-DecPreS

NASA Astrophysics Data System (ADS)

Wu, Bo; Zhou, Tianjun; Zheng, Fei

2018-02-01

A decadal climate prediction system named as IAP-DecPreS was constructed in the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, based on a fully coupled model FGOALS-s2 and a newly developed initialization scheme, referred to as EnOI-IAU. In this paper, we introduce the design of the EnOI-IAU scheme, assess the accuracies of initialization integrations using the EnOI-IAU and preliminarily evaluate hindcast skill of the IAP-DecPreS. The EnOI-IAU scheme integrates two conventional assimilation approaches, ensemble optimal interpolation (EnOI) and incremental analysis update (IAU). The EnOI and IAU were applied to calculate analysis increments and incorporate them into the model, respectively. Three continuous initialization (INIT) runs were conducted for the period of 1950-2015, in which observational sea surface temperature (SST) from the HadISST1.1 and subsurface ocean temperature profiles from the EN4.1.1 data set were assimilated. Then nine-member 10 year long hindcast runs initiated from the INIT runs were conducted for each year in the period of 1960-2005. The accuracies of the INIT runs are evaluated from the following three aspects: upper 700 m ocean temperature, temporal evolution of SST anomalies, and dominant interdecadal variability modes, Pacific Decadal Oscillation (PDO) and Atlantic Multidecadal Oscillation (AMO). Finally, preliminary evaluation of the ensemble mean of the hindcast runs suggests that the IAP-DecPreS has skill in the prediction of the PDO-related SST anomalies in the midlatitude North Pacific and AMO-related SST anomalies in the tropical North Atlantic.

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.

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.

The future of the Devon Ice cap: results from climate and ice dynamics modelling

NASA Astrophysics Data System (ADS)

Mottram, Ruth; Rodehacke, Christian; Boberg, Fredrik

2017-04-01

The Devon Ice Cap is an example of a relatively well monitored small ice cap in the Canadian Arctic. Close to Greenland, it shows a similar surface mass balance signal to glaciers in western Greenland. Here we use high resolution (5km) simulations from HIRHAM5 to drive the PISM glacier model in order to model the present day and future prospects of this small Arctic ice cap. Observational data from the Devon Ice Cap in Arctic Canada is used to evaluate the surface mass balance (SMB) data output from the HIRHAM5 model for simulations forced with the ERA-Interim climate reanalysis data and the historical emissions scenario run by the EC-Earth global climate model. The RCP8.5 scenario simulated by EC-Earth is also downscaled by HIRHAM5 and this output is used to force the PISM model to simulate the likely future evolution of the Devon Ice Cap under a warming climate. We find that the Devon Ice Cap is likely to continue its present day retreat, though in the future increased precipitation partly offsets the enhanced melt rates caused by climate change.

System and Method for Providing a Climate Data Persistence Service

NASA Technical Reports Server (NTRS)

Schnase, John L. (Inventor); Ripley, III, William David (Inventor); Duffy, Daniel Q. (Inventor); Thompson, John H. (Inventor); Strong, Savannah L. (Inventor); McInerney, Mark (Inventor); Sinno, Scott (Inventor); Tamkin, Glenn S. (Inventor); Nadeau, Denis (Inventor)

2018-01-01

A system, method and computer-readable storage devices for providing a climate data persistence service. A system configured to provide the service can include a climate data server that performs data and metadata storage and management functions for climate data objects, a compute-storage platform that provides the resources needed to support a climate data server, provisioning software that allows climate data server instances to be deployed as virtual climate data servers in a cloud computing environment, and a service interface, wherein persistence service capabilities are invoked by software applications running on a client device. The climate data objects can be in various formats, such as International Organization for Standards (ISO) Open Archival Information System (OAIS) Reference Model Submission Information Packages, Archive Information Packages, and Dissemination Information Packages. The climate data server can enable scalable, federated storage, management, discovery, and access, and can be tailored for particular use cases.

NASA Downscaling Project: Final Report

NASA Technical Reports Server (NTRS)

Ferraro, Robert; Waliser, Duane; Peters-Lidard, Christa

2017-01-01

A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA - 2 reanalyses were used to drive the NU - WRF regional climate model and a GEOS - 5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000 - 2010. The results of these experiments were compared to observational datasets to evaluate the output.

NASA Downscaling Project

NASA Technical Reports Server (NTRS)

Ferraro, Robert; Waliser, Duane; Peters-Lidard, Christa

2017-01-01

A team of researchers from NASA Ames Research Center, Goddard Space Flight Center, the Jet Propulsion Laboratory, and Marshall Space Flight Center, along with university partners at UCLA, conducted an investigation to explore whether downscaling coarse resolution global climate model (GCM) predictions might provide valid insights into the regional impacts sought by decision makers. Since the computational cost of running global models at high spatial resolution for any useful climate scale period is prohibitive, the hope for downscaling is that a coarse resolution GCM provides sufficiently accurate synoptic scale information for a regional climate model (RCM) to accurately develop fine scale features that represent the regional impacts of a changing climate. As a proxy for a prognostic climate forecast model, and so that ground truth in the form of satellite and in-situ observations could be used for evaluation, the MERRA and MERRA-2 reanalyses were used to drive the NU-WRF regional climate model and a GEOS-5 replay. This was performed at various resolutions that were at factors of 2 to 10 higher than the reanalysis forcing. A number of experiments were conducted that varied resolution, model parameterizations, and intermediate scale nudging, for simulations over the continental US during the period from 2000-2010. The results of these experiments were compared to observational datasets to evaluate the output.

Analysis of precipitation teleconnections in CMIP models as a measure of model fidelity in simulating precipitation

NASA Astrophysics Data System (ADS)

Langenbrunner, B.; Neelin, J.; Meyerson, J.

2011-12-01

The accurate representation of precipitation is a recurring issue in global climate models, especially in the tropics. Poor skill in modeling the variability and climate teleconnections associated with El Niño/Southern Oscillation (ENSO) also persisted in the latest Climate Model Intercomparison Project (CMIP) campaigns. Observed ENSO precipitation teleconnections provide a standard by which we can judge a given model's ability to reproduce precipitation and dynamic feedback processes originating in the tropical Pacific. Using CMIP3 Atmospheric Model Intercomparison Project (AMIP) runs as a baseline, we compare precipitation teleconnections between models and observations, and we evaluate these results against available CMIP5 historical and AMIP runs. Using AMIP simulations restricts evaluation to the atmospheric response, as sea surface temperatures (SSTs) in AMIP are prescribed by observations. We use a rank correlation between ENSO SST indices and precipitation to define teleconnections, since this method is robust to outliers and appropriate for non-Gaussian data. Spatial correlations of the modeled and observed teleconnections are then evaluated. We look at these correlations in regions of strong precipitation teleconnections, including equatorial S. America, the "horseshoe" region in the western tropical Pacific, and southern N. America. For each region and season, we create a "normalized projection" of a given model's teleconnection pattern onto that of the observations, a metric that assesses the quality of regional pattern simulations while rewarding signals of correct sign over the region. Comparing this to an area-averaged (i.e., more generous) metric suggests models do better when restrictions on exact spatial dependence are loosened and conservation constraints apply. Model fidelity in regional measures remains far from perfect, suggesting intrinsic issues with the models' regional sensitivities in moist processes.

Hydrogeologic setting and preliminary estimates of hydrologic components for Bull Run Lake and the Bull Run Lake drainage basin, Multnomah and Clackamas counties, Oregon

USGS Publications Warehouse

Snyder, Daniel T.; Brownell, Dorie L.

1996-01-01

Suggestions for further study include (1) evaluation of the surface-runoff component of inflow to the lake; (2) use of a cross-sectional ground-water flow model to estimate ground-water inflow, outflow, and storage; (3) additional data collection to reduce the uncertainties of the hydrologic components that have large relative uncertainties; and (4) determination of long-term trends for a wide range of climatic and hydrologic conditions.

The effect of surface boundary conditions on the climate generated by a coarse-mesh general circulation model

NASA Technical Reports Server (NTRS)

Cohen, C.

1981-01-01

A hierarchy of experiments was run, starting with an all water planet with zonally symmetric sea surface temperatures, then adding, one at a time, flat continents, mountains, surface physics, and realistic sea surface temperatures. The model was run with the sun fixed at a perpetual January. Ensemble means and standard deviations were computed and the t-test was used to determine the statistical significance of the results. The addition of realistic surface physics does not affect the model climatology to as large as extent as does the addition of mountains. Departures from zonal symmetry of the SST field result in a better simulation of the real atmosphere.

Bringing Water into an Integrated Assessment Framework

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

Izaurralde, Roberto C.; Thomson, Allison M.; Sands, Ronald

We developed a modeling capability to understand how water is allocated within a river basin and examined present and future water allocations among agriculture, energy production, other human requirements, and ecological needs. Water is an essential natural resource needed for food and fiber production, household and industrial uses, energy production, transportation, tourism and recreation, and the functioning of natural ecosystems. Anthropogenic climate change and population growth are anticipated to impose unprecedented pressure on water resources during this century. Pacific Northwest National Laboratory (PNNL) researchers have pioneered the development of integrated assessment (IA) models for the analysis of energy and economicmore » systems under conditions of climate change. This Laboratory Directed Research and Development (LDRD) effort led to the development of a modeling capability to evaluate current and future water allocations between human requirements and ecosystem services. The Water Prototype Model (WPM) was built in STELLA®, a computer modeling package with a powerful interface that enables users to construct dynamic models to simulate and integrate many processes (biological, hydrological, economics, sociological). A 150,404-km2 basin in the United States (U.S.) Pacific Northwest region served as the platform for the development of the WPM. About 60% of the study basin is in the state of Washington with the rest in Oregon. The Columbia River runs through the basin for 874 km, starting at the international border with Canada and ending (for the purpose of the simulation) at The Dalles dam. Water enters the basin through precipitation and from streamflows originating from the Columbia River at the international border with Canada, the Spokane River, and the Snake River. Water leaves the basin through evapotranspiration, consumptive uses (irrigation, livestock, domestic, commercial, mining, industrial, and off-stream power generation), and streamflow through The Dalles dam. Water also enters the Columbia River via runoff from land. The model runs on a monthly timescale to account for the impact of seasonal variations of climate, streamflows, and water uses. Data for the model prototype were obtained from national databases and ecosystem model results. The WPM can be run from three sources: 1) directly from STELLA, 2) with the isee Player®, or 3) the web version of WPM constructed with NetSim® software. When running any of these three versions, the user is presented a screen with a series of buttons, graphs, and a table. Two of the buttons provide the user with background and instructions on how to run the model. Currently, there are five types of scenarios that can be manipulated alone or in combination using the Sliding Input Devices: 1) interannual variability (e.g., El Niño), 2) climate change, 3) salmon policy, 4) future population, and 5) biodiesel production. Overall, the WPM captured the effects of streamflow conditions on hydropower production. Under La Niña conditions, more hydropower is available during all months of the year, with a substantially higher availability during spring and summer. Under El Niño conditions, hydropower would be reduced, with a total decline of 15% from normal weather conditions over the year. A policy of flow augmentation to facilitate the spring migration of smolts to the ocean would also reduce hydropower supply. Modeled hydropower generation was 23% greater than the 81 TWh reported in the 1995 U.S. Geological Survey (USGS) database. The modeling capability presented here contains the essential features to conduct basin-scale analyses of water allocation under current and future climates. Due to its underlying data structure iv and conceptual foundation, the WPM should be appropriate to conduct IA modeling at national and global scales.« less

Aerosols and Clouds: In Cahoots to Change Climate

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

Berg, Larry

Key knowledge gaps persist despite advances in the scientific understanding of how aerosols and clouds evolve and affect climate. The Two-Column Aerosol Project, or TCAP, was designed to provide a detailed set of observations to tackle this area of unknowns. Led by PNNL atmospheric scientist Larry Berg, ARM's Climate Research Facility was deployed in Cape Cod, Massachusetts for the 12-month duration of TCAP, which came to a close in June 2013. "We are developing new tools to look at particle chemistry, like our mass spectrometer used in TCAP that can tell us the individual chemical composition of an aerosol," saidmore » Berg. "Then, we'll run our models and compare it with the data that we have to make sure we're getting correct answers and make sure our climate models are reflecting the best information."« less

Aerosols and Clouds: In Cahoots to Change Climate

ScienceCinema

Berg, Larry

2018-01-16

Key knowledge gaps persist despite advances in the scientific understanding of how aerosols and clouds evolve and affect climate. The Two-Column Aerosol Project, or TCAP, was designed to provide a detailed set of observations to tackle this area of unknowns. Led by PNNL atmospheric scientist Larry Berg, ARM's Climate Research Facility was deployed in Cape Cod, Massachusetts for the 12-month duration of TCAP, which came to a close in June 2013. "We are developing new tools to look at particle chemistry, like our mass spectrometer used in TCAP that can tell us the individual chemical composition of an aerosol," said Berg. "Then, we'll run our models and compare it with the data that we have to make sure we're getting correct answers and make sure our climate models are reflecting the best information."

Biosphere-Atmosphere Transfer Scheme (BATS) version le as coupled to the NCAR community climate model. Technical note. [NCAR (National Center for Atmospheric Research)

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

Dickinson, R.E.; Henderson-Sellers, A.; Kennedy, P.J.

A comprehensive model of land-surface processes has been under development suitable for use with various National Center for Atmospheric Research (NCAR) General Circulation Models (GCMs). Special emphasis has been given to describing properly the role of vegetation in modifying the surface moisture and energy budgets. The result of these efforts has been incorporated into a boundary package, referred to as the Biosphere-Atmosphere Transfer Scheme (BATS). The current frozen version, BATS1e is a piece of software about four thousand lines of code that runs as an offline version or coupled to the Community Climate Model (CCM).

A Nonlinear Model for Interactive Data Analysis and Visualization and an Implementation Using Progressive Computation for Massive Remote Climate Data Ensembles

NASA Astrophysics Data System (ADS)

Christensen, C.; Liu, S.; Scorzelli, G.; Lee, J. W.; Bremer, P. T.; Summa, B.; Pascucci, V.

2017-12-01

The creation, distribution, analysis, and visualization of large spatiotemporal datasets is a growing challenge for the study of climate and weather phenomena in which increasingly massive domains are utilized to resolve finer features, resulting in datasets that are simply too large to be effectively shared. Existing workflows typically consist of pipelines of independent processes that preclude many possible optimizations. As data sizes increase, these pipelines are difficult or impossible to execute interactively and instead simply run as large offline batch processes. Rather than limiting our conceptualization of such systems to pipelines (or dataflows), we propose a new model for interactive data analysis and visualization systems in which we comprehensively consider the processes involved from data inception through analysis and visualization in order to describe systems composed of these processes in a manner that facilitates interactive implementations of the entire system rather than of only a particular component. We demonstrate the application of this new model with the implementation of an interactive system that supports progressive execution of arbitrary user scripts for the analysis and visualization of massive, disparately located climate data ensembles. It is currently in operation as part of the Earth System Grid Federation server running at Lawrence Livermore National Lab, and accessible through both web-based and desktop clients. Our system facilitates interactive analysis and visualization of massive remote datasets up to petabytes in size, such as the 3.5 PB 7km NASA GEOS-5 Nature Run simulation, previously only possible offline or at reduced resolution. To support the community, we have enabled general distribution of our application using public frameworks including Docker and Anaconda.

30-year Dynamics of Terrestrial Vegetation Activity and the Relationship with Climatologies

NASA Astrophysics Data System (ADS)

de Jong, R.; Schaepman, M. E.; Furrer, R.; de Bruin, S.; Verburg, P. H.

2013-12-01

The climate governs the seasonal activity of terrestrial vegetation while humankind influences it. The relative role of these drivers in changing vegetation activity is crucial information for accurate modeling of vegetation and climate dynamics and for adaptation and mitigation strategies. Disentangling the two, however, is an ongoing scientific challenge, because of limited data availability, mainly regarding non-climatic drivers, and complex biosphere-atmosphere feedback mechanisms. Here, we contribute to this quest by modeling the spatial relationship between climatologies and changes in global vegetation activity (de Jong et al., 2013a). Vegetation activity is commonly quantified using remotely sensed vegetation indices (VI). Extensive reports on temporal trends over the past decades in time series of such indices can be found in literature, including the detection of shifts (de Jong et al., 2013b), which may be related to climate (e.g. Zhao & Running, 2010). However, little remains known about the exact processes underlying vegetation change at large spatial scales. Depending on eco-region, three climatologies potentially constrain plant growth (Churkina and Running, 1998). In the humid mid-latitudes, for example, temperature is the largest influencing factor; in (semi) arid regions it is the availability of water and in the tropics incident solar radiation. Based on this logic, we developed a mixed-effect model to relate changes in these climatologies to changes in vegetation activity and to quantify the spatial process underlying the other drivers, including human land use. Little over 50% of the spatial variation in vegetation change could be attributed to changes in climatologies; conspicuously, many of the global ';greening' trends and the ';browning' hotspots in Argentina and Australia. Browning hotspots in the non-climatic component were especially located in subequatorial Africa (e.g. parts of Zimbabwe and Tanzania), where human drivers may be responsible. Indications for browning under warming conditions were found in some boreal regions. These results are examples of relationships we can find within readily available datasets, without a-priori information, and may be used as indicator for drivers of biospheric change. Churkina G, Running SW (1998) Contrasting climatic controls on the estimated productivity of global terrestrial biomes. Ecosystems, 1, 206-215 de Jong R, Schaepman ME, Furrer R, De Bruin S, Verburg PH (2013a) Spatial relationship between climatologies and changes in global vegetation activity. Global Change Biology, 19, 1953-1964 de Jong, R, Verbesselt, J, Zeileis, A, & Schaepman, ME (2013b) Shifts in Global Vegetation Activity Trends. Remote Sensing, 5, 1117-1133 Zhao M, Running SW (2010) Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009. Science, 329, 940-943

Impact of climate change on global malaria distribution.

PubMed

Caminade, Cyril; Kovats, Sari; Rocklov, Joacim; Tompkins, Adrian M; Morse, Andrew P; Colón-González, Felipe J; Stenlund, Hans; Martens, Pim; Lloyd, Simon J

2014-03-04

Malaria is an important disease that has a global distribution and significant health burden. The spatial limits of its distribution and seasonal activity are sensitive to climate factors, as well as the local capacity to control the disease. Malaria is also one of the few health outcomes that has been modeled by more than one research group and can therefore facilitate the first model intercomparison for health impacts under a future with climate change. We used bias-corrected temperature and rainfall simulations from the Coupled Model Intercomparison Project Phase 5 climate models to compare the metrics of five statistical and dynamical malaria impact models for three future time periods (2030s, 2050s, and 2080s). We evaluated three malaria outcome metrics at global and regional levels: climate suitability, additional population at risk and additional person-months at risk across the model outputs. The malaria projections were based on five different global climate models, each run under four emission scenarios (Representative Concentration Pathways, RCPs) and a single population projection. We also investigated the modeling uncertainty associated with future projections of populations at risk for malaria owing to climate change. Our findings show an overall global net increase in climate suitability and a net increase in the population at risk, but with large uncertainties. The model outputs indicate a net increase in the annual person-months at risk when comparing from RCP2.6 to RCP8.5 from the 2050s to the 2080s. The malaria outcome metrics were highly sensitive to the choice of malaria impact model, especially over the epidemic fringes of the malaria distribution.

Impact of climate change on global malaria distribution

PubMed Central

Caminade, Cyril; Kovats, Sari; Rocklov, Joacim; Tompkins, Adrian M.; Morse, Andrew P.; Colón-González, Felipe J.; Stenlund, Hans; Martens, Pim; Lloyd, Simon J.

2014-01-01

Malaria is an important disease that has a global distribution and significant health burden. The spatial limits of its distribution and seasonal activity are sensitive to climate factors, as well as the local capacity to control the disease. Malaria is also one of the few health outcomes that has been modeled by more than one research group and can therefore facilitate the first model intercomparison for health impacts under a future with climate change. We used bias-corrected temperature and rainfall simulations from the Coupled Model Intercomparison Project Phase 5 climate models to compare the metrics of five statistical and dynamical malaria impact models for three future time periods (2030s, 2050s, and 2080s). We evaluated three malaria outcome metrics at global and regional levels: climate suitability, additional population at risk and additional person-months at risk across the model outputs. The malaria projections were based on five different global climate models, each run under four emission scenarios (Representative Concentration Pathways, RCPs) and a single population projection. We also investigated the modeling uncertainty associated with future projections of populations at risk for malaria owing to climate change. Our findings show an overall global net increase in climate suitability and a net increase in the population at risk, but with large uncertainties. The model outputs indicate a net increase in the annual person-months at risk when comparing from RCP2.6 to RCP8.5 from the 2050s to the 2080s. The malaria outcome metrics were highly sensitive to the choice of malaria impact model, especially over the epidemic fringes of the malaria distribution. PMID:24596427

Current and future groundwater recharge in West Africa as estimated from a range of coupled climate model outputs

NASA Astrophysics Data System (ADS)

Verhoef, Anne; Cook, Peter; Black, Emily; Macdonald, David; Sorensen, James

2017-04-01

This research addresses the terrestrial water balance for West Africa. Emphasis is on the prediction of groundwater recharge and how this may change in the future, which has relevance to the management of surface and groundwater resources. The study was conducted as part of the BRAVE research project, "Building understanding of climate variability into planning of groundwater supplies from low storage aquifers in Africa - Second Phase", funded under the NERC/DFID/ESRC Programme, Unlocking the Potential of Groundwater for the Poor (UPGro). We used model output data of water balance components (precipitation, surface and subsurface run-off, evapotranspiration and soil moisture content) from ERA-Interim/ERA-LAND reanalysis, CMIP5, and high resolution model runs with HadGEM3 (UPSCALE; Mizielinski et al., 2014), for current and future time-periods. Water balance components varied widely between the different models; variation was particularly large for sub-surface runoff (defined as drainage from the bottom-most soil layer of each model). In-situ data for groundwater recharge obtained from the peer-reviewed literature were compared with the model outputs. Separate off-line model sensitivity studies with key land surface models were performed to gain understanding of the reasons behind the model differences. These analyses were centered on vegetation, and soil hydraulic parameters. The modelled current and future recharge time series that had the greatest degree of confidence were used to examine the spatiotemporal variability in groundwater storage. Finally, the implications for water supply planning were assessed. Mizielinski, M.S. et al., 2014. High-resolution global climate modelling: the UPSCALE project, a large-simulation campaign. Geoscientific Model Development, 7(4), pp.1629-1640.

Uncertainty Quantification and Sensitivity Analysis in the CICE v5.1 Sea Ice Model

NASA Astrophysics Data System (ADS)

Urrego-Blanco, J. R.; Urban, N. M.

2015-12-01

Changes in the high latitude climate system have the potential to affect global climate through feedbacks with the atmosphere and connections with mid latitudes. Sea ice and climate models used to understand these changes have uncertainties that need to be characterized and quantified. In this work we characterize parametric uncertainty in Los Alamos Sea Ice model (CICE) and quantify the sensitivity of sea ice area, extent and volume with respect to uncertainty in about 40 individual model parameters. Unlike common sensitivity analyses conducted in previous studies where parameters are varied one-at-a-time, this study uses a global variance-based approach in which Sobol sequences are used to efficiently sample the full 40-dimensional parameter space. This approach requires a very large number of model evaluations, which are expensive to run. A more computationally efficient approach is implemented by training and cross-validating a surrogate (emulator) of the sea ice model with model output from 400 model runs. The emulator is used to make predictions of sea ice extent, area, and volume at several model configurations, which are then used to compute the Sobol sensitivity indices of the 40 parameters. A ranking based on the sensitivity indices indicates that model output is most sensitive to snow parameters such as conductivity and grain size, and the drainage of melt ponds. The main effects and interactions among the most influential parameters are also estimated by a non-parametric regression technique based on generalized additive models. It is recommended research to be prioritized towards more accurately determining these most influential parameters values by observational studies or by improving existing parameterizations in the sea ice model.

SENSITIVITY OF THE REGIONAL WATER BALANCE IN THE COLUMBIA RIVER BASIN TO CLIMATE VARIABILITY: APPLICATION OF A SPATIALLY DISTRIBUTED WATER BALANCE MODEL

EPA Science Inventory

A one-dimensional water balance model was developed and used to simulate water balance for the Columbia River Basin. he model was run over a 10 km X 10 km grid for the United State's portion of the basin. he regional water balance was calculated using a monthly time-step for a re...

Quantifying the Aerosol Semi-Direct Effect in the NASA GEOS-5 AGCM

NASA Technical Reports Server (NTRS)

Randles, Cynthia A.; Colarco, Peter R.; daSilva, Arlindo

2011-01-01

Aerosols such as black carbon, dust, and some organic carbon species both scatter and absorb incoming solar radiation. This direct aerosol radiative forcing (DARF) redistributes solar energy both by cooling the surface and warming the atmosphere. As a result, these aerosols affect atmospheric stability and cloud cover (the semi-direct effect, or SDE). Furthermore, in regions with persistent high loadings of absorbing aerosols (e.g. Asia), regional circulation patterns may be altered, potentially resulting in changes in precipitation patterns. Here we investigate aerosol-climate coupling using the NASA Goddard Earth Observing System model version 5 (GEOS-5) atmospheric general circulation model (AGCM), in which we have implemented an online version of the Goddard Chemistry, Aerosol, Radiation and Transport (GOCART) model. GOCART includes representations of the sources, sinks, and chemical transformation of externally mixed dust, sea salt, sulfate, and carbonaceous aerosols. We examine a series of free-running ensemble climate simulations of the present-day period (2000-2009) forced by observed sea surface temperatures to determine the impact of aerosols on the model climate. The SDE and response of each simulation is determined by differencing with respect to the control simulation (no aerosol forcing). In a free-running model, any estimate of the SDE includes changes in clouds due both to atmospheric heating from aerosols and changes in circulation. To try and quantify the SDE without these circulation changes we then examine the DARF and SDE in GEOS-5 with prescribed meteorological analyses introduced by the MERRA analysis. By doing so, we are able to examine changes in model clouds that occur on shorter scales (six hours). In the GEOS-5 data assimilation system (DAS), the analysis is defined as the best estimate of the atmospheric state at any given time, and it is determined by optimally combining a first-guess short-term GCM forecast with all available observations. The Incremental Analysis Update (IAU) is added to the model forecast tendencies to align them with the analysis every six hours, thus preventing longer timescale feedbacks due to the aerosol forcing. We calculate the SDE by comparing model runs with and without aerosols, and the difference in the IAU between these runs is a useful metric with which to evaluate the impact of the SDE on the model atmosphere and clouds. Decreasing the IAU indicates that the aerosol direct and semi-direct effects act to reduce the bias between the model and observations and vice versa.

Interactions of Mean Climate Change and Climate Variability on Food Security Extremes

NASA Technical Reports Server (NTRS)

Ruane, Alexander C.; McDermid, Sonali; Mavromatis, Theodoros; Hudson, Nicholas; Morales, Monica; Simmons, John; Prabodha, Agalawatte; Ahmad, Ashfaq; Ahmad, Shakeel; Ahuja, Laj R.

2015-01-01

Recognizing that climate change will affect agricultural systems both through mean changes and through shifts in climate variability and associated extreme events, we present preliminary analyses of climate impacts from a network of 1137 crop modeling sites contributed to the AgMIP Coordinated Climate-Crop Modeling Project (C3MP). At each site sensitivity tests were run according to a common protocol, which enables the fitting of crop model emulators across a range of carbon dioxide, temperature, and water (CTW) changes. C3MP can elucidate several aspects of these changes and quantify crop responses across a wide diversity of farming systems. Here we test the hypothesis that climate change and variability interact in three main ways. First, mean climate changes can affect yields across an entire time period. Second, extreme events (when they do occur) may be more sensitive to climate changes than a year with normal climate. Third, mean climate changes can alter the likelihood of climate extremes, leading to more frequent seasons with anomalies outside of the expected conditions for which management was designed. In this way, shifts in climate variability can result in an increase or reduction of mean yield, as extreme climate events tend to have lower yield than years with normal climate.C3MP maize simulations across 126 farms reveal a clear indication and quantification (as response functions) of mean climate impacts on mean yield and clearly show that mean climate changes will directly affect the variability of yield. Yield reductions from increased climate variability are not as clear as crop models tend to be less sensitive to dangers on the cool and wet extremes of climate variability, likely underestimating losses from water-logging, floods, and frosts.

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.

Clustering of Global Climate Models outputs as a tool for scenario-based risk assessment

NASA Astrophysics Data System (ADS)

R Pereira, V.; Zullo, J., Jr.; Avila, A. M. H. D.

2016-12-01

The choice of the Global Climate Models (GCMs) future projections outputs for the scenario based risk assessment studies is a challenge for the non-climate models scientists. This study presents a method to select a range of the GCMs scenarios for regional/continental agriculture studies. The technique proposed here is based on grouping the surface air temperature (tas) anomalies in a continental /regional scale - in Brazil-South America - projected by the AR5-CMIP5-GCMs. We run the k-means cluster algorithm and the silhouette method to identify the optimal number and to group the GCMs tas outputs under the rcp 8.5. We applied the delta method to calculate the near future climate change. This method is based on the difference between the future and the baseline in a 30 year running mean periods basis. The future considered here is the 2021-2050 [2030s] and the baseline is the period of 1976-2005 (1980s). As expected, all the models projections showed increases in tas in the near future, ranging from ≅ 3.6 to 0.2 oC. The k-means clustering clearly indicates 5 groups of GCMs tas deltas. The majority of GCMs indicated an intermediate future temperature changes. There is a group of 12 GCMs that is indicating an average change of ≅ 2 oC and another group of 16 indicating ≅ 1 oC. The other two groups with 3 GCMs each indicated a most extreme tas scenario - 0.2 and 3.6 oC respectively. The results were in agreement with previous studies with the AR4 GCMs in which the Miroc5 and HADGEM ES predecessors were classified in different groups of models. The results also allowed us to gradually access the optimist - pessimist groups of 34 GCMs that is a good reference to guide the public policy demands for agriculture under climate change conditions.

Uncertainties in Hydrologic and Climate Change Impact Analysis in Major Watersheds in British Columbia, Canada

NASA Astrophysics Data System (ADS)

Bennett, K. E.; Schnorbus, M.; Werner, A. T.; Music, B.; Caya, D.; Rodenhuis, D. R.

2009-12-01

Uncertainties in the projections of future hydrologic change can be assessed using a suite of tools, thereby allowing researchers to focus on improvement to identifiable sources of uncertainty. A pareto set of optimal hydrologic parameterizations was run for three BC watersheds (Fraser, Peace and Columbia) for a range of downscaled Global Climate Model (GCM) emission scenarios to illustrate the uncertainty in hydrologic response to climate change. Results show varying responses of hydrologic regimes across geographic landscapes. Uncertainties in streamflow and water balance (runoff, evapo-transpiration, snow water equivalent, soil moisture) were analysed by forcing the Variable Infiltration Capacity (VIC) hydrologic model, run under twenty-five optimal parameter solution sets using six Bias-Corrected Statistically Downscaled (BCSD) GCM emission scenario projections for the 2050s and the 2080s. Projected changes by the 2050s include increased winter flows, increases and decreases in freshet magnitude depending on the scenario, and decreases in summer flows persisting until September. Winter runoff had the greatest range between GCM emission scenarios, while the hydrologic parameters within individual GCM emission scenarios had a winter runoff range an order of magnitude smaller. Evapo-transpiration, snow water equivalent and soil moisture exhibited a spread of ~10% or less. Streamflow changes by the 2080s lie outside the natural range of historic variability over the winter and spring. Results indicate that the changes projected between GCM emission scenarios are greater than the differences between the hydrologic model parameterizations. An alternate tool, the Canadian Regional Climate Model (CRCM) has been set up for these watersheds and various runs have been analysed to determine the range and variability present and to examine these results in comparison to the hydrologic model projections. The CRCM range and variability is an improvement over the Canadian GCM and thus requires less bias correction. However, without downscaling the CRCM results are still coarser than what is required to drive macroscale hydrologic models, such as VIC. Applying these tools has illustrated the importance of focusing on improved downscaling efforts, including downscaling CRCM results rather than CGCM data. Tools for decision-making in the face of uncertainty are emerging as a priority for the climate change impacts community, and there is a need to focus on incorporating uncertainty information along with the projection of impacts. Assessing uncertainty across a range of regimes and geographic regions can assist to identify the main sources of uncertainty and allow researchers to focus on improving those sources using more robust methodological approaches and tools.

Effects of climate change on phenology in two French LTER (Alps and Brittany) for the period 1998-2009

NASA Astrophysics Data System (ADS)

Perrimond, B.; Bigot, S.; Quénol, H.; Spielgelberger, T.; Baudry, J.

2012-04-01

Climate and vegetation are linked all over the world. In this study, we work on a seasonal weather classification based on air temperature and precipitation to deduce a link with different phenological stage (greening up, senescence, ...) over a 12 year period (1998-2009) for two different domains in France (Alps and Brittany). In temperate land, the main climatic variable with a potential effect on vegetation is the mean temperature followed by the rainfall deficit. A better understanding in season and their climatic characteristic is need to establish link between climate and phenology; so a weather classification is proposed based on empirical orthogonal functions and ascending hierarchical classification on atmospheric variables. This classification allows us to exhibit the inter-annual and intra-seasonal climatic spatiotemporal variability for both experimental site. Relationships between climate and phenology consist in a comparison between advance and delay in phenological stage and weather type issue from the classification. Experiment field are two french Long Term Ecological Research (LTER). The first one (LTER 'Alps' ) have mountain characteristics about 1000 to 4780 m ASL, ~65% of forest occupation ; the second one (LTER Armorique) is an Atlantic coastal landscape, 0-360 m ASL, ~70% of agricultural field. Climatic data are SAFRAN-France reanalysis which are developed to run SVAT model and come from the French meteorological service 'Météo-France'. All atmospheric variable needed to run a hydrological model are available (air temperature, rainfall/snowfall, wind speed, relative humidity, incoming/outcoming radiation) at a 8-8 km2 space resolution and with a daily time resolution. The phenological data are extracted from SPOT-VGT product 1-1 km2 space resolution and 10 days time resolution) by time series analysis process. Such of study is particularly important to understand relationships between environmental and ecological variables and it will allow to better predict ecological reaction under climate change constraint.

Investigating the Greenland ice sheet evolution under changing climate using a three-dimensional full-Stokes model

NASA Astrophysics Data System (ADS)

Seddik, H.; Greve, R.; Zwinger, T.; Gillet-Chaulet, F.; Gagliardini, O.

2010-12-01

A three-dimensional, thermo-mechanically coupled model is applied to the Greenland ice sheet. The model implements the full-Stokes equations for the ice dynamics, and the system is solved with the finite-element method (FEM) using the open source multi-physics package Elmer (http://www.csc.fi/elmer/). The finite-element mesh for the computational domain has been created using the Greenland surface and bedrock DEM data with a spatial resolution of 5 km (SeaRise community effort, based on Bamber and others, 2001). The study is particularly aimed at better understanding the ice dynamics near the major Greenland ice streams. The meshing procedure starts with the bedrock footprint where a mesh with triangle elements and a resolution of 5 km is constructed. Since the resulting mesh is unnecessarily dense in areas with slow ice dynamics, an anisotropic mesh adaptation procedure has been introduced. Using the measured surface velocities to evaluate the Hessian matrix of the velocities, a metric tensor is computed at the mesh vertices in order to define the adaptation scheme. The resulting meshed footprint obtained with the automatic tool YAMS shows a high density of elements in the vicinities of the North-East Greenland Ice Stream (NEGIS), the Jakobshavn ice stream (JIS) and the Kangerdlugssuaq (KL) and Helheim (HH) glaciers. On the other hand, elements with a coarser resolution are generated away from the ice streams and domain margins. The final three-dimensional mesh is obtained by extruding the 2D footprint with 21 vertical layers, so that the resulting mesh contains 400860 wedge elements and 233583 nodes. The numerical solution of the Stokes and the heat transfer equations involves direct and iterative solvers depending on the simulation case, and both methods are coupled with stabilization procedures. The boundary conditions are such that the temperature at the surface uses the present-day mean annual air temperature given by a parameterization or directly from the available data, the geothermal heat flux at the bedrock is prescribed as spatially constant and the lateral sides are open boundaries. A non-linear Weertman law is used for the basal sliding. The project goal is to better assess the effects of dynamical changes of the Greenland ice sheet on sea level rise under global-warming conditions. Hence, the simulations have been conducted in order to investigate the ice sheet evolution using the climate forcing experiments defined in the SeaRISE project. For that purpose, four different experiments have been conducted, (i) constant climate control run beginning at present (epoch 2004-1-1 0:0:0) and running up to 500 years holding the climate constant to its present state, (ii) constant climate forcing with increased basal lubrication, (iii) AR4 climate run forced by anomalies derived from results given in the IPCC 4th Assessment Report (AR4) for the A1B emission scenario, (iv) AR4 climate run with increased basal lubrication.

Impacts of future climate change on river discharge based on hydrological inference: A case study of the Grand River Watershed in Ontario, Canada.

PubMed

Li, Zhong; Huang, Guohe; Wang, Xiuquan; Han, Jingcheng; Fan, Yurui

2016-04-01

Over the recent years, climate change impacts have been increasingly studied at the watershed scale. However, the impact assessment is strongly dependent upon the performance of the climatic and hydrological models. This study developed a two-step method to assess climate change impacts on water resources based on the Providing Regional Climates for Impacts Studies (PRECIS) modeling system and a Hydrological Inference Model (HIM). PRECIS runs provided future temperature and precipitation projections for the watershed under the Intergovernmental Panel on Climate Change SRES A2 and B2 emission scenarios. The HIM based on stepwise cluster analysis is developed to imitate the complex nonlinear relationships between climate input variables and targeted hydrological variables. Its robust mathematical structure and flexibility in predictor selection makes it a desirable tool for fully utilizing various climate modeling outputs. Although PRECIS and HIM cannot fully cover the uncertainties in hydro-climate modeling, they could provide efficient decision support for investigating the impacts of climate change on water resources. The proposed method is applied to the Grand River Watershed in Ontario, Canada. The model performance is demonstrated with comparison to observation data from the watershed during the period 1972-2006. Future river discharge intervals that accommodate uncertainties in hydro-climatic modeling are presented and future river discharge variations are analyzed. The results indicate that even though the total annual precipitation would not change significantly in the future, the inter-annual distribution is very likely to be altered. The water availability is expected to increase in Winter while it is very likely to decrease in Summer over the Grand River Watershed, and adaptation strategies would be necessary. Copyright © 2016 Elsevier B.V. All rights reserved.

Future Climate Impacts on Crop Water Demand and Groundwater Longevity in Agricultural Regions

NASA Astrophysics Data System (ADS)

Russo, T. A.; Sahoo, S.; Elliott, J. W.; Foster, I.

2016-12-01

Improving groundwater management practices under future drought conditions in agricultural regions requires three steps: 1) estimating the impacts of climate and drought on crop water demand, 2) projecting groundwater availability given climate and demand forcing, and 3) using this information to develop climate-smart policy and water use practices. We present an innovative combination of models to address the first two steps, and inform the third. Crop water demand was simulated using biophysical crop models forced by multiple climate models and climate scenarios, with one case simulating climate adaptation (e.g. modify planting or harvest time) and another without adaptation. These scenarios were intended to represent a range of drought projections and farm management responses. Nexty, we used projected climate conditions and simulated water demand across the United States as inputs to a novel machine learning-based groundwater model. The model was applied to major agricultural regions relying on the High Plains and Mississippi Alluvial aquifer systems in the US. The groundwater model integrates input data preprocessed using single spectrum analysis, mutual information, and a genetic algorithm, with an artificial neural network model. Model calibration and test results indicate low errors over the 33 year model run, and strong correlations to groundwater levels in hundreds of wells across each aquifer. Model results include a range of projected groundwater level changes from the present to 2050, and in some regions, identification and timeframe of aquifer depletion. These results quantify aquifer longevity under climate and crop scenarios, and provide decision makers with the data needed to compare scenarios of crop water demand, crop yield, and groundwater response, as they aim to balance water sustainability with food security.

The foundation for climate services in Belgium: CORDEX.be

NASA Astrophysics Data System (ADS)

Van Schaeybroeck, Bert; Termonia, Piet; De Ridder, Koen; Fettweis, Xavier; Gobin, Anne; Luyten, Patrick; Marbaix, Philippe; Pottiaux, Eric; Stavrakou, Trissevgeni; Van Lipzig, Nicole; van Ypersele, Jean-Pascal; Willems, Patrick

2017-04-01

According to the Global Framework for Climate Services (GFCS) there are four pillars required to build climate services. As the first step towards the realization of a climate center in Belgium, the national project CORDEX.be focused on one pillar: research modelling and projection. By bringing together the Belgian climate and impact modeling research of nine groups a data-driven capacity development and community building in Belgium based on interactions with users. The project is based on the international CORDEX ("COordinated Regional Climate Downscaling Experiment") project where ".be" indicates it will go beyond for Belgium. Our national effort links to the regional climate initiatives through the contribution of multiple high-resolution climate simulations over Europe following the EURO-CORDEX guidelines. Additionally the same climate simulations were repeated at convection-permitting resolutions over Belgium (3 to 5 km). These were used to drive different local impact models to investigate the impact of climate change on urban effects, storm surges and waves, crop production and changes in emissions from vegetation. Akin to international frameworks such as CMIP and CORDEX a multi-model approach is adopted allowing for uncertainty estimation, a crucial aspect of climate projections for policy-making purposes. However, due to the lack of a large set of high resolution model runs, a combination of all available climate information is supplemented with the statistical downscaling approach. The organization of the project, together with its main results will be outlined. The proposed coordination framework could serve as a demonstration case for regions or countries where the climate-research capacity is present but a structure is required to assemble it coherently. Based on interactions and feedback with stakeholders different applications are planned, demonstrating the use of the climate data.

Seasonal predictions of equatorial Atlantic SST in a low-resolution CGCM with surface heat flux correction

NASA Astrophysics Data System (ADS)

Dippe, Tina; Greatbatch, Richard; Ding, Hui

2016-04-01

The dominant mode of interannual variability in tropical Atlantic sea surface temperatures (SSTs) is the Atlantic Niño or Zonal Mode. Akin to the El Niño-Southern Oscillation in the Pacific sector, it is able to impact the climate both of the adjacent equatorial African continent and remote regions. Due to heavy biases in the mean state climate of the equatorial-to-subtropical Atlantic, however, most state-of-the-art coupled global climate models (CGCMs) are unable to realistically simulate equatorial Atlantic variability. In this study, the Kiel Climate Model (KCM) is used to investigate the impact of a simple bias alleviation technique on the predictability of equatorial Atlantic SSTs. Two sets of seasonal forecasting experiments are performed: An experiment using the standard KCM (STD), and an experiment with additional surface heat flux correction (FLX) that efficiently removes the SST bias from simulations. Initial conditions for both experiments are generated by the KCM run in partially coupled mode, a simple assimilation technique that forces the KCM with observed wind stress anomalies and preserves SST as a fully prognostic variable. Seasonal predictions for both sets of experiments are run four times yearly for 1981-2012. Results: Heat flux correction substantially improves the simulated variability in the initialization runs for boreal summer and fall (June-October). In boreal spring (March-May), however, neither the initialization runs of the STD or FLX-experiments are able to capture the observed variability. FLX-predictions show no consistent enhancement of skill relative to the predictions of the STD experiment over the course of the year. The skill of persistence forecasts is hardly beat by either of the two experiments in any season, limiting the usefulness of the few forecasts that show significant skill. However, FLX-forecasts initialized in May recover skill in July and August, the peak season of the Atlantic Niño (anomaly correlation coefficients of about 0.3). Further study is necessary to determine the mechanism that drives this potentially useful recovery.

Soil Moisture and Snow Cover: Active or Passive Elements of Climate?

NASA Technical Reports Server (NTRS)

Oglesby, Robert J.; Marshall, Susan; Robertson, Franklin R.; Roads, John O.; Arnold, James E. (Technical Monitor)

2001-01-01

A key question in the study of the hydrologic cycle is the extent to which surface effects such as soil moisture and snow cover are simply passive elements or whether they can affect the evolution of climate on seasonal and longer time scales. We have constructed ensembles of predictability studies using the NCAR CCM3 in which we compared the relative roles of initial surface and atmospheric conditions over the central and western U.S. GAPP region in determining the subsequent evolution of soil moisture and of snow cover. We have also made sensitivity studies with exaggerated soil moisture and snow cover anomalies in order to determine the physical processes that may be important. Results from simulations with realistic soil moisture anomalies indicate that internal climate variability may be the strongest factor, with some indication that the initial atmospheric state is also important. The initial state of soil moisture does not appear important, a result that held whether simulations were started in late winter or late spring. Model runs with exaggerated soil moisture reductions (near-desert conditions) showed a much larger effect, with warmer surface temperatures, reduced precipitation, and lower surface pressures; the latter indicating a response of the atmospheric circulation. These results suggest the possibility of a threshold effect in soil moisture, whereby an anomaly must be of a sufficient size before it can have a significant impact on the atmospheric circulation and hence climate. Results from simulations with realistic snow cover anomalies indicate that the time of year can be crucial. When introduced in late winter, these anomalies strongly affected the subsequent evolution of snow cover. When introduced in early winter, however, little or no effect is seen on the subsequent snow cover. Runs with greatly exaggerated initial snow cover indicate that the high reflectivity of snow is the most important process by which snow cover can impact climate, through lower surface temperatures and increased surface pressures. In early winter, the amount of solar radiation is very small and so this albedo, effect is inconsequential while in late winter, with the sun higher in the sky and period of daylight longer, the effect is much stronger. The results to date were obtained for model runs with present-day conditions. We are currently analyzing runs made with projected forcings for the 21st century to see if these results are modified in any way under likely scenarios of future climate change.

Soil and Land Resources Information System (SLISYS-Tarim) for Sustainable Management of River Oases along the Tarim River, China

NASA Astrophysics Data System (ADS)

Othmanli, Hussein; Zhao, Chengyi; Stahr, Karl

2017-04-01

The Tarim River Basin is the largest continental basin in China. The region has extremely continental desert climate characterized by little rainfall <50 mm/a and high potential evaporation >3000 mm/a. The climate change is affecting severely the basin causing soil salinization, water shortage, and regression in crop production. Therefore, a Soil and Land Resources Information System (SLISYS-Tarim) for the regional simulation of crop yield production in the basin was developed. The SLISYS-Tarim consists of a database and an agro-ecological simulation model EPIC (Environmental Policy Integrated Climate). The database comprises relational tables including information about soils, terrain conditions, land use, and climate. The soil data implicate information of 50 soil profiles which were dug, analyzed, described and classified in order to characterize the soils in the region. DEM data were integrated with geological maps to build a digital terrain structure. Remote sensing data of Landsat images were applied for soil mapping, and for land use and land cover classification. An additional database for climate data, land management and crop information were linked to the system, too. Construction of the SLISYS-Tarim database was accomplished by integrating and overlaying the recommended thematic maps within environment of the geographic information system (GIS) to meet the data standard of the global and national SOTER digital database. This database forms appropriate input- and output data for the crop modelling with the EPIC model at various scales in the Tarim Basin. The EPIC model was run for simulating cotton production under a constructed scenario characterizing the current management practices, soil properties and climate conditions. For the EPIC model calibration, some parameters were adjusted so that the modeled cotton yield fits to the measured yield on the filed scale. The validation of the modeling results was achieved in a later step based on remote sensing data. The simulated cotton yield varied according to field management, soil type and salinity level, where soil salinity was the main limiting factor. Furthermore, the calibrated and validated EPIC model was run under several scenarios of climate conditions and land management practices to estimate the effect of climate change on cotton production and sustainability of agriculture systems in the basin. The application of SLISYS-Tarim showed that this database can be a suitable framework for storage and retrieval of soil and terrain data at various scales. The simulation with the EPIC model can assess the impact of climate change and management strategies. Therefore, SLISYS-Tarim can be a good tool for regional planning and serve the decision support system on regional and national scale.

Transport of ice into the stratosphere and the humidification of the stratosphere over the 21st century.

PubMed

Dessler, A E; Ye, H; Wang, T; Schoeberl, M R; Oman, L D; Douglass, A R; Butler, A H; Rosenlof, K H; Davis, S M; Portmann, R W

2016-03-16

Climate models predict that tropical lower-stratospheric humidity will increase as the climate warms. We examine this trend in two state-of-the-art chemistry-climate models. Under high greenhouse gas emissions scenarios, the stratospheric entry value of water vapor increases by ~1 part per million by volume (ppmv) over this century in both models. We show with trajectory runs driven by model meteorological fields that the warming tropical tropopause layer (TTL) explains 50-80% of this increase. The remainder is a consequence of trends in evaporation of ice convectively lofted into the TTL and lower stratosphere. Our results further show that, within the models we examined, ice lofting is primarily important on long time scales - on interannual time scales, TTL temperature variations explain most of the variations in lower stratospheric humidity. Assessing the ability of models to realistically represent ice-lofting processes should be a high priority in the modeling community.

Transport of ice into the stratosphere and the humidification of the stratosphere over the 21st century

PubMed Central

Dessler, A.E.; Ye, H.; Wang, T.; Schoeberl, M.R.; Oman, L.D.; Douglass, A.R.; Butler, A.H.; Rosenlof, K.H.; Davis, S.M.; Portmann, R.W.

2018-01-01

Climate models predict that tropical lower-stratospheric humidity will increase as the climate warms. We examine this trend in two state-of-the-art chemistry-climate models. Under high greenhouse gas emissions scenarios, the stratospheric entry value of water vapor increases by ~1 part per million by volume (ppmv) over this century in both models. We show with trajectory runs driven by model meteorological fields that the warming tropical tropopause layer (TTL) explains 50–80% of this increase. The remainder is a consequence of trends in evaporation of ice convectively lofted into the TTL and lower stratosphere. Our results further show that, within the models we examined, ice lofting is primarily important on long time scales — on interannual time scales, TTL temperature variations explain most of the variations in lower stratospheric humidity. Assessing the ability of models to realistically represent ice-lofting processes should be a high priority in the modeling community. PMID:29551841

Transport of Ice into the Stratosphere and the Humidification of the Stratosphere over the 21st Century

NASA Technical Reports Server (NTRS)

Dessler, A. E.; Ye, H.; Wang, T.; Schoeberl, M. R.; Oman, L. D.; Douglass, A. R.; Butler, A. H.; Rosenlof, K. H.; Davis, S. M.; Portmann, R. W.

2016-01-01

Climate models predict that tropical lower-stratospheric humidity will increase as the climate warms. We examine this trend in two state-of-the-art chemistry-climate models. Under high greenhouse gas emissions scenarios, the stratospheric entry value of water vapor increases by approx. 1 part per million by volume (ppmv) over this century in both models. We show with trajectory runs driven by model meteorological fields that the warming tropical tropopause layer (TTL) explains 50-80% of this increase. The remainder is a consequence of trends in evaporation of ice convectively lofted into the TTL and lower stratosphere. Our results further show that, within the models we examined, ice lofting is primarily important on long time scales - on interannual time scales, TTL temperature variations explain most of the variations in lower stratospheric humidity. Assessing the ability of models to realistically represent ice-lofting processes should be a high priority in the modeling community.

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.

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.

Applying an orographic precipitation model to improve mass balance modeling of the Juneau Icefield, AK

NASA Astrophysics Data System (ADS)

Roth, A. C.; Hock, R.; Schuler, T.; Bieniek, P.; Aschwanden, A.

2017-12-01

Mass loss from glaciers in Southeast Alaska is expected to alter downstream ecological systems as runoff patterns change. To investigate these potential changes under future climate scenarios, distributed glacier mass balance modeling is required. However, the spatial resolution gap between global or regional climate models and the requirements for glacier mass balance modeling studies must be addressed first. We have used a linear theory of orographic precipitation model to downscale precipitation from both the Weather Research and Forecasting (WRF) model and ERA-Interim to the Juneau Icefield region over the period 1979-2013. This implementation of the LT model is a unique parameterization that relies on the specification of snow fall speed and rain fall speed as tuning parameters to calculate the cloud time delay, τ. We assessed the LT model results by considering winter precipitation so the effect of melt was minimized. The downscaled precipitation pattern produced by the LT model captures the orographic precipitation pattern absent from the coarse resolution WRF and ERA-Interim precipitation fields. Observational data constraints limited our ability to determine a unique parameter combination and calibrate the LT model to glaciological observations. We established a reference run of parameter values based on literature and performed a sensitivity analysis of the LT model parameters, horizontal resolution, and climate input data on the average winter precipitation. The results of the reference run showed reasonable agreement with the available glaciological measurements. The precipitation pattern produced by the LT model was consistent regardless of parameter combination, horizontal resolution, and climate input data, but the precipitation amount varied strongly with these factors. Due to the consistency of the winter precipitation pattern and the uncertainty in precipitation amount, we suggest a precipitation index map approach to be used in combination with a distributed mass balance model for future mass balance modeling studies of the Juneau Icefield. The LT model has potential to be used in other regions in Alaska and elsewhere with strong orographic effects for improved glacier mass balance modeling and/or hydrological modeling.

Effects of different representations of transport in the new EMAC-SWIFT chemistry climate model

NASA Astrophysics Data System (ADS)

Scheffler, Janice; Langematz, Ulrike; Wohltmann, Ingo; Kreyling, Daniel; Rex, Markus

2017-04-01

It is well known that the representation of atmospheric ozone chemistry in weather and climate models is essential for a realistic simulation of the atmospheric state. Interactively coupled chemistry climate models (CCMs) provide a means to realistically simulate the interaction between atmospheric chemistry and dynamics. The calculation of chemistry in CCMs, however, is computationally expensive which renders the use of complex chemistry models not suitable for ensemble simulations or simulations with multiple climate change scenarios. In these simulations ozone is therefore usually prescribed as a climatological field or included by incorporating a fast linear ozone scheme into the model. While prescribed climatological ozone fields are often not aligned with the modelled dynamics, a linear ozone scheme may not be applicable for a wide range of climatological conditions. An alternative approach to represent atmospheric chemistry in climate models which can cope with non-linearities in ozone chemistry and is applicable to a wide range of climatic states is the Semi-empirical Weighted Iterative Fit Technique (SWIFT) that is driven by reanalysis data and has been validated against observational satellite data and runs of a full Chemistry and Transport Model. SWIFT has been implemented into the ECHAM/MESSy (EMAC) chemistry climate model that uses a modular approach to climate modelling where individual model components can be switched on and off. When using SWIFT in EMAC, there are several possibilities to represent the effect of transport inside the polar vortex: the semi-Lagrangian transport scheme of EMAC and a transport parameterisation that can be useful when using SWIFT in models not having transport of their own. Here, we present results of equivalent simulations with different handling of transport, compare with EMAC simulations with full interactive chemistry and evaluate the results with observations.

Development of incremental dynamical downscaling and analysis system for regional scale climate change projections

NASA Astrophysics Data System (ADS)

Wakazuki, Yasutaka; Hara, Masayuki; Fujita, Mikiko; Ma, Xieyao; Kimura, Fujio

2013-04-01

Regional scale climate change projections play an important role in assessments of influences of global warming and include statistical (SD) and dynamical downscaling (DD) approaches. One of DD methods is developed basing on the pseudo-global-warming (PGW) method developed by Kimura and Kitoh (2007) in this study. In general, DD uses regional climate model (RCM) with lateral boundary data. In PGW method, the climatological mean difference estimated by GCMs are added to the objective analysis data (ANAL), and the data are used as the lateral boundary data in the future climate simulations. The ANAL is also used as the lateral boundary conditions of the present climate simulation. One of merits of the PGW method is that influences of biases of GCMs in RCM simulations are reduced. However, the PGW method does not treat climate changes in relative humidity, year-to-year variation, and short-term disturbances. The developing new downscaling method is named as the incremental dynamical downscaling and analysis system (InDDAS). The InDDAS treat climate changes in relative humidity and year-to-year variations. On the other hand, uncertainties of climate change projections estimated by many GCMs are large and are not negligible. Thus, stochastic regional scale climate change projections are expected for assessments of influences of global warming. Many RCM runs must be performed to make stochastic information. However, the computational costs are huge because grid size of RCM runs should be small to resolve heavy rainfall phenomena. Therefore, the number of runs to make stochastic information must be reduced. In InDDAS, climatological differences added to ANAL become statistically pre-analyzed information. The climatological differences of many GCMs are divided into mean climatological difference (MD) and departures from MD. The departures are analyzed by principal component analysis, and positive and negative perturbations (positive and negative standard deviations multiplied by departure patterns (eigenvectors)) with multi modes are added to MD. Consequently, the most likely future states are calculated with climatological difference of MD. For example, future states in cases that temperature increase is large and small are calculated with MD plus positive and negative perturbations of the first mode.

Tropical Cyclones, Hurricanes, and Climate: NASA's Global Cloud-Scale Simulations and New Observations that Characterize the Lifecycle of Hurricanes

NASA Technical Reports Server (NTRS)

Putman, William M.

2010-01-01

One of the primary interests of Global Change research is the impact of climate changes and climate variability on extreme weather events, such as intense tropical storms and hurricanes. Atmospheric climate models run at resolutions of global weather models have been used to study the impact of climate variability, as seen in sea surface temperatures, on the frequency and intensity of tropical cyclones. NASA's Goddard Earth Observing System Model, version 5 (GEOS-5) in ensembles run at 50 km resolution has been able to reproduce the interannual variations of tropical cyclone frequency seen in nature. This, and other global models, have found it much more difficult to reproduce the interannual changes in intensity, a result that reflects the inability of the models to simulate the intensities of the most extreme storms. Better representation of the structures of cyclones requires much higher resolution models. Such improved representation is also fundamental to making best use of satellite observations. In collaboration with NOAA's Geophysical Fluid Dynamics Laboratory, GEOS-5 now has the capability of running at much higher resolution to better represent cloud-scale resolutions. Global simulations at cloud-permitting resolutions (10- to 3.5-km) allows for the development of realistic tropical cyclones from tropical storm 119 km/hr winds) to category 5 (>249km1hr winds) intensities. GEOS-5 has produced realistic rain-band and eye-wall structures in tropical cyclones that can be directly analyzed against satellite observations. For the first time a global climate model is capable of representing realistic intensity and track variability on a seasonal scale across basins. GEOS-5 is also used in assimilation mode to test the impact of NASA's observations on tropical cyclone forecasts. One such test, for tropical cyclone Nargis in the Indian Ocean in May 2008, showed that observations from Atmospheric Infrared Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU-A) on Aqua substantially reduced forecast track errors. Tropical cyclones in the northern Indian Ocean pose serious challenges to operational weather forecasting systems, partly due to their shorter lifespan and more erratic track, compared to those in the Atlantic and the Pacific. SA is also bringing several state of the art instruments in recent field campaigns to peer under the clouds and study the inner workings of the tropical storms. With the Genesis and Rapid Intensification Processes (GRIP) experiment, a NASA Earth science field experiment in 2010 that includes the Global Hawk Unmanned Airborne System (UAS) configured with a suite of in situ and remote sensing instruments that are observing and characterizing the lifecycle of hurricanes, we expect significant improvement in our understanding of the track and intensification processes with the assimilation of the satellite and field campaign observations of meteorological parameters in the numerical prediction models.

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.

Overview of Proposal on High Resolution Climate Model Simulations of Recent Hurricane and Typhoon Activity: The Impact of SSTs and the Madden Julian Oscillation

NASA Technical Reports Server (NTRS)

Schubert, Siegfried; Kang, In-Sik; Reale, Oreste

2009-01-01

This talk gives an update on the progress and further plans for a coordinated project to carry out and analyze high-resolution simulations of tropical storm activity with a number of state-of-the-art global climate models. Issues addressed include, the mechanisms by which SSTs control tropical storm. activity on inter-annual and longer time scales, the modulation of that activity by the Madden Julian Oscillation on sub-seasonal time scales, as well as the sensitivity of the results to model formulation. The project also encourages companion coarser resolution runs to help assess resolution dependence, and. the ability of the models to capture the large-scale and long-terra changes in the parameters important for hurricane development. Addressing the above science questions is critical to understanding the nature of the variability of the Asian-Australian monsoon and its regional impacts, and thus CLIVAR RAMP fully endorses the proposed tropical storm simulation activity. The project is open to all interested organizations and investigators, and the results from the runs will be shared among the participants, as well as made available to the broader scientific community for analysis.

Creating Weather System Ensembles Through Synergistic Process Modeling and Machine Learning

NASA Astrophysics Data System (ADS)

Chen, B.; Posselt, D. J.; Nguyen, H.; Wu, L.; Su, H.; Braverman, A. J.

2017-12-01

Earth's weather and climate are sensitive to a variety of control factors (e.g., initial state, forcing functions, etc). Characterizing the response of the atmosphere to a change in initial conditions or model forcing is critical for weather forecasting (ensemble prediction) and climate change assessment. Input - response relationships can be quantified by generating an ensemble of multiple (100s to 1000s) realistic realizations of weather and climate states. Atmospheric numerical models generate simulated data through discretized numerical approximation of the partial differential equations (PDEs) governing the underlying physics. However, the computational expense of running high resolution atmospheric state models makes generation of more than a few simulations infeasible. Here, we discuss an experiment wherein we approximate the numerical PDE solver within the Weather Research and Forecasting (WRF) Model using neural networks trained on a subset of model run outputs. Once trained, these neural nets can produce large number of realization of weather states from a small number of deterministic simulations with speeds that are orders of magnitude faster than the underlying PDE solver. Our neural network architecture is inspired by the governing partial differential equations. These equations are location-invariant, and consist of first and second derivations. As such, we use a 3x3 lon-lat grid of atmospheric profiles as the predictor in the neural net to provide the network the information necessary to compute the first and second moments. Results indicate that the neural network algorithm can approximate the PDE outputs with high degree of accuracy (less than 1% error), and that this error increases as a function of the prediction time lag.

Estimation of ozone dry deposition over Europe for the period 2071-2100

NASA Astrophysics Data System (ADS)

Komjáthy, Eszter; Gelybó, Györgyi; László Lagzi, István.; Mészáros, Róbert

2010-05-01

Ozone in the lower troposphere is a phytotoxic air pollutant which can cause injury to plant tissues, causing reduction in plant growth and productivity. In the last decades, several investigations have been carried out for the purpose to estimate ozone load over different surface types. At the same time, the changes of atmospheric variables as well as surface/vegetation parameters due to the global climate change could also strongly modify both temporal and spatial variations of ozone load over Europe. In this study, the possible effects of climate change on ozone deposition are analyzed. Using a sophisticated deposition model, ozone deposition was estimated on a regular grid over Europe for the period 2071-2100. Our aim is to determine the uncertainties and the possible degree of change in ozone deposition velocity as an important predictor of total ozone load using climate data from multiple climate models and runs. For these model calculations, results of the PRUDENCE (Predicting of Regional Scenarios and Uncertainties for Defining European Climate Change Risks and Effects) climate prediction project were used. As a first step, seasonal variations of ozone deposition over different vegetation types in case of different climate scenarios are presented in this study. Besides model calculations, in the frame of a sensitivity analyses, the effects of surface/vegetation parameters (e.g. leaf area index or stomatal resistance) on ozone deposition under a modified climate regime have also been analyzed.

Model Diagnostics for the Department of Energy's Accelerated Climate Modeling for Energy (ACME) Project

NASA Astrophysics Data System (ADS)

Smith, B.

2015-12-01

In 2014, eight Department of Energy (DOE) national laboratories, four academic institutions, one company, and the National Centre for Atmospheric Research combined forces in a project called Accelerated Climate Modeling for Energy (ACME) with the goal to speed Earth system model development for climate and energy. Over the planned 10-year span, the project will conduct simulations and modeling on DOE's most powerful high-performance computing systems at Oak Ridge, Argonne, and Lawrence Berkeley Leadership Compute Facilities. A key component of the ACME project is the development of an interactive test bed for the advanced Earth system model. Its execution infrastructure will accelerate model development and testing cycles. The ACME Workflow Group is leading the efforts to automate labor-intensive tasks, provide intelligent support for complex tasks and reduce duplication of effort through collaboration support. As part of this new workflow environment, we have created a diagnostic, metric, and intercomparison Python framework, called UVCMetrics, to aid in the testing-to-production execution of the ACME model. The framework exploits similarities among different diagnostics to compactly support diagnosis of new models. It presently focuses on atmosphere and land but is designed to support ocean and sea ice model components as well. This framework is built on top of the existing open-source software framework known as the Ultrascale Visualization Climate Data Analysis Tools (UV-CDAT). Because of its flexible framework design, scientists and modelers now can generate thousands of possible diagnostic outputs. These diagnostics can compare model runs, compare model vs. observation, or simply verify a model is physically realistic. Additional diagnostics are easily integrated into the framework, and our users have already added several. Diagnostics can be generated, viewed, and manipulated from the UV-CDAT graphical user interface, Python command line scripts and programs, and web browsers. The framework is designed to be scalable to large datasets, yet easy to use and familiar to scientists using previous tools. Integration in the ACME overall user interface facilitates data publication, further analysis, and quick feedback to model developers and scientists making component or coupled model runs.

High Resolution Simulation of a Colorado Rockies Extreme Snow and Rain Event in both a Current and Future Climate

NASA Astrophysics Data System (ADS)

Rasmussen, Roy; Ikeda, Kyoko; Liu, Changhai; Gutmann, Ethan; Gochis, David

2016-04-01

Modeling of extreme weather events often require very finely resolved treatment of atmospheric circulation structures in order to produce and localize the large moisture fluxes that result in extreme precipitation. This is particularly true for cool season orographic precipitation processes where the representation of the landform can significantly impact vertical velocity profiles and cloud moisture entrainment rates. This study presents results for high resolution regional climate modeling study of the Colorado Headwaters region using an updated version of the Weather Research and Forecasting (WRF) model run at 4 km horizontal resolution and a hydrological extension package called WRF-Hydro. Previous work has shown that the WRF modeling system can produce credible depictions of winter orographic precipitation over the Colorado Rockies if run at horizontal resolutions < 6 km. Here we present results from a detailed study of an extreme springtime snowfall event that occurred along the Colorado Front Range in March 2003. Results from the impact of warming on total precipitation, snow-rain partitioning and surface hydrological fluxes (evapotranspiration and runoff) will be discussed in the context of how potential changes in temperature impact the amount of precipitation, the phase of precipitation (rain vs. snow) and the timing and amplitude of streamflow responses. The results show using the Pseudo Global Warming technique that intense precipitation rates significantly increased during the event and a significant fraction of the snowfall converts to rain which significantly amplifies the runoff response from one where runoff is produced gradually to one in which runoff is rapidly translated into streamflow values that approach significant flooding risks. Results from a new, CONUS scale high resolution climate simulation of extreme events in a current and future climate will be presented as time permits.

A New Formulation for Fresh Snow Density over Antarctica for the regional climate model Modèle Atmosphérique Régionale (MAR).

NASA Astrophysics Data System (ADS)

Tedesco, M.; Datta, R.; Fettweis, X.; Agosta, C.

2015-12-01

Surface-layer snow density is important to processes contributing to surface mass balance, but is highly variable over Antarctica due to a wide range of near-surface climate conditions over the continent. Formulations for fresh snow density have typically either used fixed values or been modeled empirically using field data that is limited to specific seasons or regions. There is also currently limited work exploring how the sensitivity to fresh snow density in regional climate models varies with resolution. Here, we present a new formulation compiled from (a) over 1600 distinct density profiles from multiple sources across Antarctica and (b) near-surface variables from the regional climate model Modèle Atmosphérique Régionale (MAR). Observed values represent coastal areas as well as the plateau, in both West and East Antarctica (although East Antarctica is dominant). However, no measurements are included from the Antarctic Peninsula, which is both highly topographically variable and extends to lower latitudes than the remainder of the continent. In order to assess the applicability of this fresh snow density formulation to the Antarctic Peninsula at high resolutions, a version of MAR is run for several years both at low-resolution at the continental scale and at a high resolution for the Antarctic Peninsula alone. This setup is run both with and without the new fresh density formulation to quantify the sensitivity of the energy balance and SMB components to fresh snow density. Outputs are compared with near-surface atmospheric variables available from AWS stations (provided by the University of Wisconsin Madison) as well as net accumulation values from the SAMBA database (provided from the Laboratoire de Glaciologie et Géophysique de l'Environnement).

The impact of run-off change on physical instream habitats and its response to river morphology

NASA Astrophysics Data System (ADS)

Hauer, Christoph; Habersack, Helmut

2010-05-01

Rivers have already been substantially altered by human activity. Channelization, flow regulation, or changes in land use, especially urbanization, significantly alter the water discharge, sediment transport, and morphology of rivers. The impacts of these anthropogenic measures (disturbances) on river morphology and instream habitats were frequently investigated by the scientific community over the last decades. However, there are forms of disturbances (often induced by climate change) which cause at the beginning only a slight but (over the years) a continuous degradation of aquatic habitats (and river morphology). In the presented study the impact of such disturbances caused by climate change on summer run-off was investigated within the Gr. Mühl River catchment, Austria. So far, various studies have documented the impact of run-off change on river morphology and/or sediment load. Further the impact of run-off change on aquatic ecology (target fish species) have been documented throughout various scientific papers. However, there is a lack of knowledge how (climate induced) run-off changes affect instream aquatic habitats concerning various morphological patterns (e.g. riffle-pool morphology vs. plane bed river). Thus, the aim of the presented study was to link the impacts of climate change (e.g. reduced summer run-off) to various morphological types (riffle-pool, plane bed) using habitat modelling (2-dimensional) as integrative evaluation method. As target fish species sub-adult/adult grayling was selected due to the fact, that Thymallus thymallus features especially high sensitivity in water depth (microhabitat use). Further grayling was one the historically dominant fish species for the hyporhithral catchment of the Gr. Mühl River. Within the catchment 80% of the total river length are determined as plane bed river and 20 % as riffle-pool reaches (situated in former fine material deposits). Six reaches (3 plane-bed, 3 riffle-pool) were selected and surveyed by total station (Leica805) to derive high quality DTM-models for modelling. Monitoring data of temperature (period: 1948 - 2006) and gauging data of three stations (Vorderanger, period: 1966 - 2008; Furthmühle, period: 1951 - 2008; Teufelmühle, period: 1951 - 2008) along the investigated reach were additionally provided by the local government agencies. The results of the statistical testing (for significant breakpoints in temperature trends) exhibited significant changes (p>95%) for the seasons spring (year 1989) and summer (year 1990) (increase in regional temperature). Simultaneously, the periods of run-off below statistically determined low-flow thresholds increased significantly especially for summer periods (e.g. gauging station Furthmühle: period 1951 - 1990: n = 684 days / 1990 - 2008: n = 760 days). The impact of those intensified low flow conditions on subadult / adult grayling were (and are) limiting available physical habitats especially within the plane-bed sections (n=3). Only riffle-pool reaches exhibited suitable habitats (evaluated by habitatmodelling) for these dry-periods. However, those riffle-pool reaches are rare and randomly distributed over the 45 km river length (investigated reach). In the presented study it could be figured out, that climate change affects instream fish habitats not only by an increase in water temperatures, but also by limiting physical habitats (in relation to various morphological types). Thus, the response of fish (e.g. grayling) could be on the one hand an upstream migration due to the warmer water temperatures (frequently documented) but on the other hand a downstream migration caused by unsuitable habitats. This second finding should be seen as one crucial point especially for the restoration of regulated rivers with respect to climate change and to fulfil the aims of the European Water Framework Directive.

The role of sea ice in 2 x CO2 climate model sensitivity. Part 1: The total influence of sea ice thickness and extent

NASA Technical Reports Server (NTRS)

Rind, D.; Healy, R.; Parkinson, C.; Martinson, D.

1995-01-01

As a first step in investigating the effects of sea ice changes on the climate sensitivity to doubled atmospheric CO2, the authors use a standard simple sea ice model while varying the sea ice distributions and thicknesses in the control run. Thinner ice amplifies the atmospheric temperature senstivity in these experiments by about 15% (to a warming of 4.8 C), because it is easier for the thinner ice to be removed as the climate warms. Thus, its impact on sensitivity is similar to that of greater sea ice extent in the control run, which provides more opportunity for sea ice reduction. An experiment with sea ice not allowed to change between the control and doubled CO2 simulations illustrates that the total effect of sea ice on surface air temperature changes, including cloud cover and water vapor feedbacks that arise in response to sea ice variations, amounts to 37% of the temperature sensitivity to the CO2 doubling, accounting for 1.56 C of the 4.17 C global warming. This is about four times larger than the sea ice impact when no feedbacks are allowed. The different experiments produce a range of results for southern high latitudes with the hydrologic budget over Antarctica implying sea level increases of varying magnitude or no change. These results highlight the importance of properly constraining the sea ice response to climate perturbations, necessitating the use of more realistic sea ice and ocean models.

Liquid water on Mars - an energy balance climate model for CO2/H2O atmospheres

NASA Astrophysics Data System (ADS)

Hoffert, M. I.; Callegari, A. J.; Hsieh, T.; Ziegler, W.

1981-07-01

A simple climatic model is developed for a Mars atmosphere containing CO2 and sufficient liquid water to account for the observed hydrologic surface features by the existence of a CO2/H2O greenhouse effect. A latitude-resolved climate model originally devised for terrestrial climate studies is applied to Martian conditions, with the difference between absorbed solar flux and emitted long-wave flux to space per unit area attributed to the divergence of the meridional heat flux and the poleward heat flux assumed to equal the atmospheric eddy heat flux. The global mean energy balance is calculated as a function of atmospheric pressure to assess the CO2/H2O greenhouse liquid water hypothesis, and some latitude-resolved cases are examined in detail in order to clarify the role of atmospheric transport and temperature-albedo feedback. It is shown that the combined CO2/H2O greenhouse at plausible early surface pressures may account for climates hot enough to support a hydrological cycle and running water at present-day insolation and visible albedo levels.

Liquid water on Mars - An energy balance climate model for CO2/H2O atmospheres

NASA Technical Reports Server (NTRS)

Hoffert, M. I.; Callegari, A. J.; Hsieh, C. T.; Ziegler, W.

1981-01-01

A simple climatic model is developed for a Mars atmosphere containing CO2 and sufficient liquid water to account for the observed hydrologic surface features by the existence of a CO2/H2O greenhouse effect. A latitude-resolved climate model originally devised for terrestrial climate studies is applied to Martian conditions, with the difference between absorbed solar flux and emitted long-wave flux to space per unit area attributed to the divergence of the meridional heat flux and the poleward heat flux assumed to equal the atmospheric eddy heat flux. The global mean energy balance is calculated as a function of atmospheric pressure to assess the CO2/H2O greenhouse liquid water hypothesis, and some latitude-resolved cases are examined in detail in order to clarify the role of atmospheric transport and temperature-albedo feedback. It is shown that the combined CO2/H2O greenhouse at plausible early surface pressures may account for climates hot enough to support a hydrological cycle and running water at present-day insolation and visible albedo levels.

The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) data set

NASA Astrophysics Data System (ADS)

Day, J. J.; Tietsche, S.; Collins, M.; Goessling, H. F.; Guemas, V.; Guillory, A.; Hurlin, W. J.; Ishii, M.; Keeley, S. P. E.; Matei, D.; Msadek, R.; Sigmond, M.; Tatebe, H.; Hawkins, E.

2015-10-01

Recent decades have seen significant developments in seasonal-to-interannual timescale climate prediction capabilities. However, until recently the potential of such systems to predict Arctic climate had not been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Inter-annual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to inter-annual timescales. Here we present a description of the archived data set (which is available at the British Atmospheric Data Centre) and an update of the project's results. Although designed to address Arctic predictability, this data set could also be used to assess the predictability of other regions and modes of climate variability on these timescales, such as the El Niño Southern Oscillation.

Climate Model Diagnostic Analyzer Web Service System

NASA Astrophysics Data System (ADS)

Lee, S.; Pan, L.; Zhai, C.; Tang, B.; Jiang, J. H.

2013-12-01

The latest Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report stressed the need for the comprehensive and innovative evaluation of climate models with newly available global observations. The traditional approach to climate model evaluation, which compares a single parameter at a time, identifies symptomatic model biases and errors but fails to diagnose the model problems. The model diagnosis process requires physics-based multi-variable comparisons that typically involve large-volume and heterogeneous datasets, making them both computationally- and data-intensive. To address these challenges, we are developing a parallel, distributed web-service system that enables the physics-based multi-variable model performance evaluations and diagnoses through the comprehensive and synergistic use of multiple observational data, reanalysis data, and model outputs. We have developed a methodology to transform an existing science application code into a web service using a Python wrapper interface and Python web service frameworks (i.e., Flask, Gunicorn, and Tornado). The web-service system, called Climate Model Diagnostic Analyzer (CMDA), currently supports (1) all the datasets from Obs4MIPs and a few ocean datasets from NOAA and Argo, which can serve as observation-based reference data for model evaluation and (2) many of CMIP5 model outputs covering a broad range of atmosphere, ocean, and land variables from the CMIP5 specific historical runs and AMIP runs. Analysis capabilities currently supported by CMDA are (1) the calculation of annual and seasonal means of physical variables, (2) the calculation of time evolution of the means in any specified geographical region, (3) the calculation of correlation between two variables, and (4) the calculation of difference between two variables. A web user interface is chosen for CMDA because it not only lowers the learning curve and removes the adoption barrier of the tool but also enables instantaneous use, avoiding the hassle of local software installation and environment incompatibility. CMDA is planned to be used as an educational tool for the summer school organized by JPL's Center for Climate Science in 2014. The requirements of the educational tool are defined with the interaction with the school organizers, and CMDA is customized to meet the requirements accordingly. The tool needs to be production quality for 30+ simultaneous users. The summer school will thus serve as a valuable testbed for the tool development, preparing CMDA to serve the Earth-science modeling and model-analysis community at the end of the project. This work was funded by the NASA Earth Science Program called Computational Modeling Algorithms and Cyberinfrastructure (CMAC).

Improved pattern scaling approaches for the use in climate impact studies

NASA Astrophysics Data System (ADS)

Herger, Nadja; Sanderson, Benjamin M.; Knutti, Reto

2015-05-01

Pattern scaling is a simple way to produce climate projections beyond the scenarios run with expensive global climate models (GCMs). The simplest technique has known limitations and assumes that a spatial climate anomaly pattern obtained from a GCM can be scaled by the global mean temperature (GMT) anomaly. We propose alternatives and assess their skills and limitations. One approach which avoids scaling is to consider a period in a different scenario with the same GMT change. It is attractive as it provides patterns of any temporal resolution that are consistent across variables, and it does not distort variability. Second, we extend the traditional approach with a land-sea contrast term, which provides the largest improvements over the traditional technique. When interpolating between known bounding scenarios, the proposed methods significantly improve the accuracy of the pattern scaled scenario with little computational cost. The remaining errors are much smaller than the Coupled Model Intercomparison Project Phase 5 model spread.

Assessing Confidence in Pliocene Sea Surface Temperatures to Evaluate Predictive Models

NASA Technical Reports Server (NTRS)

Dowsett, Harry J.; Robinson, Marci M.; Haywood, Alan M.; Hill, Daniel J.; Dolan, Aisling. M.; Chan, Wing-Le; Abe-Ouchi, Ayako; Chandler, Mark A.; Rosenbloom, Nan A.; Otto-Bliesner, Bette L.;

Assessing confidence in Pliocene sea surface temperatures to evaluate predictive models

USGS Publications Warehouse

Dowsett, Harry J.; Robinson, Marci M.; Haywood, Alan M.; Hill, Daniel J.; Dolan, Aisling M.; Stoll, Danielle K.; Chan, Wing-Le; Abe-Ouchi, Ayako; Chandler, Mark A.; Rosenbloom, Nan A.; Otto-Bliesner, Bette L.; Bragg, Fran J.; Lunt, Daniel J.; Foley, Kevin M.; Riesselman, Christina R.

2012-01-01

In light of mounting empirical evidence that planetary warming is well underway, the climate research community looks to palaeoclimate research for a ground-truthing measure with which to test the accuracy of future climate simulations. Model experiments that attempt to simulate climates of the past serve to identify both similarities and differences between two climate states and, when compared with simulations run by other models and with geological data, to identify model-specific biases. Uncertainties associated with both the data and the models must be considered in such an exercise. The most recent period of sustained global warmth similar to what is projected for the near future occurred about 3.3–3.0 million years ago, during the Pliocene epoch. Here, we present Pliocene sea surface temperature data, newly characterized in terms of level of confidence, along with initial experimental results from four climate models. We conclude that, in terms of sea surface temperature, models are in good agreement with estimates of Pliocene sea surface temperature in most regions except the North Atlantic. Our analysis indicates that the discrepancy between the Pliocene proxy data and model simulations in the mid-latitudes of the North Atlantic, where models underestimate warming shown by our highest-confidence data, may provide a new perspective and insight into the predictive abilities of these models in simulating a past warm interval in Earth history. This is important because the Pliocene has a number of parallels to present predictions of late twenty-first century climate.

Change in the magnitude and mechanisms of global temperature variability with warming.

PubMed

Brown, Patrick T; Ming, Yi; Li, Wenhong; Hill, Spencer A

2017-01-01

Natural unforced variability in global mean surface air temperature (GMST) can mask or exaggerate human-caused global warming, and thus a complete understanding of this variability is highly desirable. Significant progress has been made in elucidating the magnitude and physical origins of present-day unforced GMST variability, but it has remained unclear how such variability may change as the climate warms. Here we present modeling evidence that indicates that the magnitude of low-frequency GMST variability is likely to decline in a warmer climate and that its generating mechanisms may be fundamentally altered. In particular, a warmer climate results in lower albedo at high latitudes, which yields a weaker albedo feedback on unforced GMST variability. These results imply that unforced GMST variability is dependent on the background climatological conditions, and thus climate model control simulations run under perpetual preindustrial conditions may have only limited relevance for understanding the unforced GMST variability of the future.

Change in the Magnitude and Mechanisms of Global Temperature Variability with Warming

NASA Astrophysics Data System (ADS)

Brown, P. T.; Ming, Y.; Li, W.; Hill, S. A.

2017-12-01

Natural unforced variability in global mean surface air temperature (GMST) can mask or exaggerate human-caused global warming, and thus a complete understanding of this variability is highly desirable. Significant progress has been made in elucidating the magnitude and physical origins of present-day unforced GMST variability, but it has remained unclear how such variability may change as the climate warms. Here we present modeling evidence that indicates that the magnitude of low-frequency GMST variability is likely to decline in a warmer climate and that its generating mechanisms may be fundamentally altered. In particular, a warmer climate results in lower albedo at high latitudes, which yields a weaker albedo feedback on unforced GMST variability. These results imply that unforced GMST variability is dependent on the background climatological conditions, and thus climate model control simulations run under perpetual preindustrial conditions may have only limited relevance for understanding the unforced GMST variability of the future.

Refining climate change projections for organisms with low dispersal abilities: a case study of the Caspian whip snake.

PubMed

Sahlean, Tiberiu C; Gherghel, Iulian; Papeş, Monica; Strugariu, Alexandru; Zamfirescu, Ştefan R

2014-01-01

Climate warming is one of the most important threats to biodiversity. Ectothermic organisms such as amphibians and reptiles are especially vulnerable as climatic conditions affect them directly. Ecological niche models (ENMs) are increasingly popular in ecological studies, but several drawbacks exist, including the limited ability to account for the dispersal potential of the species. In this study, we use ENMs to explore the impact of global climate change on the Caspian whip snake (Dolichophis caspius) as model for organisms with low dispersal abilities and to quantify dispersal to novel areas using GIS techniques. Models generated using Maxent 3.3.3 k and GARP for current distribution were projected on future climatic scenarios. A cost-distance analysis was run in ArcGIS 10 using geomorphological features, ecological conditions, and human footprint as "costs" to dispersal of the species to obtain a Maximum Dispersal Range (MDR) estimate. All models developed were statistically significant (P<0.05) and recovered the currently known distribution of D. caspius. Models projected on future climatic conditions using Maxent predicted a doubling of suitable climatic area, while GARP predicted a more conservative expansion. Both models agreed on an expansion of suitable area northwards, with minor decreases at the southern distribution limit. The MDR area calculated using the Maxent model represented a third of the total area of the projected model. The MDR based on GARP models recovered only about 20% of the total area of the projected model. Thus, incorporating measures of species' dispersal abilities greatly reduced estimated area of potential future distributions.

Synchronized Trajectories in a Climate "Supermodel"

NASA Astrophysics Data System (ADS)

Duane, Gregory; Schevenhoven, Francine; Selten, Frank

2017-04-01

Differences in climate projections among state-of-the-art models can be resolved by connecting the models in run-time, either through inter-model nudging or by directly combining the tendencies for corresponding variables. Since it is clearly established that averaging model outputs typically results in improvement as compared to any individual model output, averaged re-initializations at typical analysis time intervals also seems appropriate. The resulting "supermodel" is more like a single model than it is like an ensemble, because the constituent models tend to synchronize even with limited inter-model coupling. Thus one can examine the properties of specific trajectories, rather than averaging the statistical properties of the separate models. We apply this strategy to a study of the index cycle in a supermodel constructed from several imperfect copies of the SPEEDO model (a global primitive-equation atmosphere-ocean-land climate model). As with blocking frequency, typical weather statistics of interest like probabilities of heat waves or extreme precipitation events, are improved as compared to the standard multi-model ensemble approach. In contrast to the standard approach, the supermodel approach provides detailed descriptions of typical actual events.

Potential Effects of Drought on Tree Dieback in Great Britain and Implications for Forest Management in Adaptation to Climate Change

NASA Astrophysics Data System (ADS)

Yu, Jianjun; Berry, Pam

2017-04-01

The drought and heat stress has alerted the composition, structure and biogeography of forests globally, whilst the projected severe and widespread droughts are potentially increasing. This challenges the sustainable forest management to better cope with future climate and maintain the forest ecosystem functions and services. Many studies have investigated the climate change impacts on forest ecosystem but less considered the climate extremes like drought. In this study, we implement a dynamic ecosystem model based on a version of LPJ-GUESS parameterized with European tree species and apply to Great Britain at a finer spatial resolution of 5*5 km. The model runs for the baseline from 1961 to 2011 and projects to the latter 21st century using 100 climate scenarios generated from MaRIUS project to tackle the climate model uncertainty. We will show the potential impacts of climate change on forest ecosystem and vegetation transition in Great Britain by comparing the modelled conditions in the 2030s and the 2080s relative to the baseline. In particular, by analyzing the modelled tree mortality, we will show the tree dieback patterns in response to drought for various species, and assess their drought vulnerability across Great Britain. We also use species distribution modelling to project the suitable climate space for selected tree species using the same climate scenarios. Aided by these two modelling approaches and based on the corresponding modelling results, we will discuss the implications for adaptation strategy for forest management, especially in extreme drought conditions. The gained knowledge and lessons for Great Britain are considered to be transferable in many other regions.

Water resources sensitivity to the isolated effects of land use, water demand and climate change under 2 degree global warming

NASA Astrophysics Data System (ADS)

Bisselink, Berny; Bernhard, Jeroen; de Roo, Ad

2017-04-01

One of the key impacts of global change are the future water resources. These water resources are influenced by changes in land use (LU), water demand (WD) and climate change. Recent developments in scenario modelling opened new opportunities for an integrated assessment. However, for identifying water resource management strategies it is helpful to focus on the isolated effects of possible changes in LU, WD and climate that may occur in the near future. In this work, we quantify the isolated contribution of LU, WD and climate to the integrated total water resources assuming a linear model behavior. An ensemble of five EURO-CORDEX RCP8.5 climate projections for the 31-year periods centered on the year of exceeding the global-mean temperature of 2 degree is used to drive the fully distributed hydrological model LISFLOOD for multiple river catchments in Europe. The JRC's Land Use Modelling Platform LUISA was used to obtain a detailed pan-European reference land use scenario until 2050. Water demand is estimated based on socio-economic (GDP, population estimates etc.), land use and climate projections as well. For each climate projection, four model runs have been performed including an integrated (LU, WD and climate) simulation and other three simulations to isolate the effect of LU, WD and climate. Changes relative to the baseline in terms of water resources indicators of the ensemble means of the 2 degree warming period and their associated uncertainties will reveal the integrated and isolated effect of LU, WD and climate change on water resources.

Microcomputer pollution model for civilian airports and Air Force bases. Model description

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

Segal, H.M.; Hamilton, P.L.

1988-08-01

This is one of three reports describing the Emissions and Dispersion Modeling System (EDMS). EDMS is a complex source emissions/dispersion model for use at civilian airports and Air Force bases. It operates in both a refined and a screening mode and is programmed for an IBM-XT (or compatible) computer. This report--MODEL DESCRIPTION--provides the technical description of the model. It first identifies the key design features of both the emissions (EMISSMOD) and dispersion (GIMM) portions of EDMS. It then describes the type of meteorological information the dispersion model can accept and identifies the manner in which it preprocesses National Climatic Centermore » (NCC) data prior to a refined-model run. The report presents the results of running EDMS on a number of different microcomputers and compares EDMS results with those of comparable models. The appendices elaborate on the information noted above and list the source code.« less

Evaluation of land surface model representation of phenology: an analysis of model runs submitted to the NACP Interim Site Synthesis

NASA Astrophysics Data System (ADS)

Richardson, A. D.; Nacp Interim Site Synthesis Participants

2010-12-01

Phenology represents a critical intersection point between organisms and their growth environment. It is for this reason that phenology is a sensitive and robust integrator of the biological impacts of year-to-year climate variability and longer-term climate change on natural systems. However, it is perhaps equally important that phenology, by controlling the seasonal activity of vegetation on the land surface, plays a fundamental role in regulating ecosystem processes, competitive interactions, and feedbacks to the climate system. Unfortunately, the phenological sub-models implemented in most state-of-the-art ecosystem models and land surface schemes are overly simplified. We quantified model errors in the representation of the seasonal cycles of leaf area index (LAI), gross ecosystem photosynthesis (GEP), and net ecosystem exchange of CO2. Our analysis was based on site-level model runs (14 different models) submitted to the North American Carbon Program (NACP) Interim Synthesis, and long-term measurements from 10 forested (5 evergreen conifer, 5 deciduous broadleaf) sites within the AmeriFlux and Fluxnet-Canada networks. Model predictions of the seasonality of LAI and GEP were unacceptable, particularly in spring, and especially for deciduous forests. This is despite an historical emphasis on deciduous forest phenology, and the perception that controls on spring phenology are better understood than autumn phenology. Errors of up to 25 days in predicting “spring onset” transition dates were common, and errors of up to 50 days were observed. For deciduous sites, virtually every model was biased towards spring onset being too early, and autumn senescence being too late. Thus, models predicted growing seasons that were far too long for deciduous forests. For most models, errors in the seasonal representation of deciduous forest LAI were highly correlated with errors in the seasonality of both GPP and NEE, indicating the importance of getting the underlying canopy dynamics correct. Most of the models in this comparison were unable to successfully predict the observed interannual variability in either spring or autumn transition dates. And, perhaps surprisingly, the seasonal cycles of models using phenology prescribed by remote sensing observations was, in general, no better than that that predicted by models with prognostic phenology. Reasons for the poor performance of both approaches will be discussed. These results highlight the need for improved understanding of the environmental controls on vegetation phenology. Existing models are unlikely to accurately predict future responses of phenology to climate change, and therefore will misrepresent the seasonality of key biosphere-atmosphere feedbacks and interactions in coupled model runs. New data sets, as for example from webcam-based monitoring networks (e.g. PhenoCam) or citizen science efforts (USA National Phenology Network) should prove valuable in this regard.

Emerging Cyber Infrastructure for NASA's Large-Scale Climate Data Analytics

NASA Astrophysics Data System (ADS)

Duffy, D.; Spear, C.; Bowen, M. K.; Thompson, J. H.; Hu, F.; Yang, C. P.; Pierce, D.

2016-12-01

The resolution of NASA climate and weather simulations have grown dramatically over the past few years with the highest-fidelity models reaching down to 1.5 KM global resolutions. With each doubling of the resolution, the resulting data sets grow by a factor of eight in size. As the climate and weather models push the envelope even further, a new infrastructure to store data and provide large-scale data analytics is necessary. The NASA Center for Climate Simulation (NCCS) has deployed the Data Analytics Storage Service (DASS) that combines scalable storage with the ability to perform in-situ analytics. Within this system, large, commonly used data sets are stored in a POSIX file system (write once/read many); examples of data stored include Landsat, MERRA2, observing system simulation experiments, and high-resolution downscaled reanalysis. The total size of this repository is on the order of 15 petabytes of storage. In addition to the POSIX file system, the NCCS has deployed file system connectors to enable emerging analytics built on top of the Hadoop File System (HDFS) to run on the same storage servers within the DASS. Coupled with a custom spatiotemporal indexing approach, users can now run emerging analytical operations built on MapReduce and Spark on the same data files stored within the POSIX file system without having to make additional copies. This presentation will discuss the architecture of this system and present benchmark performance measurements from traditional TeraSort and Wordcount to large-scale climate analytical operations on NetCDF data.

The Dependencies of Ecosystem Pattern, Structure, and Dynamics on Climate, Climate Variability, and Climate Change

NASA Astrophysics Data System (ADS)

Flanagan, S.; Hurtt, G. C.; Fisk, J. P.; Rourke, O.

2012-12-01

A robust understanding of the sensitivity of the pattern, structure, and dynamics of ecosystems to climate, climate variability, and climate change is needed to predict ecosystem responses to current and projected climate change. We present results of a study designed to first quantify the sensitivity of ecosystems to climate through the use of climate and ecosystem data, and then use the results to test the sensitivity of the climate data in a state-of the art ecosystem model. A database of available ecosystem characteristics such as mean canopy height, above ground biomass, and basal area was constructed from sources like the National Biomass and Carbon Dataset (NBCD). The ecosystem characteristics were then paired by latitude and longitude with the corresponding climate characteristics temperature, precipitation, photosynthetically active radiation (PAR) and dew point that were retrieved from the North American Regional Reanalysis (NARR). The average yearly and seasonal means of the climate data, and their associated maximum and minimum values, over the 1979-2010 time frame provided by NARR were constructed and paired with the ecosystem data. The compiled results provide natural patterns of vegetation structure and distribution with regard to climate data. An advanced ecosystem model, the Ecosystem Demography model (ED), was then modified to allow yearly alterations to its mechanistic climate lookup table and used to predict the sensitivities of ecosystem pattern, structure, and dynamics to climate data. The combined ecosystem structure and climate data results were compared to ED's output to check the validity of the model. After verification, climate change scenarios such as those used in the last IPCC were run and future forest structure changes due to climate sensitivities were identified. The results of this study can be used to both quantify and test key relationships for next generation models. The sensitivity of ecosystem characteristics to climate data shown in the database construction and by the model reinforces the need for high-resolution datasets and stresses the importance of understanding and incorporating climate change scenarios into earth system models.

Empirical and modeled synoptic cloud climatology of the Arctic Ocean

NASA Technical Reports Server (NTRS)

Barry, R. G.; Newell, J. P.; Schweiger, A.; Crane, R. G.

1986-01-01

A set of cloud cover data were developed for the Arctic during the climatically important spring/early summer transition months. Parallel with the determination of mean monthly cloud conditions, data for different synoptic pressure patterns were also composited as a means of evaluating the role of synoptic variability on Arctic cloud regimes. In order to carry out this analysis, a synoptic classification scheme was developed for the Arctic using an objective typing procedure. A second major objective was to analyze model output of pressure fields and cloud parameters from a control run of the Goddard Institue for Space Studies climate model for the same area and to intercompare the synoptic climatatology of the model with that based on the observational data.

Direct and indirect climatic drivers of biotic interactions: ice-cover and carbon runoff shaping Arctic char Salvelinus alpinus and brown trout Salmo trutta competitive asymmetries.

PubMed

Ulvan, Eva M; Finstad, Anders G; Ugedal, Ola; Berg, Ole Kristian

2012-01-01

One of the major challenges in ecological climate change impact science is to untangle the climatic effects on biological interactions and indirect cascading effects through different ecosystems. Here, we test for direct and indirect climatic drivers on competitive impact of Arctic char (Salvelinus alpinus L.) on brown trout (Salmo trutta L.) along a climate gradient in central Scandinavia, spanning from coastal to high-alpine environments. As a measure of competitive impact, trout food consumption was measured using (137)Cs tracer methodology both during the ice-covered and ice-free periods, and contrasted between lakes with or without char coexistence along the climate gradient. Variation in food consumption between lakes was best described by a linear mixed effect model including a three-way interaction between the presence/absence of Arctic char, season and Secchi depth. The latter is proxy for terrestrial dissolved organic carbon run-off, strongly governed by climatic properties of the catchment. The presence of Arctic char had a negative impact on trout food consumption. However, this effect was stronger during ice-cover and in lakes receiving high carbon load from the catchment, whereas no effect of water temperature was evident. In conclusion, the length of the ice-covered period and the export of allochthonous material from the catchment are likely major, but contrasting, climatic drivers of the competitive interaction between two freshwater lake top predators. While future climatic scenarios predict shorter ice-cover duration, they also predict increased carbon run-off. The present study therefore emphasizes the complexity of cascading ecosystem effects in future effects of climate change on freshwater ecosystems.

Making Sense of Complexity with FRE, a Scientific Workflow System for Climate Modeling (Invited)

NASA Astrophysics Data System (ADS)

Langenhorst, A. R.; Balaji, V.; Yakovlev, A.

2010-12-01

A workflow is a description of a sequence of activities that is both precise and comprehensive. Capturing the workflow of climate experiments provides a record which can be queried or compared, and allows reproducibility of the experiments - sometimes even to the bit level of the model output. This reproducibility helps to verify the integrity of the output data, and enables easy perturbation experiments. GFDL's Flexible Modeling System Runtime Environment (FRE) is a production-level software project which defines and implements building blocks of the workflow as command line tools. The scientific, numerical and technical input needed to complete the workflow of an experiment is recorded in an experiment description file in XML format. Several key features add convenience and automation to the FRE workflow: ● Experiment inheritance makes it possible to define a new experiment with only a reference to the parent experiment and the parameters to override. ● Testing is a basic element of the FRE workflow: experiments define short test runs which are verified before the main experiment is run, and a set of standard experiments are verified with new code releases. ● FRE is flexible enough to support short runs with mere megabytes of data, to high-resolution experiments that run on thousands of processors for months, producing terabytes of output data. Experiments run in segments of model time; after each segment, the state is saved and the model can be checkpointed at that level. Segment length is defined by the user, but the number of segments per system job is calculated to fit optimally in the batch scheduler requirements. FRE provides job control across multiple segments, and tools to monitor and alter the state of long-running experiments. ● Experiments are entered into a Curator Database, which stores query-able metadata about the experiment and the experiment's output. ● FRE includes a set of standardized post-processing functions as well as the ability to incorporate user-level functions. FRE post-processing can take us all the way to the preparing of graphical output for a scientific audience, and publication of data on a public portal. ● Recent FRE development includes incorporating a distributed workflow to support remote computing.

Evaluations of high-resolution dynamically downscaled ensembles over the contiguous United States Climate Dynamics

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

Zobel, Zachary; Wang, Jiali; Wuebbles, Donald J.

This study uses Weather Research and Forecast (WRF) model to evaluate the performance of six dynamical downscaled decadal historical simulations with 12-km resolution for a large domain (7200 x 6180 km) that covers most of North America. The initial and boundary conditions are from three global climate models (GCMs) and one reanalysis data. The GCMs employed in this study are the Geophysical Fluid Dynamics Laboratory Earth System Model with Generalized Ocean Layer Dynamics component, Community Climate System Model, version 4, and the Hadley Centre Global Environment Model, version 2-Earth System. The reanalysis data is from the National Centers for Environmentalmore » Prediction-US. Department of Energy Reanalysis II. We analyze the effects of bias correcting, the lateral boundary conditions and the effects of spectral nudging. We evaluate the model performance for seven surface variables and four upper atmospheric variables based on their climatology and extremes for seven subregions across the United States. The results indicate that the simulation’s performance depends on both location and the features/variable being tested. We find that the use of bias correction and/or nudging is beneficial in many situations, but employing these when running the RCM is not always an improvement when compared to the reference data. The use of an ensemble mean and median leads to a better performance in measuring the climatology, while it is significantly biased for the extremes, showing much larger differences than individual GCM driven model simulations from the reference data. This study provides a comprehensive evaluation of these historical model runs in order to make informed decisions when making future projections.« less

Compressing climate model simulations: reducing storage burden while preserving information

NASA Astrophysics Data System (ADS)

Hammerling, Dorit; Baker, Allison; Xu, Haiying; Clyne, John; Li, Samuel

2017-04-01

Climate models, which are run at high spatial and temporal resolutions, generate massive quantities of data. As our computing capabilities continue to increase, storing all of the generated data is becoming a bottleneck, which negatively affects scientific progress. It is thus important to develop methods for representing the full datasets by smaller compressed versions, which still preserve all the critical information and, as an added benefit, allow for faster read and write operations during analysis work. Traditional lossy compression algorithms, as for example used for image files, are not necessarily ideally suited for climate data. While visual appearance is relevant, climate data has additional critical features such as the preservation of extreme values and spatial and temporal gradients. Developing alternative metrics to quantify information loss in a manner that is meaningful to climate scientists is an ongoing process still in its early stages. We will provide an overview of current efforts to develop such metrics to assess existing algorithms and to guide the development of tailored compression algorithms to address this pressing challenge.

Wind extremes in the North Sea basin under climate change: an ensemble study of 12 CMIP5 GCMs

NASA Astrophysics Data System (ADS)

de Winter, R.; Ruessink, G.; Sterl, A.

2012-12-01

Coastal safety may be influenced by climate change, as changes in extreme surge levels and wave extremes may increase the vulnerability of dunes and other coastal defenses. In the North Sea, an area already prone to severe flooding, these high surge levels and waves are generated by severe wind speeds during storm events. As a result of the geometry of the North Sea, not only the maximum wind speed is relevant, but also wind direction. Analyzing changes in a changing climate implies that several uncertainties need to be taken into account. First, there is the uncertainty in climate experiments, which represents the possible development of the emission of greenhouse gases. Second, there is uncertainty between the climate models that are used to analyze the effect of different climate experiments. The third uncertainty is the natural variability of the climate. When this system variability is large, small trends will be difficult to detect. The natural variability results in statistical uncertainty, especially for events with high return values. We addressed the first two types of uncertainties for extreme wind conditions in the North Sea using 12 CMIP5 GCMs. To evaluate the differences between the climate experiments, two climate experiments (rcp4.5 and rcp8.5) from 2050-2100 are compared with historical runs, running from 1950-2000. Rcp4.5 is considered to be a middle climate experiment and rcp8.5 represents high-end climate scenarios. The projections of the 12 GCMs for a given scenario illustrate model uncertainty. We focus on the North Sea basin, because changes in wind conditions could have a large impact on safety of the densely populated North Sea coast, an area that has already a high exposure to flooding. Our results show that, consistent with ERA-Interim results, the annual maximum wind speed in the historical run demonstrates large interannual variability. For the North Sea, the annual maximum wind speed is not projected to change in either rcp4.5 or rcp8.5. In fact, the differences in the 12 GCMs are larger than the difference between the three experiments. Furthermore, our results show that, the variation in direction of annual maximum wind speed is large and this precludes a firm statement on climate-change induced changes in these directions. Nonetheless, most models indicate a decrease in annual maximum wind speed from south-eastern directions and an increase from south-western and western directions. This might be caused by a poleward shift of the storm track. The amount of wind from north-west and north-north-west, wind directions that are responsible for the development of extreme storm surges in the southern part of the North Sea, are not projected to change. However, North Sea coasts that have the longest fetch for western direction, e.g. the German Bight, may encounter more often high storm surge levels and extreme waves when the annual maximum wind will indeed be more often from western direction.

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.;

Identifying misbehaving models using baseline climate variance

NASA Astrophysics Data System (ADS)

Schultz, Colin

2011-06-01

The majority of projections made using general circulation models (GCMs) are conducted to help tease out the effects on a region, or on the climate system as a whole, of changing climate dynamics. Sun et al., however, used model runs from 20 different coupled atmosphere-ocean GCMs to try to understand a different aspect of climate projections: how bias correction, model selection, and other statistical techniques might affect the estimated outcomes. As a case study, the authors focused on predicting the potential change in precipitation for the Murray-Darling Basin (MDB), a 1-million- square- kilometer area in southeastern Australia that suffered a recent decade of drought that left many wondering about the potential impacts of climate change on this important agricultural region. The authors first compared the precipitation predictions made by the models with 107 years of observations, and they then made bias corrections to adjust the model projections to have the same statistical properties as the observations. They found that while the spread of the projected values was reduced, the average precipitation projection for the end of the 21st century barely changed. Further, the authors determined that interannual variations in precipitation for the MDB could be explained by random chance, where the precipitation in a given year was independent of that in previous years.

Cross-scale intercomparison of climate change impacts simulated by regional and global hydrological models in eleven large river basins

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

Hattermann, F. F.; Krysanova, V.; Gosling, S. N.

Ideally, the results from models operating at different scales should agree in trend direction and magnitude of impacts under climate change. However, this implies that the sensitivity of impact models designed for either scale to climate variability and change is comparable. In this study, we compare hydrological changes simulated by 9 global and 9 regional hydrological models (HM) for 11 large river basins in all continents under reference and scenario conditions. The foci are on model validation runs, sensitivity of annual discharge to climate variability in the reference period, and sensitivity of the long-term average monthly seasonal dynamics to climatemore » change. One major result is that the global models, mostly not calibrated against observations, often show a considerable bias in mean monthly discharge, whereas regional models show a much better reproduction of reference conditions. However, the sensitivity of two HM ensembles to climate variability is in general similar. The simulated climate change impacts in terms of long-term average monthly dynamics evaluated for HM ensemble medians and spreads show that the medians are to a certain extent comparable in some cases with distinct differences in others, and the spreads related to global models are mostly notably larger. Summarizing, this implies that global HMs are useful tools when looking at large-scale impacts of climate change and variability, but whenever impacts for a specific river basin or region are of interest, e.g. for complex water management applications, the regional-scale models validated against observed discharge should be used.« less

Improving city forests through assessment, modelling and monitoring

Treesearch

D.J. Nowak

2018-01-01

Urban and peri-urban forests produce numerous benefits for society. These include moderating the climate; reducing energy use in buildings; sequestering atmospheric carbon dioxide; improving air and water quality; mitigating rainfall run-off and flooding; providing an aesthetic environment and recreational opportunities; enhancing human health and social well-being;...

Introduction to EPA's Stormwater Calculator - Incorporating Low Impact Development and Climate Science Tools

EPA Science Inventory

The EPA Office of Research and Development released its National Stormwater Calculator (SWC) which is available at: http://www.epa.gov/nrmrl/wswrd/wq/models/swc/ (contact: SWC@EPA.gov). It is a desktop application that estimates the annual amount of rainwater and frequency of run...

Evaluation of Rapid Adjustments to Radiative Forcing for Five Climate Forcing Agents in the Precipitation Driver Response Model Intercomparison Project (PDRMIP)

NASA Astrophysics Data System (ADS)

Smith, C. J.; Forster, P.; Richardson, T.; Myhre, G.

2016-12-01

Effective radiative forcing (ERF), rather than "traditional" radiative forcing (RF), has become an increasingly popular metric in recent years, as it more closely links the difference in the earth's top-of-atmosphere (TOA) energy budget to equilibrium near-surface temperature rise. One method to diagnose ERF is to take the difference of TOA radiative fluxes from two climate model runs (a perturbation and a control) with prescribed sea-surface temperatures and sea-ice coverage. ERF can be thought of as the sum of a direct forcing, which is the pure radiative effect of a forcing agent, plus rapid adjustments, which are changes in climate state triggered by the forcing agent that themselves affect the TOA energy budget and are unrelated to surface temperature changes.In addition to the classic experiment of doubling of CO2 (2xCO2), we analyse rapid adjustments to a tripling of methane (3xCH4), a quintupling of sulphate aerosol (5xSul), a ten times increase in black carbon (10xBC) and a 2% increase in the solar constant (2%Sol). We use CMIP-style climate model diagnostics from six participating models of the Precipitation Driver Response Model Intercomparison Project (PDRMIP).Assuming approximately linear contributions to the TOA flux differences, the rapid adjustments from changes in atmospheric temperature, surface temperature, surface albedo and water vapour can be cleanly and simply separated from the direct forcing by radiative kernels. The rapid adjustments are in turn decomposed into stratospheric and tropospheric components. We introduce kernels based on the HadGEM2 climate model and find similar results to those based on other models. Cloud adjustments are evaluated as a residual of the TOA radiative fluxes between all-sky and clear-sky runs once direct forcing and rapid adjustments have been subtracted. The cloud adjustments are also calculated online within the HadGEM2 model using the ISCCP simulator. For aerosol forcing experiments, rapid adjustments vary substantially between models. Much of the contribution to this model spread is in the cloud adjustments. We also notice a spread in the model calculations of direct forcing for greenhouse gases, which suggest differences in the radiative transfer parameterisations used by each model.

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.

The NorWeST Stream Temperature Database, Model, and Climate Scenarios for the Northwest U.S. (Invited)

NASA Astrophysics Data System (ADS)

Isaak, D.; Wenger, S.; Peterson, E.; Ver Hoef, J.; Luce, C.; Hostetler, S. W.; Kershner, J.; Dunham, J.; Nagel, D.; Roper, B.

2013-12-01

Anthropogenic climate change is warming the Earth's rivers and streams and threatens significant changes to aquatic biodiversity. Effective threat response will require prioritization of limited conservation resources and coordinated interagency efforts guided by accurate information about climate, and climate change, at scales relevant to the distributions of species across landscapes. Here, we describe the NorWeST (i.e., NorthWest Stream Temperature) project to develop a comprehensive interagency stream temperature database and high-resolution climate scenarios across Washington, Oregon, Idaho, Montana, and Wyoming (~400,000 stream kilometers). The NorWeST database consists of stream temperature data contributed by >60 state, federal, tribal, and private resource agencies and may be the largest of its kind in the world (>45,000,000 hourly temperature recordings at >15,000 unique monitoring sites). These data are being used with spatial statistical network models to accurately downscale (R2 = 90%; RMSE < 1 C) global climate patterns to all perennially flowing reaches within river networks at 1-kilometer resolution. Historic stream temperature scenarios are developed using air temperature data from RegCM3 runs for the NCEP historical reanalysis and future scenarios (2040s and 2080s) are developed by applying bias corrected air temperature and discharge anomalies from ensemble climate and hydrology model runs for A1B and A2 warming trajectories. At present, stream temperature climate scenarios have been developed for 230,000 stream kilometers across Idaho and western Montana using data from more than 7,000 monitoring sites. The raw temperature data and stream climate scenarios are made available as ArcGIS geospatial products for download through the NorWeST website as individual river basins are completed (http://www.fs.fed.us/rm/boise/AWAE/projects/NorWeST.shtml). By providing open access to temperature data and scenarios, the project is fostering new research on stream temperatures and better collaborative management of aquatic resources through improved: 1) climate vulnerability assessments for sensitive species, 2) decision support tools that use regionally consistent scenarios, 3) water quality assessments, and 4) temperature and biological monitoring programs. Additional project details are contained in this Great Northern Landscape Conservation Cooperative newsletter (http://greatnorthernlcc.org/features/streamtemp-database).

Shallow aquifer response to climate change scenarios in a small catchment in the Guarani Aquifer outcrop zone.

PubMed

Melo, Davi C D; Wendland, Edson

2017-05-01

Water availability restrictions are already a reality in several countries. This issue is likely to worsen due to climate change, predicted for the upcoming decades. This study aims to estimate the impacts of climate change on groundwater system in the Guarani Aquifer outcrop zone. Global Climate Models (GCM) outputs were used as inputs to a water balance model, which produced recharge estimates for the groundwater model. Recharge was estimated across different land use types considering a control period from 2004 to 2014, and a future period from 2081 to 2099. Major changes in monthly rainfall means are expected to take place in dry seasons. Most of the analysed scenarios predict increase of more than 2 ºC in monthly mean temperatures. Comparing the control and future runs, our results showed a mean recharge change among scenarios that ranged from ~-80 to ~+60%, depending on the land use type. As a result of such decrease in recharge rates, the response given by the groundwater model indicates a lowering of the water table under most scenarios.

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.

Summary of results of January climate simulations with the GISS coarse-mesh model

NASA Technical Reports Server (NTRS)

Spar, J.; Cohen, C.; Wu, P.

1981-01-01

The large scale climates generated by extended runs of the model are relatively independent of the initial atmospheric conditions, if the first few months of each simulation are discarded. The perpetual January simulations with a specified SST field produced excessive snow accumulation over the continents of the Northern Hemisphere. Mass exchanges between the cold (warm) continents and the warm (cold) adjacent oceans produced significant surface pressure changes over the oceans as well as over the land. The effect of terrain and terrain elevation on the amount of precipitation was examined. The evaporation of continental moisture was calculated to cause large increases in precipitation over the continents.

ARM/GCSS/SPARC TWP-ICE CRM Intercomparison Study

NASA Technical Reports Server (NTRS)

Fridlind, Ann; Ackerman, Andrew; Petch, Jon; Field, Paul; Hill, Adrian; McFarquhar, Greg; Xie, Shaocheng; Zhang, Minghua

2010-01-01

Specifications are provided for running a cloud-resolving model (CRM) and submitting results in a standardized format for inclusion in a n intercomparison study and archiving for public access. The simulated case study is based on measurements obtained during the 2006 Tropical Warm Pool - International Cloud Experiment (TWP-ICE) led by the U. S. department of Energy Atmospheric Radiation Measurement (ARM) program. The modeling intercomparison study is based on objectives developed in concert with the Stratospheric Processes And their Role in Climate (SPARC) program and the GEWEX cloud system study (GCSS) program. The Global Energy and Water Cycle Experiment (GEWEX) is a core project of the World Climate Research PRogramme (WCRP).

Combining climatic and soil properties better predicts covers of Brazilian biomes.

PubMed

Arruda, Daniel M; Fernandes-Filho, Elpídio I; Solar, Ricardo R C; Schaefer, Carlos E G R

2017-04-01

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km 2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

Combining climatic and soil properties better predicts covers of Brazilian biomes

NASA Astrophysics Data System (ADS)

Arruda, Daniel M.; Fernandes-Filho, Elpídio I.; Solar, Ricardo R. C.; Schaefer, Carlos E. G. R.

2017-04-01

Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

Fluctuations of a Greenlandic tidewater glacier driven by changes in atmospheric forcing: observations and modelling of Kangiata Nunaata Sermia, 1859-present

NASA Astrophysics Data System (ADS)

Lea, J. M.; Mair, D. W. F.; Nick, F. M.; Rea, B. R.; van As, D.; Morlighem, M.; Nienow, P. W.; Weidick, A.

2014-11-01

Many tidewater glaciers in Greenland are known to have undergone significant retreat during the last century following their Little Ice Age maxima. Where it is possible to reconstruct glacier change over this period, they provide excellent records for comparison to climate records, as well as calibration/validation for numerical models. These glacier change records therefore allow for tests of numerical models that seek to simulate tidewater glacier behaviour over multi-decadal to centennial timescales. Here we present a detailed record of behaviour from Kangiata Nunaata Sermia (KNS), SW Greenland, between 1859 and 2012, and compare it against available oceanographic and atmospheric temperature data between 1871 and 2012. We also use these records to evaluate the ability of a well-established one-dimensional flow-band model to replicate behaviour for the observation period. The record of terminus change demonstrates that KNS has advanced/retreated in phase with atmosphere and ocean climate anomalies averaged over multi-annual to decadal timescales. Results from an ensemble of model runs demonstrate that observed dynamics can be replicated. Model runs that provide a reasonable match to observations always require a significant atmospheric forcing component, but do not necessarily require an oceanic forcing component. Although the importance of oceanic forcing cannot be discounted, these results demonstrate that changes in atmospheric forcing are likely to be a primary driver of the terminus fluctuations of KNS from 1859 to 2012. We propose that the detail and length of the record presented makes KNS an ideal site for model validation exercises investigating links between climate, calving rates, and tidewater glacier dynamics.

Development of a Trajectory Model for the Analysis of Stratospheric Water Vapor

NASA Astrophysics Data System (ADS)

Koby, Timothy Robert

To study stratospheric water vapor, a new trajectory model was created. The model is built from first principles specific to stratospheric motion and can run on any gridded dataset, making it more versatile than current solutions. The design of a new model was motivated by measurements of elevated stratospheric water vapor, which in situ isotopic measurements have determined to be tropospheric in origin. A moist stratosphere has substantial feedbacks in the climate system including radiative, chemical, and biological effects. Additionally, elevated stratospheric water vapor is theorized as an important coupling in the historical transition to the Eocene, 56 million years ago, as well as emergence from the Eocene 40 million years ago. This transition mirrors modern climate change, both in surface temperature and carbon dioxide increase. However, the historical transition became much more extreme and settled to a state of warm temperatures from the equator to the poles with little variation in between. The lack of latitudinal gradient in temperature is associated with a moist stratosphere, which provides additional motivation for thoroughly understanding the effects of adding water vapor to the stratosphere in a climatological context. The time evolution of water vapor enhancements from convective injection is analyzed by initializing trajectories over satellite-measured water vapor enhancements. The model runs show water vapor concentrations that remain elevated over the background concentrations for several days and often over a week, which is of the timescale that warrants concern over increased halogen catalyzed ozone loss and the subsequent risk to public health. By analyzing stratospheric winds during the summer months over North America using normalized angular momentum, a pattern of frequent stratospheric anticyclonic activity over North America emerges as a unique feature of the region. This provides a mechanism for the modeled persistent elevated water vapor and validates observations. In a climate like today's with increasing surface radiative forcing, the magnitude and frequency of convective injection may increase, with dramatic consequences on the climate system and human health.

Parametric vs. non-parametric daily weather generator: validation and comparison

NASA Astrophysics Data System (ADS)

Dubrovsky, Martin

2016-04-01

As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30 years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series from several European stations available from the ECA&D database.

Observation-based Estimate of Climate Sensitivity with a Scaling Climate Response Function

NASA Astrophysics Data System (ADS)

Hébert, Raphael; Lovejoy, Shaun

2016-04-01

To properly adress the anthropogenic impacts upon the earth system, an estimate of the climate sensitivity to radiative forcing is essential. Observation-based estimates of climate sensitivity are often limited by their ability to take into account the slower response of the climate system imparted mainly by the large thermal inertia of oceans, they are nevertheless essential to provide an alternative to estimates from global circulation models and increase our confidence in estimates of climate sensitivity by the multiplicity of approaches. It is straightforward to calculate the Effective Climate Sensitivity(EffCS) as the ratio of temperature change to the change in radiative forcing; the result is almost identical to the Transient Climate Response(TCR), but it underestimates the Equilibrium Climate Sensitivity(ECS). A study of global mean temperature is thus presented assuming a Scaling Climate Response Function to deterministic radiative forcing. This general form is justified as there exists a scaling symmetry respected by the dynamics, and boundary conditions, over a wide range of scales and it allows for long-range dependencies while retaining only 3 parameter which are estimated empirically. The range of memory is modulated by the scaling exponent H. We can calculate, analytically, a one-to-one relation between the scaling exponent H and the ratio of EffCS to TCR and EffCS to ECS. The scaling exponent of the power law is estimated by a regression of temperature as a function of forcing. We consider for the analysis 4 different datasets of historical global mean temperature and 100 scenario runs of the Coupled Model Intercomparison Project Phase 5 distributed among the 4 Representative Concentration Pathways(RCP) scenarios. We find that the error function for the estimate on historical temperature is very wide and thus, many scaling exponent can be used without meaningful changes in the fit residuals of historical temperatures; their response in the year 2100 on the other hand, is very broad, especially for a low-emission scenario such as RCP 2.6. CMIP5 scenario runs thus allow for a narrower estimate of H which can then be used to estimate the ECS and TCR from the EffCS estimated from the historical data.

SPAGETTA: a Multi-Purpose Gridded Stochastic Weather Generator

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Huth, R.; Rotach, M. W.; Dabhi, H.

2017-12-01

SPAGETTA is a new multisite/gridded multivariate parametric stochastic weather generator (WG). Site-specific precipitation occurrence and amount are modelled by Markov chain and Gamma distribution, the non-precipitation variables are modelled by an autoregressive (AR) model conditioned on precipitation occurrence, and the spatial coherence of all variables is modelled following the Wilks' (2009) approach. SPAGETTA may be run in two modes. Mode 1: it is run as a classical WG, which is calibrated using weather series from multiple sites, and only then it may produce arbitrarily long synthetic series mimicking the spatial and temporal structure of the calibration data. To generate the weather series representing the future climate, the WG parameters are modified according to the climate change scenario, typically derived from GCM or RCM simulations. Mode 2: the user provides only basic information (not necessarily to be realistic) on the temporal and spatial auto-correlation structure of the weather variables and their mean annual cycle; the generator itself derives the parameters of the underlying AR model, which produces the multi-site weather series. Optionally, the user may add the spatially varying trend, which is superimposed to the synthetic series. The contribution consists of following parts: (a) Model of the WG. (b) Validation of WG in terms of the spatial temperature and precipitation characteristics, including characteristics of spatial hot/cold/dry/wet spells. (c) Results of the climate change impact experiment, in which the WG parameters representing the spatial and temporal variability are modified using the climate change scenarios and the effect on the above spatial validation indices is analysed. In this experiment, the WG is calibrated using the E-OBS gridded daily weather data for several European regions, and the climate change scenarios are derived from the selected RCM simulations (CORDEX database). (d) The second mode of operation will be demonstrated by results obtained while developing the methodology for assessing collective significance of trends in multi-site weather series. The performance of the proposed test statistics is assessed based on large number of realisations of synthetic series produced by WG assuming a given statistical structure and trend of the weather series.

Nudging the Arctic Ocean to quantify Arctic sea ice feedbacks

NASA Astrophysics Data System (ADS)

Dekker, Evelien; Severijns, Camiel; Bintanja, Richard

2017-04-01

It is well-established that the Arctic is warming 2 to 3 time faster than rest of the planet. One of the great uncertainties in climate research is related to what extent sea ice feedbacks amplify this (seasonally varying) Arctic warming. Earlier studies have analyzed existing climate model output using correlations and energy budget considerations in order to quantify sea ice feedbacks through indirect methods. From these analyses it is regularly inferred that sea ice likely plays an important role, but details remain obscure. Here we will take a different and a more direct approach: we will keep the sea ice constant in a sensitivity simulation, using a state-of -the-art climate model (EC-Earth), applying a technique that has never been attempted before. This experimental technique involves nudging the temperature and salinity of the ocean surface (and possibly some layers below to maintain the vertical structure and mixing) to a predefined prescribed state. When strongly nudged to existing (seasonally-varying) sea surface temperatures, ocean salinity and temperature, we force the sea ice to remain in regions/seasons where it is located in the prescribed state, despite the changing climate. Once we obtain fixed' sea ice, we will run a future scenario, for instance 2 x CO2 with and without prescribed sea ice, with the difference between these runs providing a measure as to what extent sea ice contributes to Arctic warming, including the seasonal and geographical imprint of the effects.

CORDEX.be: COmbining Regional climate Downscaling EXpertise in Belgium

NASA Astrophysics Data System (ADS)

Termonia, P.

2015-12-01

The main objective of the ongoing project CORDEX.be, "COmbining Regional Downscaling EXpertise in Belgium: CORDEX and Beyond", is to gather existing and ongoing Belgian research activities in the domain of climate modelling to create a coherent scientific basis for future climate services in Belgium. The project regroups 8 Belgian Institutes under a single research program of the Belgian Science Policy (BELSPO). The project involves three regional climate models: the ALARO model, the COSMO-CLM model and the MAR model running according to the guidelines of the CORDEX project and at convection permitting resolution on small domains over Belgium. The project creates a framework to address four objectives/challenges. First, this projects aims to contribute to the EURO-CORDEX project. Secondly, RCP simulations are executed at convection-permitting resolutions (3 to 5 km) on small domains. Thirdly, the output of the atmospheric models is used to drive land surface models (the SURFEX model and the Urbclim model) with urban modules, a crop model (REGCROP), a tides and storm model (COHERENS) and the MEGAN-MOHYCAN model that simulates the fluxes emitted by vegetation. Finally, one work package will translate the uncertainty present in the CORDEX database to the high-resolution output of the CORDEX.be project. The organization of the project will be presented and first results will be shown, demonstrating that convection-permitting models can add extra skill to the mesoscale version of the regional climate models, in particular regarding the extreme value statistics and the diurnal cycle.

CORDEX.be: COmbining Regional climate Downscaling EXpertise in Belgium

NASA Astrophysics Data System (ADS)

Termonia, Piet; Van Schaeybroeck, Bert; De Ridder, Koen; Fettweis, Xavier; Gobin, Anne; Luyten, Patrick; Marbaix, Philippe; Pottiaux, Eric; Stavrakou, Trissevgeni; Van Lipzig, Nicole; van Ypersele, Jean-Pascal; Willems, Patrick

2016-04-01

The main objective of the ongoing project CORDEX.be, "COmbining Regional Downscaling EXpertise in Belgium: CORDEX and Beyond" is to gather existing and ongoing Belgian research activities in the domain of climate modelling to create a coherent scientific basis for future climate services in Belgium. The project regroups eight Belgian Institutes under a single research program of the Belgian Science Policy (BELSPO). The project involves three regional climate models: the ALARO model, the COSMO-CLM model and the MAR model running according to the guidelines of the CORDEX project and at convection permitting resolution on small domains over Belgium. The project creates a framework to address four objectives/challenges. First, this projects aims to contribute to the EURO-CORDEX project. Secondly, RCP simulations are executed at convection-permitting resolutions (3 to 5 km) on small domains. Thirdly, the output of the atmospheric models is used to drive land surface models (the SURFEX model and the Urbclim model) with urban modules, a crop model (REGCROP), a tides and storm model (COHERENS) and the MEGAN-MOHYCAN model that simulates the fluxes emitted by vegetation. Finally, one work package will translate the uncertainty present in the CORDEX database to the high-resolution output of the CORDEX.be project. The organization of the project will be presented and first results will be shown, demonstrating that convection-permitting models can add extra skill to the mesoscale version of the regional climate models, in particular regarding the extreme value statistics and the diurnal cycle.

Climatology of salt transitions and implications for stone weathering.

PubMed

Grossi, C M; Brimblecombe, P; Menéndez, B; Benavente, D; Harris, I; Déqué, M

2011-06-01

This work introduces the notion of salt climatology. It shows how climate affects salt thermodynamic and the potential to relate long-term salt damage to climate types. It mainly focuses on specific sites in Western Europe, which include some cities in France and Peninsular Spain. Salt damage was parameterised using the number of dissolution-crystallisation events for unhydrated (sodium chloride) and hydrated (sodium sulphate) systems. These phase transitions have been calculated using daily temperature and relative humidity from observation meteorological data and Climate Change models' output (HadCM3 and ARPEGE). Comparing the number of transitions with meteorological seasonal data allowed us to develop techniques to estimate the frequency of salt transitions based on the local climatology. Results show that it is possible to associate the Köppen-Geiger climate types with potential salt weathering. Temperate fully humid climates seem to offer the highest potential for salt damage and possible higher number of transitions in summer. Climates with dry summers tend to show a lesser frequency of transitions in summer. The analysis of temperature, precipitation and relative output from Climate Change models suggests changes in the Köppen-Geiger climate types and changes in the patterns of salt damage. For instance, West Europe areas with a fully humid climate may change to a more Mediterranean like or dry climates, and consequently the seasonality of different salt transitions. The accuracy and reliability of the projections might be improved by simultaneously running multiple climate models (ensembles). Copyright © 2011 Elsevier B.V. All rights reserved.

Future heat-waves, droughts and floods in 571 European cities

NASA Astrophysics Data System (ADS)

Guerreiro, Selma B.; Dawson, Richard J.; Kilsby, Chris; Lewis, Elizabeth; Ford, Alistair

2018-03-01

Cities are particularly vulnerable to climate risks due to their agglomeration of people, buildings and infrastructure. Differences in methodology, hazards considered, and climate models used limit the utility and comparability of climate studies on individual cities. Here we assess, for the first time, future changes in flood, heat-waves (HW), and drought impacts for all 571 European cities in the Urban Audit database using a consistent approach. To capture the full range of uncertainties in natural variability and climate models, we use all climate model runs from the Coupled Model Inter-comparison Project Phase 5 (CMIP5) for the RCP8.5 emissions scenario to calculate Low, Medium and High Impact scenarios, which correspond to the 10th, 50th and 90th percentiles of each hazard for each city. We find that HW days increase across all cities, but especially in southern Europe, whilst the greatest HW temperature increases are expected in central European cities. For the low impact scenario, drought conditions intensify in southern European cities while river flooding worsens in northern European cities. However, the high impact scenario projects that most European cities will see increases in both drought and river flood risks. Over 100 cities are particularly vulnerable to two or more climate impacts. Moreover, the magnitude of impacts exceeds those previously reported highlighting the substantial challenge cities face to manage future climate risks.

Watershed-scale response to climate change through the twenty-first century for selected basins across the United States

USGS Publications Warehouse

Hay, Lauren E.; Markstrom, Steven; Ward-Garrison, Christian D.

2011-01-01

The hydrologic response of different climate-change emission scenarios for the twenty-first century were evaluated in 14 basins from different hydroclimatic regions across the United States using the Precipitation-Runoff Modeling System (PRMS), a process-based, distributed-parameter watershed model. This study involves four major steps: 1) setup and calibration of the PRMS model in 14 basins across the United States by local U.S. Geological Survey personnel; 2) statistical downscaling of the World Climate Research Programme’s Coupled Model Intercomparison Project phase 3 climate-change emission scenarios to create PRMS input files that reflect these emission scenarios; 3) run PRMS for the climate-change emission scenarios for the 14 basins; and 4) evaluation of the PRMS output.This paper presents an overview of this project, details of the methodology, results from the 14 basin simulations, and interpretation of these results. A key finding is that the hydrological response of the different geographical regions of the United States to potential climate change may be very different, depending on the dominant physical processes of that particular region. Also considered is the tremendous amount of uncertainty present in the climate emission scenarios and how this uncertainty propagates through the hydrologic simulations. This paper concludes with a discussion of the lessons learned and potential for future work.

Climate change effects on international stability : a white paper.

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

Murphy, Kathryn; Taylor, Mark A.; Fujii, Joy

2004-12-01

This white paper represents a summary of work intended to lay the foundation for development of a climatological/agent model of climate-induced conflict. The paper combines several loosely-coupled efforts and is the final report for a four-month late-start Laboratory Directed Research and Development (LDRD) project funded by the Advanced Concepts Group (ACG). The project involved contributions by many participants having diverse areas of expertise, with the common goal of learning how to tie together the physical and human causes and consequences of climate change. We performed a review of relevant literature on conflict arising from environmental scarcity. Rather than simply reviewingmore » the previous work, we actively collected data from the referenced sources, reproduced some of the work, and explored alternative models. We used the unfolding crisis in Darfur (western Sudan) as a case study of conflict related to or triggered by climate change, and as an exercise for developing a preliminary concept map. We also outlined a plan for implementing agents in a climate model and defined a logical progression toward the ultimate goal of running both types of models simultaneously in a two-way feedback mode, where the behavior of agents influences the climate and climate change affects the agents. Finally, we offer some ''lessons learned'' in attempting to keep a diverse and geographically dispersed group working together by using Web-based collaborative tools.« less

Numerical run-out modelling used for reassessment of existing permanent avalanche paths in the Krkonose Mts., Czechia

NASA Astrophysics Data System (ADS)

Blahut, Jan; Klimes, Jan; Balek, Jan; Taborik, Petr; Juras, Roman; Pavlasek, Jiri

2015-04-01

Run-out modelling of snow avalanches is being widely applied in high mountain areas worldwide. This study presents application of snow avalanche run-out calculation applied to mid-mountain ranges - the Krkonose, Jeseniky and Kralicky Sneznik Mountains. All mentioned mountain ranges lie in the northern part of Czechia, close to the border with Poland. Its highest peak reaches only 1602 m a.s.l. However, climatic conditions and regular snowpack presence are the reason why these mountain ranges experience considerable snow avalanche activity every year, sometimes resulting in injuries or even fatalities. Within the aim of an applied project dealing with snow avalanche hazard prediction a re-assessment of permanent snow avalanche paths has been performed based on extensive statistics covering period from 1961/62 till present. On each avalanche path different avalanches with different return periods were modelled using the RAMMS code. As a result, an up-to-date snow avalanche hazard map was prepared.

A comparative modeling study on non-climatic and climatic risk assessment on Asian Tiger Mosquito (Aedes albopictus)

PubMed Central

Shafapour Tehrany, Mahyat; Solhjouy-fard, Samaneh; Kumar, Lalit

2018-01-01

Aedes albopictus, the Asian Tiger Mosquito, vector of Chikungunya, Dengue Fever and Zika viruses, has proven its hardy adaptability in expansion from its natural Asian, forest edge, tree hole habitat on the back of international trade transportation, re-establishing in temperate urban surrounds, in a range of water receptacles and semi-enclosures of organic matter. Conventional aerial spray mosquito vector controls focus on wetland and stagnant water expanses, proven to miss the protected hollows and crevices favoured by Ae. albopictus. New control or eradication strategies are thus essential, particular in light of potential expansions in the southeastern and eastern USA. Successful regional vector control strategies require risk level analysis. Should strategies prioritize regions with non-climatic or climatic suitability parameters for Ae. albopictus? Our study used current Ae. albopictus distribution data to develop two independent models: (i) regions with suitable non-climatic factors, and (ii) regions with suitable climate for Ae. albopictus in southeastern USA. Non-climatic model processing used Evidential Belief Function (EBF), together with six geographical conditioning factors (raster data layers), to establish the probability index. Validation of the analysis results was estimated with area under the curve (AUC) using Ae. albopictus presence data. Climatic modeling was based on two General Circulation Models (GCMs), Miroc3.2 and CSIRO-MK30 running the RCP 8.5 scenario in MaxEnt software. EBF non-climatic model results achieved a 0.70 prediction rate and 0.73 success rate, confirming suitability of the study site regions for Ae. albopictus establishment. The climatic model results showed the best-fit model comprised Coldest Quarter Mean Temp, Precipitation of Wettest Quarter and Driest Quarter Precipitation factors with mean AUC value of 0.86. Both GCMs showed that the whole study site is highly suitable and will remain suitable climatically, according to the prediction for 2055, for Ae. albopictus expansion. PMID:29576954

Climate impacts on agricultural biomass production in the CORDEX.be project context

NASA Astrophysics Data System (ADS)

Gobin, Anne; Van Schaeybroeck, Bert; Termonia, Piet; Willems, Patrick; Van Lipzig, Nicole; Marbaix, Philippe; van Ypersele, Jean-Pascal; Fettweis, Xavier; De Ridder, Koen; Stavrakou, Trissevgeni; Luyten, Patrick; Pottiaux, Eric

2016-04-01

The most important coordinated international effort to translate the IPCC-AR5 outcomes to regional climate modelling is the so-called "COordinated Regional climate Downscaling EXperiment" (CORDEX, http://wcrp-cordex.ipsl.jussieu.fr/). CORDEX.be is a national initiative that aims at combining the Belgian climate and impact modelling research into a single network. The climate network structure is naturally imposed by the top-down data flow, from the four participating upper-air Regional Climate Modelling groups towards seven Local Impact Models (LIMs). In addition to the production of regional climate projections following the CORDEX guidelines, very high-resolution results are provided at convection-permitting resolutions of about 4 km across Belgium. These results are coupled to seven local-impact models with severity indices as output. A multi-model approach is taken that allows uncertainty estimation, a crucial aspect of climate projections for policy-making purposes. The down-scaled scenarios at 4 km resolution allow for impact assessment in different Belgian agro-ecological zones. Climate impacts on arable agriculture are quantified using REGCROP which is a regional dynamic agri-meteorological model geared towards modelling climate impact on biomass production of arable crops (Gobin, 2010, 2012). Results from previous work show that heat stress and water shortages lead to reduced crop growth, whereas increased CO2-concentrations and a prolonged growing season have a positive effect on crop yields. The interaction between these effects depend on the crop type and the field conditions. Root crops such as potato will experience increased drought stress particularly when the probability rises that sensitive crop stages coincide with dry spells. This may be aggravated when wet springs cause water logging in the field and delay planting dates. Despite lower summer precipitation projections for future climate in Belgium, winter cereal yield reductions due to drought stress will be smaller due to earlier maturity. Preliminary results will be presented using the new scenario runs for Belgium.

A comparative modeling study on non-climatic and climatic risk assessment on Asian Tiger Mosquito (Aedes albopictus).

PubMed

Shabani, Farzin; Shafapour Tehrany, Mahyat; Solhjouy-Fard, Samaneh; Kumar, Lalit

2018-01-01

Aedes albopictus , the Asian Tiger Mosquito, vector of Chikungunya, Dengue Fever and Zika viruses, has proven its hardy adaptability in expansion from its natural Asian, forest edge, tree hole habitat on the back of international trade transportation, re-establishing in temperate urban surrounds, in a range of water receptacles and semi-enclosures of organic matter. Conventional aerial spray mosquito vector controls focus on wetland and stagnant water expanses, proven to miss the protected hollows and crevices favoured by Ae. albopictus. New control or eradication strategies are thus essential, particular in light of potential expansions in the southeastern and eastern USA. Successful regional vector control strategies require risk level analysis. Should strategies prioritize regions with non-climatic or climatic suitability parameters for Ae. albopictus ? Our study used current Ae. albopictus distribution data to develop two independent models: (i) regions with suitable non-climatic factors, and (ii) regions with suitable climate for Ae. albopictus in southeastern USA. Non-climatic model processing used Evidential Belief Function (EBF), together with six geographical conditioning factors (raster data layers), to establish the probability index. Validation of the analysis results was estimated with area under the curve (AUC) using Ae. albopictus presence data. Climatic modeling was based on two General Circulation Models (GCMs), Miroc3.2 and CSIRO-MK30 running the RCP 8.5 scenario in MaxEnt software. EBF non-climatic model results achieved a 0.70 prediction rate and 0.73 success rate, confirming suitability of the study site regions for Ae. albopictus establishment. The climatic model results showed the best-fit model comprised Coldest Quarter Mean Temp, Precipitation of Wettest Quarter and Driest Quarter Precipitation factors with mean AUC value of 0.86. Both GCMs showed that the whole study site is highly suitable and will remain suitable climatically, according to the prediction for 2055, for Ae. albopictus expansion.

Equilibrium and Effective Climate Sensitivity

NASA Astrophysics Data System (ADS)

Rugenstein, M.; Bloch-Johnson, J.

2016-12-01

Atmosphere-ocean general circulation models, as well as the real world, take thousands of years to equilibrate to CO2 induced radiative perturbations. Equilibrium climate sensitivity - a fully equilibrated 2xCO2 perturbation - has been used for decades as a benchmark in model intercomparisons, as a test of our understanding of the climate system and paleo proxies, and to predict or project future climate change. Computational costs and limited time lead to the widespread practice of extrapolating equilibrium conditions from just a few decades of coupled simulations. The most common workaround is the "effective climate sensitivity" - defined through an extrapolation of a 150 year abrupt2xCO2 simulation, including the assumption of linear climate feedbacks. The definitions of effective and equilibrium climate sensitivity are often mixed up and used equivalently, and it is argued that "transient climate sensitivity" is the more relevant measure for predicting the next decades. We present an ongoing model intercomparison, the "LongRunMIP", to study century and millennia time scales of AOGCM equilibration and the linearity assumptions around feedback analysis. As a true ensemble of opportunity, there is no protocol and the only condition to participate is a coupled model simulation of any stabilizing scenario simulating more than 1000 years. Many of the submitted simulations took several years to conduct. As of July 2016 the contribution comprises 27 scenario simulations of 13 different models originating from 7 modeling centers, each between 1000 and 6000 years. To contribute, please contact the authors as soon as possible We present preliminary results, discussing differences between effective and equilibrium climate sensitivity, the usefulness of transient climate sensitivity, extrapolation methods, and the state of the coupled climate system close to equilibrium. Caption for the Figure below: Evolution of temperature anomaly and radiative imbalance of 22 simulations with 12 models (color indicates the model). 20 year moving average.

Uncertainties in models of tropospheric ozone based on Monte Carlo analysis: Tropospheric ozone burdens, atmospheric lifetimes and surface distributions

NASA Astrophysics Data System (ADS)

Derwent, Richard G.; Parrish, David D.; Galbally, Ian E.; Stevenson, David S.; Doherty, Ruth M.; Naik, Vaishali; Young, Paul J.

2018-05-01

Recognising that global tropospheric ozone models have many uncertain input parameters, an attempt has been made to employ Monte Carlo sampling to quantify the uncertainties in model output that arise from global tropospheric ozone precursor emissions and from ozone production and destruction in a global Lagrangian chemistry-transport model. Ninety eight quasi-randomly Monte Carlo sampled model runs were completed and the uncertainties were quantified in tropospheric burdens and lifetimes of ozone, carbon monoxide and methane, together with the surface distribution and seasonal cycle in ozone. The results have shown a satisfactory degree of convergence and provide a first estimate of the likely uncertainties in tropospheric ozone model outputs. There are likely to be diminishing returns in carrying out many more Monte Carlo runs in order to refine further these outputs. Uncertainties due to model formulation were separately addressed using the results from 14 Atmospheric Chemistry Coupled Climate Model Intercomparison Project (ACCMIP) chemistry-climate models. The 95% confidence ranges surrounding the ACCMIP model burdens and lifetimes for ozone, carbon monoxide and methane were somewhat smaller than for the Monte Carlo estimates. This reflected the situation where the ACCMIP models used harmonised emissions data and differed only in their meteorological data and model formulations whereas a conscious effort was made to describe the uncertainties in the ozone precursor emissions and in the kinetic and photochemical data in the Monte Carlo runs. Attention was focussed on the model predictions of the ozone seasonal cycles at three marine boundary layer stations: Mace Head, Ireland, Trinidad Head, California and Cape Grim, Tasmania. Despite comprehensively addressing the uncertainties due to global emissions and ozone sources and sinks, none of the Monte Carlo runs were able to generate seasonal cycles that matched the observations at all three MBL stations. Although the observed seasonal cycles were found to fall within the confidence limits of the ACCMIP members, this was because the model seasonal cycles spanned extremely wide ranges and there was no single ACCMIP member that performed best for each station. Further work is required to examine the parameterisation of convective mixing in the models to see if this erodes the isolation of the marine boundary layer from the free troposphere and thus hides the models' real ability to reproduce ozone seasonal cycles over marine stations.

Modelling extreme climatic events in Guadalquivir Estuary ( Spain)

NASA Astrophysics Data System (ADS)

Delgado, Juan; Moreno-Navas, Juan; Pulido, Antoine; García-Lafuente, Juan; Calero Quesada, Maria C.; García, Rodrigo

2017-04-01

Extreme climatic events, such as heat waves and severe storms are predicted to increase in frequency and magnitude as a consequence of global warming but their socio-ecological effects are poorly understood, particularly in estuarine ecosystems. The Guadalquivir Estuary has been anthropologically modified several times, the original salt marshes have been transformed to grow rice and cotton and approximately one-fourth of the total surface of the estuary is now part of two protected areas, one of them is a UNESCO, MAB Biosphere Reserve. The climatic events are most likely to affect Europe in forthcoming decades and a further understanding how these climatic disturbances drive abrupt changes in the Guadalquivir estuary is needed. A barotropic model has been developed to study how severe storm events affects the estuary by conducting paired control and climate-events simulations. The changes in the local wind and atmospheric pressure conditions in the estuary have been studied in detail and several scenarios are obtained by running the model under control and real storm conditions. The model output has been validated with in situ water elevation and good agreement between modelled and real measurements have been obtained. Our preliminary results show that the model demonstrated the capability describe of the tide-surge levels in the estuary, opening the possibility to study the interaction between climatic events and the port operations and food production activities. The barotropic hydrodynamic model provide spatially explicit information on the key variables governing the tide dynamics of estuarine areas under severe climatic scenarios . The numerical model will be a powerful tool in future climate change mitigation and adaptation programs in a complex socio-ecological system.

Fine-Resolution Modeling of the Santa Cruz and San Pedro River Basins for Climate Change and Riparian System Studies

NASA Astrophysics Data System (ADS)

Robles-Morua, A.; Vivoni, E. R.; Volo, T. J.; Rivera, E. R.; Dominguez, F.; Meixner, T.

2011-12-01

This project is part of a multidisciplinary effort aimed at understanding the impacts of climate variability and change on the ecological services provided by riparian ecosystems in semiarid watersheds of the southwestern United States. Valuing the environmental and recreational services provided by these ecosystems in the future requires a numerical simulation approach to estimate streamflow in ungauged tributaries as well as diffuse and direct recharge to groundwater basins. In this work, we utilize a distributed hydrologic model known as the TIN-based Real-time Integrated Basin Simulator (tRIBS) in the upper Santa Cruz and San Pedro basins with the goal of generating simulated hydrological fields that will be coupled to a riparian groundwater model. With the distributed model, we will evaluate a set of climate change and population scenarios to quantify future conditions in these two river systems and their impacts on flood peaks, recharge events and low flows. Here, we present a model confidence building exercise based on high performance computing (HPC) runs of the tRIBS model in both basins during the period of 1990-2000. Distributed model simulations utilize best-available data across the US-Mexico border on topography, land cover and soils obtained from analysis of remotely-sensed imagery and government databases. Meteorological forcing over the historical period is obtained from a combination of sparse ground networks and weather radar rainfall estimates. We then focus on a comparison between simulation runs using ground-based forcing to cases where the Weather Research Forecast (WRF) model is used to specify the historical conditions. Two spatial resolutions are considered from the WRF model fields - a coarse (35-km) and a downscaled (10- km) forcing. Comparisons will focus on the distribution of precipitation, soil moisture, runoff generation and recharge and assess the value of the WRF coarse and downscaled products. These results provide confidence in the model application and a measure of modeling uncertainty that will help set the foundation for forthcoming climate change studies.

A New High Resolution Climate Dataset for Climate Change Impacts Assessments in New England

NASA Astrophysics Data System (ADS)

Komurcu, M.; Huber, M.

2016-12-01

Assessing regional impacts of climate change (such as changes in extreme events, land surface hydrology, water resources, energy, ecosystems and economy) requires much higher resolution climate variables than those available from global model projections. While it is possible to run global models in higher resolution, the high computational cost associated with these simulations prevent their use in such manner. To alleviate this problem, dynamical downscaling offers a method to deliver higher resolution climate variables. As part of an NSF EPSCoR funded interdisciplinary effort to assess climate change impacts on New Hampshire ecosystems, hydrology and economy (the New Hampshire Ecosystems and Society project), we create a unique high-resolution climate dataset for New England. We dynamically downscale global model projections under a high impact emissions scenario using the Weather Research and Forecasting model (WRF) with three nested grids of 27, 9 and 3 km horizontal resolution with the highest resolution innermost grid focusing over New England. We prefer dynamical downscaling over other methods such as statistical downscaling because it employs physical equations to progressively simulate climate variables as atmospheric processes interact with surface processes, emissions, radiation, clouds, precipitation and other model components, hence eliminates fix relationships between variables. In addition to simulating mean changes in regional climate, dynamical downscaling also allows for the simulation of climate extremes that significantly alter climate change impacts. We simulate three time slices: 2006-2015, 2040-2060 and 2080-2100. This new high-resolution climate dataset (with more than 200 variables saved in hourly (six hourly) intervals for the highest resolution domain (outer two domains)) along with model input and restart files used in our WRF simulations will be publicly available for use to the broader scientific community to support in-depth climate change impacts assessments for New England. We present results focusing on future changes in New England extreme events.

Evaluating Impacts of climate and land use changes on streamflow using SWAT and land use models based CESM1-CAM5 Climate scenarios

NASA Astrophysics Data System (ADS)

Lin, Tzu Ping; Lin, Yu Pin; Lien, Wan Yu

2015-04-01

Climate change projects have various levels of impacts on hydrological cycles around the world. The impact of climate change and uncertainty of climate projections from general circulation models (GCMs) from the Coupled Model Intercomparison Project (CMIP5) which has been just be released in Taiwan, 2014. Since the streamflow run into ocean directly due to the steep terrain and the rainfall difference between wet and dry seasons is apparent; as a result, the allocation water resource reasonable is very challenge in Taiwan, particularly under climate change. The purpose of this study was to evaluate the impacts of climate and land use changes on a small watershed in Taiwan. The AR5 General Circulation Models(GCM) output data was adopted in this study and was downscaled from the monthly to the daily weather data as the input data of hydrological model such as Soil and Water Assessment Tool (SWAT) model in this study. The spatially explicit land uses change model, the Conservation of Land Use and its Effects at Small regional extent (CLUE-s), was applied to simulate land use scenarios in 2020-2039. Combined climate and land use change scenarios were adopted as input data of the hydrological model, the SWAT model, to estimate the future streamflows. With the increasing precipitation, increasing urban area and decreasing agricultural and grass land, the annual streamflow in the most of twenty-three subbasins were also increased. Besides, due to the increasing rainfall in wet season and decreasing rainfall in dry season, the difference of streamflow between wet season and dry season are also increased. This result indicates a more stringent challenge on the water resource management in future. Therefore, impacts on water resource caused by climate change and land use change should be considered in water resource planning for the Datuan river watershed. Keywords: SWAT, GCM, CLUE-s, streamflow, climate change, land use change

Reliable low precision simulations in land surface models

NASA Astrophysics Data System (ADS)

Dawson, Andrew; Düben, Peter D.; MacLeod, David A.; Palmer, Tim N.

2017-12-01

Weather and climate models must continue to increase in both resolution and complexity in order that forecasts become more accurate and reliable. Moving to lower numerical precision may be an essential tool for coping with the demand for ever increasing model complexity in addition to increasing computing resources. However, there have been some concerns in the weather and climate modelling community over the suitability of lower precision for climate models, particularly for representing processes that change very slowly over long time-scales. These processes are difficult to represent using low precision due to time increments being systematically rounded to zero. Idealised simulations are used to demonstrate that a model of deep soil heat diffusion that fails when run in single precision can be modified to work correctly using low precision, by splitting up the model into a small higher precision part and a low precision part. This strategy retains the computational benefits of reduced precision whilst preserving accuracy. This same technique is also applied to a full complexity land surface model, resulting in rounding errors that are significantly smaller than initial condition and parameter uncertainties. Although lower precision will present some problems for the weather and climate modelling community, many of the problems can likely be overcome using a straightforward and physically motivated application of reduced precision.

Trends and uncertainties in budburst projections of Norway spruce in Northern Europe.

PubMed

Olsson, Cecilia; Olin, Stefan; Lindström, Johan; Jönsson, Anna Maria

2017-12-01

Budburst is regulated by temperature conditions, and a warming climate is associated with earlier budburst. A range of phenology models has been developed to assess climate change effects, and they tend to produce different results. This is mainly caused by different model representations of tree physiology processes, selection of observational data for model parameterization, and selection of climate model data to generate future projections. In this study, we applied (i) Bayesian inference to estimate model parameter values to address uncertainties associated with selection of observational data, (ii) selection of climate model data representative of a larger dataset, and (iii) ensembles modeling over multiple initial conditions, model classes, model parameterizations, and boundary conditions to generate future projections and uncertainty estimates. The ensemble projection indicated that the budburst of Norway spruce in northern Europe will on average take place 10.2 ± 3.7 days earlier in 2051-2080 than in 1971-2000, given climate conditions corresponding to RCP 8.5. Three provenances were assessed separately (one early and two late), and the projections indicated that the relationship among provenance will remain also in a warmer climate. Structurally complex models were more likely to fail predicting budburst for some combinations of site and year than simple models. However, they contributed to the overall picture of current understanding of climate impacts on tree phenology by capturing additional aspects of temperature response, for example, chilling. Model parameterizations based on single sites were more likely to result in model failure than parameterizations based on multiple sites, highlighting that the model parameterization is sensitive to initial conditions and may not perform well under other climate conditions, whether the change is due to a shift in space or over time. By addressing a range of uncertainties, this study showed that ensemble modeling provides a more robust impact assessment than would a single phenology model run.

An Assessment of the Potential Predictability of Drought Over the United States Based on Climate Model Simulations with Specified SST

NASA Technical Reports Server (NTRS)

Schubert, Siegfried; Wang, Hailan; Suarez, Max; Koster, Randal

2010-01-01

The USCLIV AR working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land-atmosphere feedbacks on regional drought. The runs were done with several global atmospheric models including NASA/NSIPP-l, NCEP/GFS, GFDLlAM2, and NCAR CCM3 and CAM3.5. Specific questions that the runs are designed to address include: What are mechanisms that maintain drought across the seasonal cycle and from one year to the next. To what extent can droughts develop independently of ocean variability due to year-to-year memory that may be inherent to the land. What is the role of the different ocean basins? Here we focus on the potential predictability of drought conditions over the United States. Specific issues addressed include the seasonality and regionality of the signal-to-noise ratios associated with Pacific and Atlantic SST forcing, and the sensitivity of the results to the climatological stationary waves simulated by the different AGCMs.

Momentum and Energy Assessments with NASA and Other Model and Data Assimilation Systems

NASA Technical Reports Server (NTRS)

Salstein, David; Nelson, Peter; Hu, Wen-Jie

2001-01-01

Support from the NASA Global Modeling and Analysis Program has been used for the following research objectives: 1) the study of aspects of dynamics of torques and angular momentum based on the Goddard GEOS and other analyses; 2) the study of how models participating in the second Atmospheric Model Intercomparison Project (AMIP-2) have success in simulating certain large-scale quantities; 3) the study of the energetics and momentum cycle from certain runs from the Goddard Laboratory for Atmospheres and other models as well; 4) the assessment of changes in diabatic heating and related energetics in the community climate model (CCM3); 5) the analysis of modes of climate of the atmosphere, especially the Arctic and North Atlantic Oscillations. Further information on these endeavors will be provided in published works and the Final Report of the project.

The impact of future forest dynamics on climate: interactive effects of changing vegetation and disturbance regimes.

PubMed

Thom, Dominik; Rammer, Werner; Seidl, Rupert

2017-11-01

Currently, the temperate forest biome cools the earth's climate and dampens anthropogenic climate change. However, climate change will substantially alter forest dynamics in the future, affecting the climate regulation function of forests. Increasing natural disturbances can reduce carbon uptake and evaporative cooling, but at the same time increase the albedo of a landscape. Simultaneous changes in vegetation composition can mitigate disturbance impacts, but also influence climate regulation directly (e.g., via albedo changes). As a result of a number of interactive drivers (changes in climate, vegetation, and disturbance) and their simultaneous effects on climate-relevant processes (carbon exchange, albedo, latent heat flux) the future climate regulation function of forests remains highly uncertain. Here we address these complex interactions to assess the effect of future forest dynamics on the climate system. Our specific objectives were (1) to investigate the long-term interactions between changing vegetation composition and disturbance regimes under climate change, (2) to quantify the response of climate regulation to changes in forest dynamics, and (3) to identify the main drivers of the future influence of forests on the climate system. We investigated these issues using the individual-based forest landscape and disturbance model (iLand). Simulations were run over 200 yr for Kalkalpen National Park (Austria), assuming different future climate projections, and incorporating dynamically responding wind and bark beetle disturbances. To consistently assess the net effect on climate the simulated responses of carbon exchange, albedo, and latent heat flux were expressed as contributions to radiative forcing. We found that climate change increased disturbances (+27.7% over 200 yr) and specifically bark beetle activity during the 21st century. However, negative feedbacks from a simultaneously changing tree species composition (+28.0% broadleaved species) decreased disturbance activity in the long run (-10.1%), mainly by reducing the host trees available for bark beetles. Climate change and the resulting future forest dynamics significantly reduced the climate regulation function of the landscape, increasing radiative forcing by up to +10.2% on average over 200 yr. Overall, radiative forcing was most strongly driven by carbon exchange. We conclude that future changes in forest dynamics can cause amplifying climate feedbacks from temperate forest ecosystems.

Suggestions for Forest Conservation Policy under Climate Change

NASA Astrophysics Data System (ADS)

Choe, H.; Thorne, J. H.; Lee, D. K.; Seo, C.

2015-12-01

Climate change and the destruction of natural habitats by land-use change are two main factors in decreasing terrestrial biodiversity. Studying land-use and climate change and their impact under different scenarios can help suggest policy directions for future events. This study explores the spatial results of different land use and climate models on the extent of species rich areas in South Korea. We built land use models of forest conversion and created four 2050 scenarios: (1) a loss trend following current levels, resulting in 15.5% lost; (2) similar loss, but with forest conservation in areas with suitable future climates; (3) a reduction of forest loss by 50%; and (4) a combination of preservation of forest climate refugia and overall reduction of loss by 50%. Forest climate refugia were identified through the use of species distribution models run on 1,031 forest plant species to project current and 2050 distributions. We calculated change in species richness under four climate projections, permitting an assessment of forest refugia zones. We then crossed the four land use models with the climate-driven change in species richness. Forest areas predominantly convert to agricultural areas, while climate-suitable extents for forest plants decline and move northward, especially to higher elevations. Scenario 2, that has the higher level of deforestation but protects future species rich areas, conserves nearly as much future biodiversity as scenario 3, which reduced deforestation rates by 50%. This points to the importance of including biogeographic climate dynamics in forest policy. Scenario 4 was the most effective at conserving forest biodiversity. We suggest conserving forest areas with suitable climates for biodiversity conservation and the establishment of monoculture plantations targeted to areas where species richness will decline based on our results.

Simulating Climate Change in Ireland

NASA Astrophysics Data System (ADS)

Nolan, P.; Lynch, P.

2012-04-01

At the Meteorology & Climate Centre at University College Dublin, we are using the CLM-Community's COSMO-CLM Regional Climate Model (RCM) and the WRF RCM (developed at NCAR) to simulate the climate of Ireland at high spatial resolution. To address the issue of model uncertainty, a Multi-Model Ensemble (MME) approach is used. The ensemble method uses different RCMs, driven by several Global Climate Models (GCMs), to simulate climate change. Through the MME approach, the uncertainty in the RCM projections is quantified, enabling us to estimate the probability density function of predicted changes, and providing a measure of confidence in the predictions. The RCMs were validated by performing a 20-year simulation of the Irish climate (1981-2000), driven by ECMWF ERA-40 global re-analysis data, and comparing the output to observations. Results confirm that the output of the RCMs exhibit reasonable and realistic features as documented in the historical data record. Projections for the future Irish climate were generated by downscaling the Max Planck Institute's ECHAM5 GCM, the UK Met Office HadGEM2-ES GCM and the CGCM3.1 GCM from the Canadian Centre for Climate Modelling. Simulations were run for a reference period 1961-2000 and future period 2021-2060. The future climate was simulated using the A1B, A2, B1, RCP 4.5 & RCP 8.5 greenhouse gas emission scenarios. Results for the downscaled simulations show a substantial overall increase in precipitation and wind speed for the future winter months and a decrease during the summer months. The predicted annual change in temperature is approximately 1.1°C over Ireland. To date, all RCM projections are in general agreement, thus increasing our confidence in the robustness of the results.

Urban amplification of the global warming in Moscow megacity

NASA Astrophysics Data System (ADS)

Kislov, Alexander; Konstantinov, Pavel; Varentsov, Mikhail; Samsonov, Timofey; Gorlach, Irina; Trusilova, Kristina

2015-04-01

Climate changes in the large cities are very important and requires better understanding. The focus of this paper is climate change of the Moscow megacity. Its urban features strongly influence the atmospheric boundary layer above the Moscow agglomeration area and determine the microclimatic features of the local environment, such as urban heat island (UHI). Available meteorological observations within the Moscow urban area and surrounding territory allow us to assess the natural climate variations and human-induced climate warming separately. To obtain more precisely viewing on the UHI structure we have included into the analysis the satellite data (Meteosat-10), providing temperature and humidity profiles with high resolution. To investigate the mechanism of the urban amplification we realized the regional climate model COSMO-CLM+TEB. Apart from detailed climate research the model runs will be planned for climate projecting of Moscow agglomeration area. Climate change differences between urban and rural areas are determined by changes of the shape of the UHI and their relationships with changes of building height and density. Therefore, the urban module of COSMO-CLM+TEB model is fed by information from special GIS database contenting both geometric characteristics of the urban canyons and other characteristics of the urban surface. The sources of information were maps belonging to the OpenStreetMap, and digital elevation models SRTM90 and ASTER GDEM v.2 as well. The multiscale GIS database allows us to generate such kind of information with different spatial resolution (200, 500 and 1000 meters).

Scenario Analysis With Economic-Energy Systems Models Coupled to Simple Climate Models

NASA Astrophysics Data System (ADS)

Hanson, D. A.; Kotamarthi, V. R.; Foster, I. T.; Franklin, M.; Zhu, E.; Patel, D. M.

2008-12-01

Here, we compare two scenarios based on Stanford University's Energy Modeling Forum Study 22 on global cooperative and non-cooperative climate policies. In the former, efficient transition paths are implemented including technology Research and Development effort, energy conservation programs, and price signals for greenhouse gas (GHG) emissions. In the non-cooperative case, some countries try to relax their regulations and be free riders. Total emissions and costs are higher in the non-cooperative scenario. The simulations, including climate impacts, run to the year 2100. We use the Argonne AMIGA-MARS economic-energy systems model, the Texas AM University's Forest and Agricultural Sector Optimization Model (FASOM), and the University of Illinois's Integrated Science Assessment Model (ISAM), with offline coupling between the FASOM and AMIGA-MARS and an online coupling between AMIGA-MARS and ISAM. This set of models captures the interaction of terrestrial systems, land use, crops and forests, climate change, human activity, and energy systems. Our scenario simulations represent dynamic paths over which all the climate, terrestrial, economic, and energy technology equations are solved simultaneously Special attention is paid to biofuels and how they interact with conventional gasoline/diesel fuel markets. Possible low-carbon penetration paths are based on estimated costs for new technologies, including cellulosic biomass, coal-to-liquids, plug-in electric vehicles, solar and nuclear energy. We explicitly explore key uncertainties that affect mitigation and adaptation scenarios.

Applying an economical scale-aware PDF-based turbulence closure model in NOAA NCEP GCMs

NASA Astrophysics Data System (ADS)

Belochitski, A.; Krueger, S. K.; Moorthi, S.; Bogenschutz, P.; Pincus, R.

2016-12-01

A novel unified representation of sub-grid scale (SGS) turbulence, cloudiness, and shallow convection is being implemented into the NOAA NCEP Global Forecasting System (GFS) general circulation model. The approach, known as Simplified High Order Closure (SHOC), is based on predicting a joint PDF of SGS thermodynamic variables and vertical velocity and using it to diagnose turbulent diffusion coefficients, SGS fluxes, condensation and cloudiness. Unlike other similar methods, only one new prognostic variable, turbulent kinetic energy (TKE), needs to be intoduced, making the technique computationally efficient.SHOC is now incorporated into a version of GFS, as well as into the next generation of the NCEP global model - NOAA Environmental Modeling System (NEMS). Turbulent diffusion coefficients computed by SHOC are now used in place of those produced by the boundary layer turbulence and shallow convection parameterizations. Large scale microphysics scheme is no longer used to calculate cloud fraction or the large-scale condensation/deposition. Instead, SHOC provides these variables. Radiative transfer parameterization uses cloudiness computed by SHOC.Outstanding problems include high level tropical cloud fraction being too high in SHOC runs, possibly related to the interaction of SHOC with condensate detrained from deep convection.Future work will consist of evaluating model performance and tuning the physics if necessary, by performing medium-range NWP forecasts with prescribed initial conditions, and AMIP-type climate tests with prescribed SSTs. Depending on the results, the model will be tuned or parameterizations modified. Next, SHOC will be implemented in the NCEP CFS, and tuned and evaluated for climate applications - seasonal prediction and long coupled climate runs. Impact of new physics on ENSO, MJO, ISO, monsoon variability, etc will be examined.

Climate Modeling: Ocean Cavities below Ice Shelves

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

Petersen, Mark Roger

The Accelerated Climate Model for Energy (ACME), a new initiative by the U.S. Department of Energy, includes unstructured-mesh ocean, land-ice, and sea-ice components using the Model for Prediction Across Scales (MPAS) framework. The ability to run coupled high-resolution global simulations efficiently on large, high-performance computers is a priority for ACME. Sub-ice shelf ocean cavities are a significant new capability in ACME, and will be used to better understand how changing ocean temperature and currents influence glacial melting and retreat. These simulations take advantage of the horizontal variable-resolution mesh and adaptive vertical coordinate in MPAS-Ocean, in order to place high resolutionmore » below ice shelves and near grounding lines.« less

Modeling ecohydrologic processes at Hubbard Brook: Initial results for Watershed 6 stream discharge and chemistry

EPA Science Inventory

The Hubbard Brook Long Term Ecological Research site has produced some of the most extensive and long-running databases on the hydrology, biology and chemistry of forest ecosystem responses to climate and forest harvest. We used these long-term databases to calibrate and apply G...

Promoting Climate Literacy and Conceptual Understanding among In-service Secondary Science Teachers requires an Epistemological Perspective

NASA Astrophysics Data System (ADS)

Bhattacharya, D.; Forbes, C.; Roehrig, G.; Chandler, M. A.

2017-12-01

Promoting climate literacy among in-service science teachers necessitates an understanding of fundamental concepts about the Earth's climate System (USGCRP, 2009). Very few teachers report having any formal instruction in climate science (Plutzer et al., 2016), therefore, rather simple conceptions of climate systems and their variability exist, which has implications for students' science learning (Francies et al., 1993; Libarkin, 2005; Rebich, 2005). This study uses the inferences from a NASA Innovations in Climate Education (NICE) teacher professional development program (CYCLES) to establish the necessity for developing an epistemological perspective among teachers. In CYCLES, 19 middle and high school (male=8, female=11) teachers were assessed for their understanding of global climate change (GCC). A qualitative analysis of their concept maps and an alignment of their conceptions with the Essential Principles of Climate Literacy (NOAA, 2009) demonstrated that participants emphasized on EPCL 1, 3, 6, 7 focusing on the Earth system, atmospheric, social and ecological impacts of GCC. However, EPCL 4 (variability in climate) and 5 (data-based observations and modeling) were least represented and emphasized upon. Thus, participants' descriptions about global climatic patterns were often factual rather than incorporating causation (why the temperatures are increasing) and/or correlation (describing what other factors might influence global temperatures). Therefore, engaging with epistemic dimensions of climate science to understand the processes, tools, and norms through which climate scientists study the Earth's climate system (Huxter et al., 2013) is critical for developing an in-depth conceptual understanding of climate. CLiMES (Climate Modeling and Epistemology of Science), a NSF initiative proposes to use EzGCM (EzGlobal Climate Model) to engage students and teachers in designing and running simulations, performing data processing activities, and analyzing computational models to develop their own evidence-based claims about the Earth's climate system. We describe how epistemological investigations can be conducted using EzGCM to bring the scientific process and authentic climate science practice to middle and high school classrooms.

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

Agents, Bayes, and Climatic Risks - a modular modelling approach

NASA Astrophysics Data System (ADS)

Haas, A.; Jaeger, C.

2005-08-01

When insurance firms, energy companies, governments, NGOs, and other agents strive to manage climatic risks, it is by no way clear what the aggregate outcome should and will be. As a framework for investigating this subject, we present the LAGOM model family. It is based on modules depicting learning social agents. For managing climate risks, our agents use second order probabilities and update them by means of a Bayesian mechanism while differing in priors and risk aversion. The interactions between these modules and the aggregate outcomes of their actions are implemented using further modules. The software system is implemented as a series of parallel processes using the CIAMn approach. It is possible to couple modules irrespective of the language they are written in, the operating system under which they are run, and the physical location of the machine.

Climate model uncertainty in impact assessments for agriculture: A multi-ensemble case study on maize in sub-Saharan Africa

NASA Astrophysics Data System (ADS)

Dale, Amy; Fant, Charles; Strzepek, Kenneth; Lickley, Megan; Solomon, Susan

2017-03-01

We present maize production in sub-Saharan Africa as a case study in the exploration of how uncertainties in global climate change, as reflected in projections from a range of climate model ensembles, influence climate impact assessments for agriculture. The crop model AquaCrop-OS (Food and Agriculture Organization of the United Nations) was modified to run on a 2° × 2° grid and coupled to 122 climate model projections from multi-model ensembles for three emission scenarios (Coupled Model Intercomparison Project Phase 3 [CMIP3] SRES A1B and CMIP5 Representative Concentration Pathway [RCP] scenarios 4.5 and 8.5) as well as two "within-model" ensembles (NCAR CCSM3 and ECHAM5/MPI-OM) designed to capture internal variability (i.e., uncertainty due to chaos in the climate system). In spite of high uncertainty, most notably in the high-producing semi-arid zones, we observed robust regional and sub-regional trends across all ensembles. In agreement with previous work, we project widespread yield losses in the Sahel region and Southern Africa, resilience in Central Africa, and sub-regional increases in East Africa and at the southern tip of the continent. Spatial patterns of yield losses corresponded with spatial patterns of aridity increases, which were explicitly evaluated. Internal variability was a major source of uncertainty in both within-model and between-model ensembles and explained the majority of the spatial distribution of uncertainty in yield projections. Projected climate change impacts on maize production in different regions and nations ranged from near-zero or positive (upper quartile estimates) to substantially negative (lower quartile estimates), highlighting a need for risk management strategies that are adaptive and robust to uncertainty.

Evaluation and projected changes of precipitation statistics in convection-permitting WRF climate simulations over Central Europe

NASA Astrophysics Data System (ADS)

Knist, Sebastian; Goergen, Klaus; Simmer, Clemens

2018-02-01

We perform simulations with the WRF regional climate model at 12 and 3 km grid resolution for the current and future climates over Central Europe and evaluate their added value with a focus on the daily cycle and frequency distribution of rainfall and the relation between extreme precipitation and air temperature. First, a 9 year period of ERA-Interim driven simulations is evaluated against observations; then global climate model runs (MPI-ESM-LR RCP4.5 scenario) are downscaled and analyzed for three 12-year periods: a control, a mid-of-century and an end-of-century projection. The higher resolution simulations reproduce both the diurnal cycle and the hourly intensity distribution of precipitation more realistically compared to the 12 km simulation. Moreover, the observed increase of the temperature-extreme precipitation scaling from the Clausius-Clapeyron (C-C) scaling rate of 7% K-1 to a super-adiabatic scaling rate for temperatures above 11 °C is reproduced only by the 3 km simulation. The drop of the scaling rates at high temperatures under moisture limited conditions differs between sub-regions. For both future scenario time spans both simulations suggest a slight decrease in mean summer precipitation and an increase in hourly heavy and extreme precipitation. This increase is stronger in the 3 km runs. Temperature-extreme precipitation scaling curves in the future climate are projected to shift along the 7% K-1 trajectory to higher peak extreme precipitation values at higher temperatures. The curves keep their typical shape of C-C scaling followed by super-adiabatic scaling and a drop-off at higher temperatures due to moisture limitation.

Climate Change: Integrating Science and Economics

NASA Astrophysics Data System (ADS)

Prinn, R. G.

2008-12-01

The world is facing an ever-growing conflict between environment and development. Climate change is a century-scale threat requiring a century-long effort in science, technology and policy analysis, and institutions that can sustain this effort over generations. To inform policy development and implementation there is urgent need for better integration of the diverse components of the problem. Motivated by this challenge, we have developed the Integrated Global System Model (IGSM) at MIT. It comprises coupled sub- models of economic development, atmospheric chemistry, climate dynamics and ecosystems. The results of a recent uncertainty analysis involving hundreds of runs of the IGSM imply that, without mitigation policies, the global average surface temperature may rise much faster than previously estimated. Polar temperatures are projected to rise even faster than the average rate with obvious great risks for high latitude ecosystems and ice sheets at the high end of this range. Analysis of policies for climate mitigation, show that the greatest effect of these policies is to lower the probability of extreme changes as opposed to lowering the medians. Faced with the above estimated impacts, the long lifetimes of most greenhouse gases in the atmosphere, the long delay in ultimate warming due to ocean heat uptake, and the capital-intensive global energy infrastructure, the case is strong for concerted action now. Results of runs of the IGSM indicate the need for transformation of the global energy industry on a very large scale to mitigate climate change. Carbon sequestration, renewable energy sources, and nuclear present new economic, technological, and environmental challenges when implemented at the needed scales. Economic analyses using the IGSM indicate that global implementation of efficient policies could allow the needed transformations at bearable costs.

High Resolution Modeling of Hurricanes in a Climate Context

NASA Astrophysics Data System (ADS)

Knutson, T. R.

2007-12-01

Modeling of tropical cyclone activity in a climate context initially focused on simulation of relatively weak tropical storm-like disturbances as resolved by coarse grid (200 km) global models. As computing power has increased, multi-year simulations with global models of grid spacing 20-30 km have become feasible. Increased resolution also allowed for simulation storms of increasing intensity, and some global models generate storms of hurricane strength, depending on their resolution and other factors, although detailed hurricane structure is not simulated realistically. Results from some recent high resolution global model studies are reviewed. An alternative for hurricane simulation is regional downscaling. An early approach was to embed an operational (GFDL) hurricane prediction model within a global model solution, either for 5-day case studies of particular model storm cases, or for "idealized experiments" where an initial vortex is inserted into an idealized environments derived from global model statistics. Using this approach, hurricanes up to category five intensity can be simulated, owing to the model's relatively high resolution (9 km grid) and refined physics. Variants on this approach have been used to provide modeling support for theoretical predictions that greenhouse warming will increase the maximum intensities of hurricanes. These modeling studies also simulate increased hurricane rainfall rates in a warmer climate. The studies do not address hurricane frequency issues, and vertical shear is neglected in the idealized studies. A recent development is the use of regional model dynamical downscaling for extended (e.g., season-length) integrations of hurricane activity. In a study for the Atlantic basin, a non-hydrostatic model with grid spacing of 18km is run without convective parameterization, but with internal spectral nudging toward observed large-scale (basin wavenumbers 0-2) atmospheric conditions from reanalyses. Using this approach, our model reproduces the observed increase in Atlantic hurricane activity (numbers, Accumulated Cyclone Energy (ACE), Power Dissipation Index (PDI), etc.) over the period 1980-2006 fairly realistically, and also simulates ENSO-related interannual variations in hurricane counts. Annual simulated hurricane counts from a two-member ensemble correlate with observed counts at r=0.86. However, the model does not simulate hurricanes as intense as those observed, with minimum central pressures of 937 hPa (category 4) and maximum surface winds of 47 m/s (category 2) being the most intense simulated so far in these experiments. To explore possible impacts of future climate warming on Atlantic hurricane activity, we are re-running the 1980- 2006 seasons, keeping the interannual to multidecadal variations unchanged, but altering the August-October mean climate according to changes simulated by an 18-member ensemble of AR4 climate models (years 2080- 2099, A1B emission scenario). The warmer climate state features higher Atlantic SSTs, and also increased vertical wind shear across the Caribbean (Vecchi and Soden, GRL 2007). A key assumption of this approach is that the 18-model ensemble-mean climate change is the best available projection of future climate change in the Atlantic. Some of the 18 global models show little increase in wind shear, or even a decrease, and thus there will be considerable uncertainty associated with the hurricane frequency results, which will require further exploration. Results from our simulations will be presented at the meeting.

Climate-informed stochastic hydrological modeling: Incorporating decadal-scale variability using paleoclimate data

NASA Astrophysics Data System (ADS)

Henley, B. J.; Thyer, M. A.; Kuczera, G. A.

2012-12-01

A hierarchical framework for incorporating modes of climate variability into stochastic simulations of hydrological data is developed, termed the climate-informed multi-time scale stochastic (CIMSS) framework. To characterize long-term variability for the first level of the hierarchy, paleoclimate and instrumental data describing the Interdecadal Pacific Oscillation (IPO) and the Pacific Decadal Oscillation (PDO) are analyzed. A new paleo IPO-PDO time series dating back 440 yrs is produced, combining seven IPO-PDO paleo sources using an objective smoothing procedure to fit low-pass filters to individual records. The paleo data analysis indicates that wet/dry IPO-PDO states have a broad range of run-lengths, with 90% between 3 and 33 yr and a mean of 15 yr. Model selection techniques were used to determine a suitable stochastic model to simulate these run-lengths. The Markov chain model, previously used to simulate oscillating wet/dry climate states, was found to underestimate the probability of wet/dry periods >5 yr, and was rejected in favor of a gamma distribution. For the second level of the hierarchy, a seasonal rainfall model is conditioned on the simulated IPO-PDO state. Application to two high-quality rainfall sites close to water supply reservoirs found that mean seasonal rainfall in the IPO-PDO dry state was 15%-28% lower than the wet state. The model was able to replicate observed statistics such as seasonal and multi-year accumulated rainfall distributions and interannual autocorrelations for the case study sites. In comparison, an annual lag-one autoregressive AR(1) model was unable to adequately capture the observed rainfall distribution within separate IPO-PDO states. Furthermore, analysis of the impact of the CIMSS framework on drought risk analysis found that short-term drought risks conditional on IPO/PDO state were considerably higher than the traditional AR(1) model.hort-term conditional water supply drought risks for the CIMSS and AR(1) models for the dry IPO-PDO scenario with a range of initial storage levels expressed as a proportion of the annual demand (yield).

Calculating distributed glacier mass balance for the Swiss Alps from RCM output: Development and testing of downscaling and validation methods

NASA Astrophysics Data System (ADS)

Machguth, H.; Paul, F.; Kotlarski, S.; Hoelzle, M.

2009-04-01

Climate model output has been applied in several studies on glacier mass balance calculation. Hereby, computation of mass balance has mostly been performed at the native resolution of the climate model output or data from individual cells were selected and statistically downscaled. Little attention has been given to the issue of downscaling entire fields of climate model output to a resolution fine enough to compute glacier mass balance in rugged high-mountain terrain. In this study we explore the use of gridded output from a regional climate model (RCM) to drive a distributed mass balance model for the perimeter of the Swiss Alps and the time frame 1979-2003. Our focus lies on the development and testing of downscaling and validation methods. The mass balance model runs at daily steps and 100 m spatial resolution while the RCM REMO provides daily grids (approx. 18 km resolution) of dynamically downscaled re-analysis data. Interpolation techniques and sub-grid parametrizations are combined to bridge the gap in spatial resolution and to obtain daily input fields of air temperature, global radiation and precipitation. The meteorological input fields are compared to measurements at 14 high-elevation weather stations. Computed mass balances are compared to various sets of direct measurements, including stake readings and mass balances for entire glaciers. The validation procedure is performed separately for annual, winter and summer balances. Time series of mass balances for entire glaciers obtained from the model run agree well with observed time series. On the one hand, summer melt measured at stakes on several glaciers is well reproduced by the model, on the other hand, observed accumulation is either over- or underestimated. It is shown that these shifts are systematic and correlated to regional biases in the meteorological input fields. We conclude that the gap in spatial resolution is not a large drawback, while biases in RCM output are a major limitation to model performance. The development and testing of methods to reduce regionally variable biases in entire fields of RCM output should be a focus of pursuing studies.

Large ensemble modeling of last deglacial retreat of the West Antarctic Ice Sheet: comparison of simple and advanced statistical techniques

NASA Astrophysics Data System (ADS)

Pollard, D.; Chang, W.; Haran, M.; Applegate, P.; DeConto, R.

2015-11-01

A 3-D hybrid ice-sheet model is applied to the last deglacial retreat of the West Antarctic Ice Sheet over the last ~ 20 000 years. A large ensemble of 625 model runs is used to calibrate the model to modern and geologic data, including reconstructed grounding lines, relative sea-level records, elevation-age data and uplift rates, with an aggregate score computed for each run that measures overall model-data misfit. Two types of statistical methods are used to analyze the large-ensemble results: simple averaging weighted by the aggregate score, and more advanced Bayesian techniques involving Gaussian process-based emulation and calibration, and Markov chain Monte Carlo. Results for best-fit parameter ranges and envelopes of equivalent sea-level rise with the simple averaging method agree quite well with the more advanced techniques, but only for a large ensemble with full factorial parameter sampling. Best-fit parameter ranges confirm earlier values expected from prior model tuning, including large basal sliding coefficients on modern ocean beds. Each run is extended 5000 years into the "future" with idealized ramped climate warming. In the majority of runs with reasonable scores, this produces grounding-line retreat deep into the West Antarctic interior, and the analysis provides sea-level-rise envelopes with well defined parametric uncertainty bounds.

GEOSS AIP-2 Climate Change and Biodiversity Use Scenarios: Interoperability Infrastructures

NASA Astrophysics Data System (ADS)

Nativi, Stefano; Santoro, Mattia

2010-05-01

In the last years, scientific community is producing great efforts in order to study the effects of climate change on life on Earth. In this general framework, a key role is played by the impact of climate change on biodiversity. To assess this, several use scenarios require the modeling of climatological change impact on the regional distribution of biodiversity species. Designing and developing interoperability infrastructures which enable scientists to search, discover, access and use multi-disciplinary resources (i.e. datasets, services, models, etc.) is currently one of the main research fields for the Earth and Space Science Informatics. This presentation introduces and discusses an interoperability infrastructure which implements the discovery, access, and chaining of loosely-coupled resources in the climatology and biodiversity domains. This allows to set up and run forecast and processing models. The presented framework was successfully developed and experimented in the context of GEOSS AIP-2 (Global Earth Observation System of Systems, Architecture Implementation Pilot- Phase 2) Climate Change & Biodiversity thematic Working Group. This interoperability infrastructure is comprised of the following main components and services: a)GEO Portal: through this component end user is able to search, find and access the needed services for the scenario execution; b)Graphical User Interface (GUI): this component provides user interaction functionalities. It controls the workflow manager to perform the required operations for the scenario implementation; c)Use Scenario controller: this component acts as a workflow controller implementing the scenario business process -i.e. a typical climate change & biodiversity projection scenario; d)Service Broker implementing Mediation Services: this component realizes a distributed catalogue which federates several discovery and access components (exposing them through a unique CSW standard interface). Federated components publish climate, environmental and biodiversity datasets; e)Ecological Niche Model Server: this component is able to run one or more Ecological Niche Models (ENM) on selected biodiversity and climate datasets; f)Data Access Transaction server: this component publishes the model outputs. This framework was assessed in two use scenarios of GEOSS AIP-2 Climate Change and Biodiversity WG. Both scenarios concern the prediction of species distributions driven by climatological change forecasts. The first scenario dealt with the Pikas specie regional distribution in the Great Basin area (North America). While, the second one concerned the modeling of the Arctic Food Chain species in the North Pole area -the relationships between different environmental parameters and Polar Bears distribution was analyzed. The scientific patronage was provided by the University of Colorado and the University of Alaska, respectively. Results are published in the GEOSS AIP-2 web site: http://www.ogcnetwork.net/AIP2develop.

Downscaling of RCM outputs for representative catchments in the Mediterranean region, for the 1951-2100 time-frame

NASA Astrophysics Data System (ADS)

Deidda, Roberto; Marrocu, Marino; Pusceddu, Gabriella; Langousis, Andreas; Mascaro, Giuseppe; Caroletti, Giulio

2013-04-01

Within the activities of the EU FP7 CLIMB project (www.climb-fp7.eu), we developed downscaling procedures to reliably assess climate forcing at hydrologically relevant scales, and applied them to six representative hydrological basins located in the Mediterranean region: Riu Mannu and Noce in Italy, Chiba in Tunisia, Kocaeli in Turkey, Thau in France, and Gaza in Palestine. As a first step towards this aim, we used daily precipitation and temperature data from the gridded E-OBS project (www.ecad.eu/dailydata), as reference fields, to rank 14 Regional Climate Model (RCM) outputs from the ENSEMBLES project (http://ensembles-eu.metoffice.com). The four best performing model outputs were selected, with the additional constraint of maintaining 2 outputs obtained from running different RCMs driven by the same GCM, and 2 runs from the same RCM driven by different GCMs. For these four RCM-GCM model combinations, a set of downscaling techniques were developed and applied, for the period 1951-2100, to variables used in hydrological modeling (i.e. precipitation; mean, maximum and minimum daily temperatures; direct solar radiation, relative humidity, magnitude and direction of surface winds). The quality of the final products is discussed, together with the results obtained after applying a bias reduction procedure to daily temperature and precipitation fields.

Reconstructing Holocene climate using a climate model: Model strategy and preliminary results

NASA Astrophysics Data System (ADS)

Haberkorn, K.; Blender, R.; Lunkeit, F.; Fraedrich, K.

2009-04-01

An Earth system model of intermediate complexity (Planet Simulator; PlaSim) is used to reconstruct Holocene climate based on proxy data. The Planet Simulator is a user friendly general circulation model (GCM) suitable for palaeoclimate research. Its easy handling and the modular structure allow for fast and problem dependent simulations. The spectral model is based on the moist primitive equations conserving momentum, mass, energy and moisture. Besides the atmospheric part, a mixed layer-ocean with sea ice and a land surface with biosphere are included. The present-day climate of PlaSim, based on an AMIP II control-run (T21/10L resolution), shows reasonable agreement with ERA-40 reanalysis data. Combining PlaSim with a socio-technological model (GLUES; DFG priority project INTERDYNAMIK) provides improved knowledge on the shift from hunting-gathering to agropastoral subsistence societies. This is achieved by a data assimilation approach, incorporating proxy time series into PlaSim to initialize palaeoclimate simulations during the Holocene. For this, the following strategy is applied: The sensitivities of the terrestrial PlaSim climate are determined with respect to sea surface temperature (SST) anomalies. Here, the focus is the impact of regionally varying SST both in the tropics and the Northern Hemisphere mid-latitudes. The inverse of these sensitivities is used to determine the SST conditions necessary for the nudging of land and coastal proxy climates. Preliminary results indicate the potential, the uncertainty and the limitations of the method.

Moisture balance over the Iberian Peninsula computed using a high resolution regional climate model. The impact of 3DVAR data assimilation.

NASA Astrophysics Data System (ADS)

González-Rojí, Santos J.; Sáenz, Jon; Ibarra-Berastegi, Gabriel

2016-04-01

A numerical downscaling exercise over the Iberian Peninsula has been run nesting the WRF model inside ERA Interim. The Iberian Peninsula has been covered by a 15km x 15 km grid with 51 vertical levels. Two model configurations have been tested in two experiments spanning the period 2010-2014 after a one year spin-up (2009). In both cases, the model uses high resolution daily-varying SST fields and the Noah land surface model. In the first experiment (N), after the model is initialised, boundary conditions drive the model, as usual in numerical downscaling experiments. The second experiment (D) is configured the same way as the N case, but 3DVAR data assimilation is run every six hours (00Z, 06Z, 12Z and 18Z) using observations obtained from the PREPBUFR dataset (NCEP ADP Global Upper Air and Surface Weather Observations) using a 120' window around analysis times. For the data assimilation experiment (D), seasonally (monthly) varying background error covariance matrices have been prepared according to the parameterisations used and the mesoscale model domain. For both N and D runs, the moisture balance of the model runs has been evaluated over the Iberian Peninsula, both internally according to the model results (moisture balance in the model) and also in terms of the observed moisture fields from observational datasets (particularly precipitable water and precipitation from observations). Verification has been performed both at the daily and monthly time scales. The verification has also been performed for ERA Interim, the driving coarse-scale dataset used to drive the regional model too. Results show that the leading terms that must be considered over the area are the tendency in the precipitable water column, the divergence of moisture flux, evaporation (computed from latent heat flux at the surface) and precipitation. In the case of ERA Interim, the divergence of Qc is also relevant, although still a minor player in the moisture balance. Both mesoscale model runs are more effective at closing the moisture balance over the whole Iberian Peninsula than ERA Interim. The N experiment (no data assimilation) shows a better closure than the D case, as could be expected from the lack of analysis increments in it. This result is robust both at the daily and monthly time scales. Both ERA Interim and the D experiment produce a negative residual in the balance equation (compatible with excess evaporation or increased convergence of moisture over the Iberian Peninsula). This is a result of the data assimilation process in the D dataset, since in the N experiment the residual is mainly positive. The seasonal cycle of evaporation is much closer in the D experiment to the one in ERA Interim than in the N case, with a higher evaporation during summer months. However, both regional climate model runs show a lower evaporation rate than ERA Interim, particularly during summer months.

The Arctic Predictability and Prediction on Seasonal-to-Interannual TimEscales (APPOSITE) project: a summary

NASA Astrophysics Data System (ADS)

Hawkins, Ed; Day, Jonny; Tietsche, Steffen

2016-04-01

Recent years have seen significant developments in seasonal-to-interannual timescale climate prediction capabilities. However, until recently the potential of such systems to predict Arctic climate had not been assessed. We describe a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Inter-annual TimEscales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable. In order to achieve this, a coordinated set of idealised initial-value predictability experiments, with seven general circulation models, was conducted. This was the first model intercomparison project designed to quantify the predictability of Arctic climate on seasonal to inter-annual timescales. Here we provide a summary and update of the project's results which include: (1) quantifying the predictability of Arctic climate, especially sea ice; (2) the state-dependence of this predictability, finding that extreme years are potentially more predictable than neutral years; (3) analysing a spring 'predictability barrier' to skillful forecasts; (4) initial sea ice thickness information provides much of the skill for summer forecasts; (5) quantifying the sources of error growth and uncertainty in Arctic predictions. The dataset is now publicly available.

Climate variability in China during the last millennium based on reconstructions and simulations

NASA Astrophysics Data System (ADS)

García-Bustamante, E.; Luterbacher, J.; Xoplaki, E.; Werner, J. P.; Jungclaus, J.; Zorita, E.; González-Rouco, J. F.; Fernández-Donado, L.; Hegerl, G.; Ge, Q.; Hao, Z.; Wagner, S.

2012-04-01

Multi-decadal to centennial climate variability in China during the last millennium is analysed. We compare the low frequency temperature and precipitation variations from proxy-based reconstructions and palaeo-simulations from climate models. Focusing on the regional responses to the global climate evolution is of high relevance due to the complexity of the interactions between physical mechanisms at different spatio-temporal scales and the potential severity of the derived multiple socio-economic impacts. China stands out as a particularly interesting region, not only due to its complex climatic features, ranging from the semiarid northwestern Tibetan Plateau to the tropical monsoon southeastern climates, but also because of its wealth of proxy data. However, comprehensive assessments of proxy- and model-based information about palaeo-climatic variations in China are, to our knowledge, still lacking. In addition, existing studies depict a general lack of agreement between reconstructions and model simulations with respect to the amplitude and/or occurrence of warmer/colder and wetter/drier periods during the last millennium and the magnitude of the 20th century warming trend. Furthermore, these works are mainly focused on eastern China regions that show a denser proxy data coverage. We investigate how last millennium palaeo-runs compare to independent evidences from an unusual large number of proxy reconstructions over the study area by employing state-of-the-art palaeo-simulations with multi-member ensembles from the CMIP5/PMIP3 project. This shapes an ideal frame for the evaluation of the uncertainties associated to internal and intermodel model variability. Preliminary results indicate that despite the strong regional and seasonal dependencies, temperature reconstructions in China evidence coherent variations among all regions at centennial scale, especially during the last 500 years. The spatial consistency of low frequency temperature changes is an interesting aspect and of relevance for the assessment of forced climatic responses in China. The comparison between reconstructions and simulations from climate models show that, apart from the 20th century warming trend, the variance of the reconstructed mean China temperature lies in the envelope (uncertainty range) spanned by the temperature simulations. The uncertainty arises from the internal (multi-member ensembles) and the inter-model variability. Centennial variations tend to be broadly synchronous in the reconstructions and the simulations. However, the simulations show a delay of the warm period 1000-1300 AD. This warm medieval period both in the simulations and the reconstructions is followed by cooling till 1800 AD. Based on the simulations, the recent warming is not unprecedented and is comparable to the medieval warming. Further steps of this study will address the individual contribution of anthropogenic and natural forcings on climate variability and change during the last millennium in China. We will make use of of models that provide runs including single forcings (fingerprints) for the attribution of climate variations from decadal to multi-centennial time scales. With this aim, we will implement statistical techniques for the detection of optimal signal-to-noise-ratio between external forcings and internal variability of reconstructed temperatures and precipitation. To apply these approaches the uncertainties associated with both reconstructions and simulations will be estimated. The latter will shed some light into the mechanisms behind current climate evolution and will help to constrain uncertainties in the sensitivity of model simulations to increasing CO2 scenarios of future climate change. This work will also contribute to the overall aims of the PAGES 2k initiative in Asia (http://www.pages.unibe.ch/workinggroups/2k-network)

Stable isotopes and Digital Elevation Models to study nutrient inputs in high-Arctic lakes

NASA Astrophysics Data System (ADS)

Calizza, Edoardo; Rossi, David; Costantini, Maria Letizia; Careddu, Giulio; Rossi, Loreto

2016-04-01

Ice cover, run-off from the watershed, aquatic and terrestrial primary productivity, guano deposition from birds are key factors controlling nutrient and organic matter inputs in high-Arctic lakes. All these factors are expected to be significantly affected by climate change. Quantifying these controls is a key baseline step to understand what combination of factors subtends the biological productivity in Arctic lakes and will drive their ecological response to environmental change. Basing on Digital Elevation Models, drainage maps, and C and N elemental content and stable isotope analysis in sediments, aquatic vegetation and a dominant macroinvertebrate species (Lepidurus arcticus Pallas 1973) belonging to Tvillingvatnet, Storvatnet and Kolhamna, three lakes located in North Spitsbergen (Svalbard), we propose an integrated approach for the analysis of (i) nutrient and organic matter inputs in lakes; (ii) the role of catchment hydro-geomorphology in determining inter-lake differences in the isotopic composition of sediments; (iii) effects of diverse nutrient inputs on the isotopic niche of Lepidurus arcticus. Given its high run-off and large catchment, organic deposits in Tvillingvatnet where dominated by terrestrial inputs, whereas inputs were mainly of aquatic origin in Storvatnet, a lowland lake with low potential run-off. In Kolhamna, organic deposits seem to be dominated by inputs from birds, which actually colonise the area. Isotopic signatures were similar between samples within each lake, representing precise tracers for studies on the effect of climate change on biogeochemical cycles in lakes. The isotopic niche of L. aricticus reflected differences in sediments between lakes, suggesting a bottom-up effect of hydro-geomorphology characterizing each lake on nutrients assimilated by this species. The presented approach proven to be an effective research pathway for the identification of factors subtending to nutrient and organic matter inputs and transfer within each water body, as well as for the modelling of expected changes in nutrient content associated to changes in isotopic composition of sediments. Key words: nitrogen; carbon, sediment; biogeochemical cycle; climate change; hydro-ecology; isotopic niche; Svalbard

Sensitivity of sea-level forecasting to the horizontal resolution and sea surface forcing for different configurations of an oceanographic model of the Adriatic Sea

NASA Astrophysics Data System (ADS)

Bressan, Lidia; Valentini, Andrea; Paccagnella, Tiziana; Montani, Andrea; Marsigli, Chiara; Stefania Tesini, Maria

2017-04-01

At the Hydro-meteo-climate service of the Regional environmental agency of Emilia-Romagna, Italy (Arpae-SIMC), the oceanographic numerical model AdriaROMS is used in the operational forecasting suite to compute sea level, temperature, salinity and 3-D current fields of the Adriatic Sea (northern Mediterranean Sea). In order to evaluate the performance of the sea-level forecast and to study different configurations of the ROMS model, two marine storms occurred on the Emilia Romagna coast during the winter 2015-2016 are investigated. The main focus of this study is to analyse the sensitivity of the model to the horizontal resolution and to the meteorological forcing. To this end, the model is run with two different configurations and with two horizontal grids at 1 and 2 km resolution. To study the influence of the meteorological forcing, the two storms have been reproduced by running ROMS in ensemble mode, forced by the 16-members of the meteorological ensemble COSMO-LEPS system. Possible optimizations of the model set-up are deduced by the comparison of the different run outputs.

Cloud-System Resolving Models: Status and Prospects

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Moncreiff, Mitch

2008-01-01

Cloud-system resolving models (CRM), which are based on the nonhydrostatic equations of motion and typically have a grid-spacing of about a kilometer, originated as cloud-process models in the 1970s. This paper reviews the status and prospects of CRMs across a wide range of issues, such as microphysics and precipitation; interaction between clouds and radiation; and the effects of boundary-layer and surface-processes on cloud systems. Since CRMs resolve organized convection, tropical waves and the large-scale circulation, there is the prospect for several advances in both basic knowledge of scale-interaction requisite to parameterizing mesoscale processes in climate models. In superparameterization, CRMs represent convection, explicitly replacing many of the assumptions necessary in contemporary parameterization. Global CRMs have been run on an experimental basis, giving prospect to a new generation of climate weather prediction in a decade, and climate models due course. CRMs play a major role in the retrieval of surface-rain and latent heating from satellite measurements. Finally, enormous wide dynamic ranges of CRM simulations present new challenges for model validation against observations.

Determining the impacts of climate change and catchment development on future water availability in Tasmania, Australia

NASA Astrophysics Data System (ADS)

Post, David

2010-05-01

In a water-scarce country such as Australia, detailed, accurate and reliable assessments of current and future water availability are essential in order to adequately manage the limited water resource. This presentation describes a recently completed study which provided an assessment of current water availability in Tasmania, Australia, and also determined how this water availability would be impacted by climate change and proposed catchment development by the year 2030. The Tasmania Sustainable Yields Project (http://www.csiro.au/partnerships/TasSY.html) assessed current water availability through the application of rainfall-runoff models, river models, and recharge and groundwater models. These were calibrated to streamflow records and parameterised using estimates of current groundwater and surface water extractions and use. Having derived a credible estimate of current water availability, the impacts of future climate change on water availability were determined through deriving changes in rainfall and potential evapotranspiration from 15 IPCC AR4 global climate models. These changes in rainfall were then dynamically downscaled using the CSIRO-CCAM model over the relatively small study area (50,000 square km). A future climate sequence was derived by modifying the historical 84-year climate sequence based on these changes in rainfall and potential evapotranspiration. This future climate sequence was then run through the rainfall-runoff, river, recharge and groundwater models to give an estimate of water availability under future climate. To estimate the impacts of future catchment development on water availability, the models were modified and re-run to reflect projected increases in development. Specifically, outputs from the rainfall-runoff and recharge models were reduced over areas of projected future plantation forestry. Conversely, groundwater recharge was increased over areas of new irrigated agriculture and new extractions of water for irrigation were implemented in the groundwater and river models. Results indicate that historical average water availability across the project area was 21,815 GL/year. Of this, 636 GL/year of surface water and 38 GL/year of groundwater are currently extracted for use. By 2030, rainfall is projected to decrease by an average of 3% over the project area. This decrease in rainfall and concurrent increase in potential evapotranspiration leads to a decrease in water availability of 5% by 2030. As a result of lower streamflows, under current cease-to-take rules, currently licensed extractions are projected to decrease by 3% (19 GL/year). This however is offset by an additional 120 GL/year of extractions for proposed new irrigated agriculture. These new extractions, along with the increase in commercial forest plantations lead to a reduction in total surface water of 1% in addition to the 5% reduction due to climate change. Results from this study are being used by the Tasmanian and Australian governments to guide the development of a sustainable irrigated agriculture industry in Tasmania. In part, this is necessary to offset the loss of irrigated agriculture from the southern Murray-Darling Basin where climate change induced reductions in rainfall are projected to be far worse.

Fast Atmosphere-Ocean Model Runs with Large Changes in CO2

NASA Technical Reports Server (NTRS)

Russell, Gary L.; Lacis, Andrew A.; Rind, David H.; Colose, Christopher; Opstbaum, Roger F.

2013-01-01

How does climate sensitivity vary with the magnitude of climate forcing? This question was investigated with the use of a modified coupled atmosphere-ocean model, whose stability was improved so that the model would accommodate large radiative forcings yet be fast enough to reach rapid equilibrium. Experiments were performed in which atmospheric CO2 was multiplied by powers of 2, from 1/64 to 256 times the 1950 value. From 8 to 32 times, the 1950 CO2, climate sensitivity for doubling CO2 reaches 8 C due to increases in water vapor absorption and cloud top height and to reductions in low level cloud cover. As CO2 amount increases further, sensitivity drops as cloud cover and planetary albedo stabilize. No water vapor-induced runaway greenhouse caused by increased CO2 was found for the range of CO2 examined. With CO2 at or below 1/8 of the 1950 value, runaway sea ice does occur as the planet cascades to a snowball Earth climate with fully ice covered oceans and global mean surface temperatures near 30 C.

Model-Based Development of Automotive Electronic Climate Control Software

NASA Astrophysics Data System (ADS)

Kakade, Rupesh; Murugesan, Mohan; Perugu, Bhupal; Nair, Mohanan

With increasing complexity of software in today's products, writing and maintaining thousands of lines of code is a tedious task. Instead, an alternative methodology must be employed. Model-based development is one candidate that offers several benefits and allows engineers to focus on the domain of their expertise than writing huge codes. In this paper, we discuss the application of model-based development to the electronic climate control software of vehicles. The back-to-back testing approach is presented that ensures flawless and smooth transition from legacy designs to the model-based development. Simulink report generator to create design documents from the models is presented along with its usage to run the simulation model and capture the results into the test report. Test automation using model-based development tool that support the use of unique set of test cases for several testing levels and the test procedure that is independent of software and hardware platform is also presented.

Projected climate and vegetation changes and potential biotic effects for Fort Benning, Georgia; Fort Hood, Texas; and Fort Irwin, California

USGS Publications Warehouse

Shafer, S.L.; Atkins, J.; Bancroft, B.A.; Bartlein, P.J.; Lawler, J.J.; Smith, B.; Wilsey, C.B.

2012-01-01

The responses of species and ecosystems to future climate changes will present challenges for conservation and natural resource managers attempting to maintain both species populations and essential habitat. This report describes projected future changes in climate and vegetation for three study areas surrounding the military installations of Fort Benning, Georgia, Fort Hood, Texas, and Fort Irwin, California. Projected climate changes are described for the time period 2070–2099 (30-year mean) as compared to 1961–1990 (30-year mean) for each study area using data simulated by the coupled atmosphere-ocean general circulation models CCSM3, CGCM3.1(T47), and UKMO-HadCM3, run under the B1, A1B, and A2 future greenhouse gas emissions scenarios. These climate data are used to simulate potential changes in important components of the vegetation for each study area using LPJ, a dynamic global vegetation model, and LPJ-GUESS, a dynamic vegetation model optimized for regional studies. The simulated vegetation results are compared with observed vegetation data for the study areas. Potential effects of the simulated future climate and vegetation changes for species and habitats of management concern are discussed in each study area, with a particular focus on federally listed threatened and endangered species.

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.

Indigenous Waters: Applying the SWAT Hydrological Model to the Lumbee River Watershed

NASA Astrophysics Data System (ADS)

Painter, J.; Singh, N.; Martin, K. L.; Vose, J. M.; Wear, D. N.; Emanuel, R. E.

2016-12-01

Hydrological modeling can reveal insight about how rainfall becomes streamflow in a watershed comprising heterogeneous soils, terrain and land cover. Modeling can also help disentangle predicted impacts of climate and land use change on hydrological processes. We applied a hydrological model to the Lumbee River watershed, also known as the Lumber River Watershed, in the coastal plain of North Carolina (USA) to better understand how streamflow may be impacted by predicted climate and land use change in the mid-21st century. The Lumbee River flows through a predominantly Native American community, which may be affected by changing water resources during this period. The long-term goal of our project is to predict the effects of climate and land use change on the Lumbee River watershed and on the Native community that relies upon the river. We applied the Soil & Water Assessment Tool for ArcGIS (ArcSWAT), which was calibrated to historical climate and USGS streamflow data during the late 20th century, and we determined frequency distributions for key model parameters that best predicted streamflow during this time period. After calibrating and validating the model during the historical period, we identified land use and climate projections to represent a range of future conditions in the watershed. Specifically, we selected downscaled climate forcing data from four general circulation models running the RCP8.5 scenario. We also selected land use projections from a cornerstone scenario of the USDA Forest Service's Southern Forest Futures Project. This presentation reports on our methods for propagating parameter and climatic uncertainty through model predictions, and it reports on spatial patterns of land use change predicted by the cornerstone scenario.

Paleoclimate reconstruction through Bayesian data assimilation

NASA Astrophysics Data System (ADS)

Fer, I.; Raiho, A.; Rollinson, C.; Dietze, M.

2017-12-01

Methods of paleoclimate reconstruction from plant-based proxy data rely on assumptions of static vegetation-climate link which is often established between modern climate and vegetation. This approach might result in biased climate constructions as it does not account for vegetation dynamics. Predictive tools such as process-based dynamic vegetation models (DVM) and their Bayesian inversion could be used to construct the link between plant-based proxy data and palaeoclimate more realistically. In other words, given the proxy data, it is possible to infer the climate that could result in that particular vegetation composition, by comparing the DVM outputs to the proxy data within a Bayesian state data assimilation framework. In this study, using fossil pollen data from five sites across the northern hardwood region of the US, we assimilate fractional composition and aboveground biomass into dynamic vegetation models, LINKAGES, LPJ-GUESS and ED2. To do this, starting from 4 Global Climate Model outputs, we generate an ensemble of downscaled meteorological drivers for the period 850-2015. Then, as a first pass, we weigh these ensembles based on their fidelity with independent paleoclimate proxies. Next, we run the models with this ensemble of drivers, and comparing the ensemble model output to the vegetation data, adjust the model state estimates towards the data. At each iteration, we also reweight the climate values that make the model and data consistent, producing a reconstructed climate time-series dataset. We validated the method using present-day datasets, as well as a synthetic dataset, and then assessed the consistency of results across ecosystem models. Our method allows the combination of multiple data types to reconstruct the paleoclimate, with associated uncertainty estimates, based on ecophysiological and ecological processes rather than phenomenological correlations with proxy data.

Export product diversification and the environmental Kuznets curve: evidence from Turkey.

PubMed

Gozgor, Giray; Can, Muhlis

2016-11-01

Countries try to stabilize the demand for energy on one hand and sustain economic growth on the other, but the worsening global warming and climate change problems have put pressure on them. This paper estimates the environmental Kuznets curve over the period 1971-2010 in Turkey both in the short and the long run. For this purpose, the unit root test with structural breaks and the cointegration analysis with multiple endogenous structural breaks are used. The effects of energy consumption and export product diversification on CO 2 emissions are also controlled in the dynamic empirical models. It is observed that the environmental Kuznets curve hypothesis is valid in Turkey in both the short run and the long run. The positive effect on energy consumption on CO 2 emissions is also obtained in the long run. In addition, it is found that a greater product diversification of exports yields higher CO 2 emissions in the long run. Inferences and policy implications are also discussed.

Program MAMO: Models for avian management optimization-user guide

USGS Publications Warehouse

Guillaumet, Alban; Paxton, Eben H.

2017-01-01

The following chapters describe the structure and code of MAMO, and walk the reader through running the different components of the program with sample data. This manual should be used alongside a computer running R, so that the reader can copy and paste code into R, observe the output, and follow along interactively. Taken together, chapters 2–4 will allow the user to replicate a simulation study investigating the consequences of climate change and two potential management actions on the population dynamics of a vulnerable and iconic Hawaiian forest bird, the ‘I‘iwi (Drepanis coccinea; hereafter IIWI).

Simulating the hydrological impacts of inter-annual and seasonal variability in land use land cover change on streamflow

NASA Astrophysics Data System (ADS)

Taxak, A. K.; Ojha, C. S. P.

2017-12-01

Land use and land cover (LULC) changes within a watershed are recognised as an important factor affecting hydrological processes and water resources. LULC changes continuously not only in long term but also on the inter-annual and season level. Changes in LULC affects the interception, storage and moisture. A widely used approach in rainfall-runoff modelling through Land surface models (LSM)/ hydrological models is to keep LULC same throughout the model running period. In long term simulations where land use change take place during the run period, using a single LULC does not represent a true picture of ground conditions could result in stationarity of model responses. The present work presents a case study in which changes in LULC are incorporated by using multiple LULC layers. LULC for the study period were created using imageries from Landsat series, Sentinal, EO-1 ALI. Distributed, physically based Variable Infiltration Capacity (VIC) model was modified to allow inclusion of LULC as a time varying variable just like climate. The Narayani basin was simulated with LULC, leaf area index (LAI), albedo and climate data for 1992-2015. The results showed that the model simulation with varied parametrization approach has a large improvement over the conventional fixed parametrization approach in terms of long-term water balance. The proposed modelling approach could improve hydrological modelling for applications like land cover change studies, water budget studies etc.

Modeling Climate Change Impacts on Landscape Evolution, Fire, and Hydrology

NASA Astrophysics Data System (ADS)

Sheppard, B. S.; O Connor, C.; Falk, D. A.; Garfin, G. M.

2015-12-01

Landscape disturbances such as wildfire interact with climate variability to influence hydrologic regimes. We coupled landscape, fire, and hydrologic models and forced them using projected climate to demonstrate climate change impacts anticipated at Fort Huachuca in southeastern Arizona, USA. The US Department of Defense (DoD) recognizes climate change as a trend that has implications for military installations, national security and global instability. The goal of this DoD Strategic Environmental Research and Development Program (SERDP) project (RC-2232) is to provide decision making tools for military installations in the southwestern US to help them adapt to the operational realities associated with climate change. For this study we coupled the spatially explicit fire and vegetation dynamics model FireBGCv2 with the Automated Geospatial Watershed Assessment tool (AGWA) to evaluate landscape vegetation change, fire disturbance, and surface runoff in response to projected climate forcing. A projected climate stream for the years 2005-2055 was developed from the Multivariate Adaptive Constructed Analogs (MACA) 4 km statistical downscaling of the CanESM2 GCM using Representative Concentration Pathway (RCP) 8.5. AGWA, an ArcGIS add-in tool, was used to automate the parameterization and execution of the Soil Water Assessment Tool (SWAT) and the KINematic runoff and EROSion2 (KINEROS2) models based on GIS layers. Landscape raster data generated by FireBGCv2 project an increase in fire and drought associated tree mortality and a decrease in vegetative basal area over the years of simulation. Preliminary results from SWAT modeling efforts show an increase to surface runoff during years following a fire, and for future winter rainy seasons. Initial results from KINEROS2 model runs show that peak runoff rates are expected to increase 10-100 fold as a result of intense rainfall falling on burned areas.

Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies

NASA Astrophysics Data System (ADS)

Williams, Paul; Howe, Nicola; Gregory, Jonathan; Smith, Robin; Joshi, Manoj

2016-04-01

In climate simulations, the impacts of the sub-grid scales on the resolved scales are conventionally represented using deterministic closure schemes, which assume that the impacts are uniquely determined by the resolved scales. Stochastic parameterization relaxes this assumption, by sampling the sub-grid variability in a computationally inexpensive manner. This presentation shows that the simulated climatological state of the ocean is improved in many respects by implementing a simple stochastic parameterization of ocean eddies into a coupled atmosphere-ocean general circulation model. Simulations from a high-resolution, eddy-permitting ocean model are used to calculate the eddy statistics needed to inject realistic stochastic noise into a low-resolution, non-eddy-permitting version of the same model. A suite of four stochastic experiments is then run to test the sensitivity of the simulated climate to the noise definition, by varying the noise amplitude and decorrelation time within reasonable limits. The addition of zero-mean noise to the ocean temperature tendency is found to have a non-zero effect on the mean climate. Specifically, in terms of the ocean temperature and salinity fields both at the surface and at depth, the noise reduces many of the biases in the low-resolution model and causes it to more closely resemble the high-resolution model. The variability of the strength of the global ocean thermohaline circulation is also improved. It is concluded that stochastic ocean perturbations can yield reductions in climate model error that are comparable to those obtained by refining the resolution, but without the increased computational cost. Therefore, stochastic parameterizations of ocean eddies have the potential to significantly improve climate simulations. Reference PD Williams, NJ Howe, JM Gregory, RS Smith, and MM Joshi (2016) Improved Climate Simulations through a Stochastic Parameterization of Ocean Eddies. Journal of Climate, under revision.

ICLUS v1.3 Population Projections

EPA Pesticide Factsheets

Climate and land-use change are major components of global environmental change with feedbacks between these components. The consequences of these interactions show that land use may exacerbate or alleviate climate change effects. Based on these findings it is important to use land-use scenarios that are consistent with the specific assumptions underlying climate-change scenarios. The Integrated Climate and Land-Use Scenarios (ICLUS) project developed land-use outputs that are based on a downscaled version of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) social, economic, and demographic storylines. ICLUS outputs are derived from a pair of models. A demographic model generates county-level population estimates that are distributed by a spatial allocation model (SERGoM v3) as housing density across the landscape. Land-use outputs were developed for the four main SRES storylines and a baseline (base case). The model is run for the conterminous USA and output is available for each scenario by decade to 2100. In addition to housing density at a 1 hectare spatial resolution, this project also generated estimates of impervious surface at a resolution of 1 square kilometer. This shapefile holds population data for all counties of the conterminous USA for all decades (2010-2100) and SRES population growth scenarios (A1, A2, B1, B2), as well as a 'base case' (BC) scenario, for use in the Integrated Climate and Land Use

Collaboration pathway(s) using new tools for optimizing operational climate monitoring from space

NASA Astrophysics Data System (ADS)

Helmuth, Douglas B.; Selva, Daniel; Dwyer, Morgan M.

2014-10-01

Consistently collecting the earth's climate signatures remains a priority for world governments and international scientific organizations. Architecting a solution requires transforming scientific missions into an optimized robust `operational' constellation that addresses the needs of decision makers, scientific investigators and global users for trusted data. The application of new tools offers pathways for global architecture collaboration. Recent (2014) rulebased decision engine modeling runs that targeted optimizing the intended NPOESS architecture, becomes a surrogate for global operational climate monitoring architecture(s). This rule-based systems tools provide valuable insight for Global climate architectures, through the comparison and evaluation of alternatives considered and the exhaustive range of trade space explored. A representative optimization of Global ECV's (essential climate variables) climate monitoring architecture(s) is explored and described in some detail with thoughts on appropriate rule-based valuations. The optimization tools(s) suggest and support global collaboration pathways and hopefully elicit responses from the audience and climate science shareholders.

Change in the magnitude and mechanisms of global temperature variability with warming

PubMed Central

Brown, Patrick T.; Ming, Yi; Li, Wenhong; Hill, Spencer A.

2017-01-01

Natural unforced variability in global mean surface air temperature (GMST) can mask or exaggerate human-caused global warming, and thus a complete understanding of this variability is highly desirable. Significant progress has been made in elucidating the magnitude and physical origins of present-day unforced GMST variability, but it has remained unclear how such variability may change as the climate warms. Here we present modeling evidence that indicates that the magnitude of low-frequency GMST variability is likely to decline in a warmer climate and that its generating mechanisms may be fundamentally altered. In particular, a warmer climate results in lower albedo at high latitudes, which yields a weaker albedo feedback on unforced GMST variability. These results imply that unforced GMST variability is dependent on the background climatological conditions, and thus climate model control simulations run under perpetual preindustrial conditions may have only limited relevance for understanding the unforced GMST variability of the future. PMID:29391875

Ensemble of regional climate model projections for Ireland

NASA Astrophysics Data System (ADS)

Nolan, Paul; McGrath, Ray

2016-04-01

The method of Regional Climate Modelling (RCM) was employed to assess the impacts of a warming climate on the mid-21st-century climate of Ireland. The RCM simulations were run at high spatial resolution, up to 4 km, thus allowing a better evaluation of the local effects of climate change. Simulations were run for a reference period 1981-2000 and future period 2041-2060. Differences between the two periods provide a measure of climate change. To address the issue of uncertainty, a multi-model ensemble approach was employed. Specifically, the future climate of Ireland was simulated using three different RCMs, driven by four Global Climate Models (GCMs). To account for the uncertainty in future emissions, a number of SRES (B1, A1B, A2) and RCP (4.5, 8.5) emission scenarios were used to simulate the future climate. Through the ensemble approach, the uncertainty in the RCM projections can be partially quantified, thus providing a measure of confidence in the predictions. In addition, likelihood values can be assigned to the projections. The RCMs used in this work are the COnsortium for Small-scale MOdeling-Climate Limited-area Modelling (COSMO-CLM, versions 3 and 4) model and the Weather Research and Forecasting (WRF) model. The GCMs used are the Max Planck Institute's ECHAM5, the UK Met Office's HadGEM2-ES, the CGCM3.1 model from the Canadian Centre for Climate Modelling and the EC-Earth consortium GCM. The projections for mid-century indicate an increase of 1-1.6°C in mean annual temperatures, with the largest increases seen in the east of the country. Warming is enhanced for the extremes (i.e. hot or cold days), with the warmest 5% of daily maximum summer temperatures projected to increase by 0.7-2.6°C. The coldest 5% of night-time temperatures in winter are projected to rise by 1.1-3.1°C. Averaged over the whole country, the number of frost days is projected to decrease by over 50%. The projections indicate an average increase in the length of the growing season of over 35 days per year. Results show significant projected decreases in mean annual, spring and summer precipitation amounts by mid-century. The projected decreases are largest for summer, with "likely" reductions ranging from 0% to 20%. The frequencies of heavy precipitation events show notable increases (approximately 20%) during the winter and autumn months. The number of extended dry periods is projected to increase substantially during autumn and summer. Regional variations of projected precipitation change remain statistically elusive. The energy content of the wind is projected to significantly decrease for the future spring, summer and autumn months. Projected increases for winter were found to be statistically insignificant. The projected decreases were largest for summer, with "likely" values ranging from 3% to 15%. Results suggest that the tracks of intense storms are projected to extend further south over Ireland relative to those in the reference simulation. As extreme storm events are rare, the storm-tracking research needs to be extended. Future work will focus on analysing a larger ensemble, thus allowing a robust statistical analysis of extreme storm track projections.

Climate change mitigation: comparative assessment of Malaysian and ASEAN scenarios.

PubMed

Rasiah, Rajah; Ahmed, Adeel; Al-Amin, Abul Quasem; Chenayah, Santha

2017-01-01

This paper analyses empirically the optimal climate change mitigation policy of Malaysia with the business as usual scenario of ASEAN to compare their environmental and economic consequences over the period 2010-2110. A downscaling empirical dynamic model is constructed using a dual multidisciplinary framework combining economic, earth science, and ecological variables to analyse the long-run consequences. The model takes account of climatic variables, including carbon cycle, carbon emission, climatic damage, carbon control, carbon concentration, and temperature. The results indicate that without optimal climate policy and action, the cumulative cost of climate damage for Malaysia and ASEAN as a whole over the period 2010-2110 would be MYR40.1 trillion and MYR151.0 trillion, respectively. Under the optimal policy, the cumulative cost of climatic damage for Malaysia would fall to MYR5.3 trillion over the 100 years. Also, the additional economic output of Malaysia will rise from MYR2.1 billion in 2010 to MYR3.6 billion in 2050 and MYR5.5 billion in 2110 under the optimal climate change mitigation scenario. The additional economic output for ASEAN would fall from MYR8.1 billion in 2010 to MYR3.2 billion in 2050 before rising again slightly to MYR4.7 billion in 2110 in the business as usual ASEAN scenario.

Decadal climate prediction (project GCEP).

PubMed

Haines, Keith; Hermanson, Leon; Liu, Chunlei; Putt, Debbie; Sutton, Rowan; Iwi, Alan; Smith, Doug

2009-03-13

Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.

High resolution present climate and surface mass balance (SMB) of Svalbard modelled by MAR and implementation of a new online SMB downscaling method

NASA Astrophysics Data System (ADS)

Lang, C.; Fettweis, X.; Kittel, C.; Erpicum, M.

2017-12-01

We present the results of high resolution simulations of the climate and SMB of Svalbard with the regional climate model MAR forced by ERA-40 then ERA-Interim, as well as an online downscaling method allowing us to model the SMB and its components at a resolution twice as high (2.5 vs 5 km here) using only about 25% more CPU time. Spitsbergen, the largest island in Svalbard, has a very hilly topography and a high spatial resolution is needed to correctly represent the local topography and the complex pattern of ice distribution and precipitation. However, high resolution runs with an RCM fully coupled to an energy balance module like MAR require a huge amount of computation time. The hydrostatic equilibrium hypothesis used in MAR also becomes less valid as the spatial resolution increases. We therefore developed in MAR a method to run the snow module at a resolution twice as high as the atmospheric module. Near-surface temperature and humidity are corrected on a grid with a resolution twice as high, as a function of their local gradients and the elevation difference between the corresponding pixels in the 2 grids. We compared the results of our runs at 5 km and with SMB downscaled at 2.5 km over 1960 — 2016 and compared those to previous 10 km runs. On Austfonna, where the slopes are gentle, the agreement between observations and the 5 km SMB is better than with the 10 km SMB. It is again improved at 2.5 km but the gain is relatively small, showing the interest of our method rather than running a time consuming classic 2.5 km resolution simulation. On Spitsbergen, we show that a spatial resolution of 2.5 km is still not enough to represent the complex pattern of topography, precipitation and SMB. Due to a change in the summer atmospheric circulation, from a westerly flow over Svalbard to a northwesterly flow bringing colder air, the SMB of Svalbard was stable between 2006 and 2012, while several melt records were broken in Greenland, due to conditions more anticyclonic than usual. In 2013, the reverse situation happened and a southwesterly atmospheric circulation brought warmer air over Svalbard. The SMB broke the last 55 years' record. In 2016, the temperature was higher than average and a new record melt was broken despite a northwesterly flow. The northerly flow still mitigated the warming over Svalbard, which was much lower than most regions of the Arctic.

Earth System Model Development and Analysis using FRE-Curator and Live Access Servers: On-demand analysis of climate model output with data provenance.

NASA Astrophysics Data System (ADS)

Radhakrishnan, A.; Balaji, V.; Schweitzer, R.; Nikonov, S.; O'Brien, K.; Vahlenkamp, H.; Burger, E. F.

2016-12-01

There are distinct phases in the development cycle of an Earth system model. During the model development phase, scientists make changes to code and parameters and require rapid access to results for evaluation. During the production phase, scientists may make an ensemble of runs with different settings, and produce large quantities of output, that must be further analyzed and quality controlled for scientific papers and submission to international projects such as the Climate Model Intercomparison Project (CMIP). During this phase, provenance is a key concern:being able to track back from outputs to inputs. We will discuss one of the paths taken at GFDL in delivering tools across this lifecycle, offering on-demand analysis of data by integrating the use of GFDL's in-house FRE-Curator, Unidata's THREDDS and NOAA PMEL's Live Access Servers (LAS).Experience over this lifecycle suggests that a major difficulty in developing analysis capabilities is only partially the scientific content, but often devoted to answering the questions "where is the data?" and "how do I get to it?". "FRE-Curator" is the name of a database-centric paradigm used at NOAA GFDL to ingest information about the model runs into an RDBMS (Curator database). The components of FRE-Curator are integrated into Flexible Runtime Environment workflow and can be invoked during climate model simulation. The front end to FRE-Curator, known as the Model Development Database Interface (MDBI) provides an in-house web-based access to GFDL experiments: metadata, analysis output and more. In order to provide on-demand visualization, MDBI uses Live Access Servers which is a highly configurable web server designed to provide flexible access to geo-referenced scientific data, that makes use of OPeNDAP. Model output saved in GFDL's tape archive, the size of the database and experiments, continuous model development initiatives with more dynamic configurations add complexity and challenges in providing an on-demand visualization experience to our GFDL users.

Effect of climate change on shoreline shifts at a straight and continuous coast

NASA Astrophysics Data System (ADS)

Rajasree, B. R.; Deo, M. C.; Sheela Nair, L.

2016-12-01

The prediction of the rate of shoreline shifts as well as that of erosion and accretion over future at a given location is traditionally done on the basis of analysis of past wave data. However under the changing climate affected by global warming it is better done considering the projected wave conditions over the future. The same is demonstrated in this work with respect to a stretch of coastline at 'Udupi' along the west coast of India. The shoreline changes in the past are first determined with the help of historic satellite images. A numerical shoreline model is later run on the basis of wave simulations of past 35 years as well as future 35 years. The latter wave conditions are obtained from wind projections corresponding to a high resolution regional climate model run for a moderate pathway of global warming. Alternatively prediction of the changes over future 35 years is also made by using the soft computing tool of artificial neural network (ANN) trained with the help of past satellite images. The results indicate that the area under consideration presently undergoes considerable erosion and this process will accelerate in future. The volume of annual sediment transport will also substantially increase over the future. The alternative computations made with the help of an ANN confirmed the future rising trend of erosion, albeit at smaller rate than the numerically predicted one.

Glaciological reconstruction of Holocene ice margins in northwestern Greenland

NASA Astrophysics Data System (ADS)

Birkel, S. D.; Osterberg, E. C.; Kelly, M. A.; Axford, Y.

2014-12-01

The past few decades of climate warming have brought overall margin retreat to the Greenland Ice Sheet. In order to place recent and projected changes in context, we are undertaking a collaborative field-modeling study that aims to reconstruct the Holocene history of ice-margin fluctuation near Thule (~76.5°N, 68.7°W), and also along the North Ice Cap (NIC) in the Nunatarssuaq region (~76.7°N, 67.4°W). Fieldwork reported by Kelly et al. (2013) reveals that ice in the study areas was less extensive than at present ca. 4700 (GIS) and ca. 880 (NIC) cal. years BP, presumably in response to a warmer climate. We are now exploring Holocene ice-climate coupling using the University of Maine Ice Sheet Model (UMISM). Our approach is to first test what imposed climate anomalies can afford steady state ice margins in accord with field data. A second test encompasses transient simulation of the Holocene, with climate boundary conditions supplied by existing paleo runs of the Community Climate System Model version 4 (CCSM4), and a climate forcing signal derived from Greenland ice cores. In both cases, the full ice sheet is simulated at 10 km resolution with nested domains at 0.5 km for the study areas. UMISM experiments are underway, and results will be reported at the meeting.

Response of the European ecosystems to climate change: a modelling approach for the 21st century.

NASA Astrophysics Data System (ADS)

Dury, Marie; Warnant, Pierre; François, Louis; Henrot, Alexandra; Favre, Eric; Hambuckers, Alain

2010-05-01

According to projections, over the 21st century, significant climatic changes appear and will be strengthened all over the world with the continuing increase of the atmospheric CO2 level. Climate will be generally warmer with notably changes in the seasonality and in the precipitation regime. These changes will have major impacts on the environment and on the biodiversity of natural ecosystems. Geographic distribution of ecosystems may be modified since species will be driven to migrate towards more suitable areas (e. g., shifting of the arctic tree lines). The CARAIB dynamic vegetation model (Carbon Assimilation in the Biosphere) forced with 21st century climate scenarios of the IPCC (ARPEGE-Climat model) is used to illustrate and analyse the potential impacts of climate change on tree species distribution and productivity over Europe. Changes in hydrological budget (e. g., runoff) and fire effects on forests will also be shown. Transient runs (1975-2100) with a new dynamic module introduced in CARAIB are performed to follow the future evolutions. In the new module, the processes of species establishment, competition and mortality due to stresses and disturbances have been improved. Among others, increased atmospheric CO2 and warmer climate increase tree productivity while drier conditions decrease it. Regions with more severe droughts will also be affected by an increase of wildfire frequency, which may have large impacts on vegetation density and distribution.

A general circulation model study of the climatic effect of observed stratospheric ozone depletion between 1980 and 1990

NASA Technical Reports Server (NTRS)

Dudek, Michael P.; Wang, Wei-Chyung; Liang, Xin-Zhong; Li, Zhu

1994-01-01

The total ozone mapping spectrometer (TOMS) and stratospheric aerosol and gas experiment (SAGE) measurements show a significant reduction in the stratospheric ozone over the middle and high latitudes of both hemispheres between the years 1979 and 1991 (WMO, 1992). This change in ozone will effect both the solar and longwave radiation with climate implications. However, recent studies (Ramaswamy et al., 1992; WMO, 1992) indicate that the net effect depends not only on latitudes and seasons, but also on the response of the lower stratospheric temperature. In this study we use a general circulation model (GCM) to calculate the climatic effect due to stratospheric ozone depletion and compare the effect with that due to observed increases of trace gases CO2, CH4, N2O, and CFC's for the period 1980-1990. In the simulations, we use the observed changes in ozone derived from the TOMS data. The GCM used is a version of the NCAR community climate model referenced in Wang et al. (1991). For the present study we run the model in perpetual January and perpetual July modes in which the incoming solar radiation and climatological sea surface temperatures are held constant.

Development of ALARO-Climate regional climate model for a very high resolution

NASA Astrophysics Data System (ADS)

Skalak, Petr; Farda, Ales; Brozkova, Radmila; Masek, Jan

2013-04-01

ALARO-Climate is a new regional climate model (RCM) derived from the ALADIN LAM model family. It is based on the numerical weather prediction model ALARO and developed at the Czech Hydrometeorological Institute. The model is expected to able to work in the so called "grey zone" physics (horizontal resolution of 4 - 7 km) and at the same time retain its ability to be operated in resolutions in between 20 and 50 km, which are typical for contemporary generation of regional climate models. Here we present the main features of the RCM ALARO-Climate and results of the first model simulations on longer time-scales (1961-1990). The model was driven by the ERA-40/Interim re-analyses and run on the large pan-European integration domain ("ENSEMBLES / Euro-Cordex domain") with spatial resolution of 25 km. The simulated model climate was compared with the gridded observation of air temperature (mean, maximum, minimum) and precipitation from the E-OBS version 7 dataset. The validation of the first ERA-40 simulation has revealed significant cold biases in all seasons (between -4 and -2 °C) and overestimation of precipitation on 20% to 60% in the selected Central Europe target area (0° - 30° eastern longitude ; 40° - 60° northern latitude). The consequent adaptations in the model and their effect on the simulated properties of climate variables are illustrated. Acknowledgements: This study was performed within the frame of projects ALARO (project P209/11/2405 sponsored by the Czech Science Foundation) and CzechGlobe Centre (CZ.1.05/1.1.00/02.0073). The partial support was also provided under the projects P209-11-0956 of the Czech Science Foundation and CZ.1.07/2.4.00/31.0056 (Operational Programme of Education for Competitiveness of Ministry of Education, Youth and Sports of the Czech Republic).

The Role of Arctic Sea Ice in Last Millennium Climate Variability: Model-Proxy Comparisons Using Ensemble Members and Novel Model Experiments.

NASA Astrophysics Data System (ADS)

Gertler, C. G.; Monier, E.; Prinn, R. G.

2016-12-01

Variability in sea ice extent is a prominent feature of forced simulations of the last millennium and reconstructions of paleoclimate using proxy records. The rapid 20th century decline in sea ice extent is most likely due to greenhouse gas forcing, but the accuracy of future projections depend on the characterization of natural variability. Declining sea ice extent affects regional climate and society, but also plays a large role in Arctic amplification, with implications for mid-latitude circulation and even large-scale climate oscillations. To characterize the effects of natural and anthropogenic climate forcing on sea ice and the related changes in large-scale atmospheric circulation, a combination of instrumental record, paleoclimate reconstructions, and general circulation models can be employed to recreate sea ice extents and the corresponding atmosphere-ocean states. Model output from the last millennium ensemble (LME) is compared to a proxy-based sea ice reconstruction and a global proxy network using a variety of statistical and data assimilation techniques. Further model runs using the Community Earth Systems Model (CESM) are performed with the same inputs as LME but forced with experimental sea ice extents, and results are contextualized within the larger ensemble by a variety of metrics.

Climate regulation enhances the value of second generation biofuel technology

NASA Astrophysics Data System (ADS)

Hertel, T. W.; Steinbuks, J.; Tyner, W.

2014-12-01

Commercial scale implementation of second generation (2G) biofuels has long been 'just over the horizon - perhaps a decade away'. However, with recent innovations, and higher oil prices, we appear to be on the verge of finally seeing commercial scale implementations of cellulosic to liquid fuel conversion technologies. Interest in this technology derives from many quarters. Environmentalists see this as a way of reducing our carbon footprint, however, absent a global market for carbon emissions, private firms will not factor this into their investment decisions. Those interested in poverty and nutrition see this as a channel for lessening the biofuels' impact on food prices. But what is 2G technology worth to society? How valuable are prospective improvements in this technology? And how are these valuations affected by future uncertainties, including climate regulation, climate change impacts, and energy prices? This paper addresses all of these questions. We employ FABLE, a dynamic optimization model for the world's land resources which characterizes the optimal long run path for protected natural lands, managed forests, crop and livestock land use, energy extraction and biofuels over the period 2005-2105. By running this model twice for each future state of the world - once with 2G biofuels technology available and once without - we measure the contribution of the technology to global welfare. Given the uncertainty in how these technologies are likely to evolve, we consider a range cost estimates - from optimistic to pessimistic. In addition to technological uncertainty, there is great uncertainty in the conditions characterizing our baseline for the 21st century. For each of the 2G technology scenarios, we therefore also consider a range of outcomes for key drivers of global land use, including: population, income, oil prices, climate change impacts and climate regulation. We find that the social valuation of 2G technologies depends critically on climate change regulations and future oil prices. In the base case with no climate policy and higher oil prices, the value of second generation biofuels is roughly $8 billion. With stringent climate change regulations in place, 2G biofuels are worth about fifty percent more.

The US CLIVAR Working Group on Drought: A Multi-Model Assessment of the Impact of SST Anomalies on Regional Drought

NASA Astrophysics Data System (ADS)

Schubert; Drought Working Group, S.

2008-12-01

The USCLIVAR working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land-atmosphere feedbacks on regional drought. Specific questions that the runs are designed to address include: What are mechanisms that maintain drought across the seasonal cycle and from one year to the next. What is the role of the land? What is the role of the different ocean basins, including the impact of El Nino/Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Atlantic Multi-decadal Oscillation (AMO), and warming trends in the global oceans? The runs were done with several global atmospheric models including NASA/NSIPP-1, NCEP/GFS, GFDL/AM2, and NCAR CCM3 and CAM3. In addition, runs were done with the NCEP CFS (coupled atmosphere-ocean) model by employing a novel adjustment technique to nudge the coupled model towards the imposed SST forcing patterns. This talk provides an overview of the experiments and some initial results.

The US CLIVAR Working Group on Drought: A Multi-Model Assessment of the Impact of SST Anomalies on Regional Drought

NASA Technical Reports Server (NTRS)

Schubert, Siegfried

2008-01-01

The US CLIVAR working group on drought recently initiated a series of global climate model simulations forced with idealized SST anomaly patterns, designed to address a number of uncertainties regarding the impact of SST forcing and the role of land-atmosphere feedbacks on regional drought. Specific questions that the runs are designed to address include: What are mechanisms that maintain drought across the seasonal cycle and from one year to the next. What is the role of the land? What is the role of the different ocean basins, including the impact of EL Nino/Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), the Atlantic Multi-decadal Oscillation (AMO), and warming trends in the global oceans? The runs were done with several global atmospheric models including NASA/NSIPP-1, NCEP/GFS, GFDL/AM2, and NCAR CCM3 and CAM3. In addition, runs were done with the NCEP CFS (coupled atmosphere-ocean) model by employing a novel adjustment technique to nudge the coupled model towards the imposed SST forcing patterns. This talk provides an overview of the experiments and some initial results.

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.

Snowmelt runoff modeling in simulation and forecasting modes with the Martinec-Mango model

NASA Technical Reports Server (NTRS)

Shafer, B.; Jones, E. B.; Frick, D. M. (Principal Investigator)

1982-01-01

The Martinec-Rango snowmelt runoff model was applied to two watersheds in the Rio Grande basin, Colorado-the South Fork Rio Grande, a drainage encompassing 216 sq mi without reservoirs or diversions and the Rio Grande above Del Norte, a drainage encompassing 1,320 sq mi without major reservoirs. The model was successfully applied to both watersheds when run in a simulation mode for the period 1973-79. This period included both high and low runoff seasons. Central to the adaptation of the model to run in a forecast mode was the need to develop a technique to forecast the shape of the snow cover depletion curves between satellite data points. Four separate approaches were investigated-simple linear estimation, multiple regression, parabolic exponential, and type curve. Only the parabolic exponential and type curve methods were run on the South Fork and Rio Grande watersheds for the 1980 runoff season using satellite snow cover updates when available. Although reasonable forecasts were obtained in certain situations, neither method seemed ready for truly operational forecasts, possibly due to a large amount of estimated climatic data for one or two primary base stations during the 1980 season.

DESYCO: a Decision Support System to provide climate services for coastal stakeholders dealing with climate change impacts.

NASA Astrophysics Data System (ADS)

Torresan, S.; Gallina, V.; Giannini, V.; Rizzi, J.; Zabeo, A.; Critto, A.; Marcomini, A.

2012-04-01

At the international level climate services are recognized as innovative tools aimed at providing and distributing climate data and information according to the needs of end-users. Furthermore, needs-based climate services are extremely effective to manage climate risks and take advantage of the opportunities associated with climate change impacts. To date, climate services are mainly related to climate models that supply climate data (e.g. temperature, precipitations) at different spatial and time scales. However, there is a significant gap of tools aimed at providing information about risks and impacts induced by climate change and allowing non-expert stakeholders to use both climate-model and climate-impact data. DESYCO is a GIS-Decision Support System aimed at the integrated assessment of multiple climate change impacts on vulnerable coastal systems (e.g. beaches, river deltas, estuaries and lagoons, wetlands, agricultural and urban areas). It is an open source software that manages different input data (e.g. raster or shapefiles) coming from climate models (e.g. global and regional climate projections) and high resolution impact models (e.g. hydrodynamic, hydrological and biogeochemical simulations) in order to provide hazard, exposure, susceptibility, risk and damage maps for the identification and prioritization of hot-spot areas and to provide a basis for the definition of coastal adaptation and management strategies. Within the CLIM-RUN project (FP7) DESYCO is proposed as an helpful tool to bridge the gap between climate data and stakeholder needs and will be applied to the coastal area of the North Adriatic Sea (Italy) in order to provide climate services for local authorities involved in coastal zone management. Accordingly, a first workshop was held in Venice (Italy) with coastal authorities, climate experts and climate change risk experts, in order to start an iterative exchange of information about the knowledge related to climate change, climate models and projections, impact and risk parameters and to know what are stakeholder needs related to climate change in a climate service perspective. The preliminary results gained from the workshop showed that DESYCO is an helpful tool for the impact and risk assessment related to climate change that could be improved in order to fulfill stakeholder needs.

pyhector: A Python interface for the simple climate model Hector

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

N Willner, Sven; Hartin, Corinne; Gieseke, Robert

2017-04-01

Pyhector is a Python interface for the simple climate model Hector (Hartin et al. 2015) developed in C++. Simple climate models like Hector can, for instance, be used in the analysis of scenarios within integrated assessment models like GCAM1, in the emulation of complex climate models, and in uncertainty analyses. Hector is an open-source, object oriented, simple global climate carbon cycle model. Its carbon cycle consists of a one pool atmosphere, three terrestrial pools which can be broken down into finer biomes or regions, and four carbon pools in the ocean component. The terrestrial carbon cycle includes primary production andmore » respiration fluxes. The ocean carbon cycle circulates carbon via a simplified thermohaline circulation, calculating air-sea fluxes as well as the marine carbonate system (Hartin et al. 2016). The model input is time series of greenhouse gas emissions; as example scenarios for these the Pyhector package contains the Representative Concentration Pathways (RCPs)2. These were developed to cover the range of baseline and mitigation emissions scenarios and are widely used in climate change research and model intercomparison projects. Using DataFrames from the Python library Pandas (McKinney 2010) as a data structure for the scenarios simplifies generating and adapting scenarios. Other parameters of the Hector model can easily be modified when running the model. Pyhector can be installed using pip from the Python Package Index.3 Source code and issue tracker are available in Pyhector's GitHub repository4. Documentation is provided through Readthedocs5. Usage examples are also contained in the repository as a Jupyter Notebook (Pérez and Granger 2007; Kluyver et al. 2016). Courtesy of the Mybinder project6, the example Notebook can also be executed and modified without installing Pyhector locally.« less

A comparison of river discharge calculated by using a regional climate model output with different reanalysis datasets in 1980s and 1990s

NASA Astrophysics Data System (ADS)

Ma, X.; Yoshikane, T.; Hara, M.; Adachi, S. A.; Wakazuki, Y.; Kawase, H.; Kimura, F.

2014-12-01

To check the influence of boundary input data on a modeling result, we had a numerical investigation of river discharge by using runoff data derived by a regional climate model with a 4.5-km resolution as input data to a hydrological model. A hindcast experiment, which to reproduce the current climate was carried out for the two decades, 1980s and 1990s. We used the Advanced Research WRF (ARW) (ver. 3.2.1) with a two-way nesting technique and the WRF single-moment 6-class microphysics scheme. Noah-LSM is adopted to simulate the land surface process. The NCEP/NCAR and ERA-Interim 6-hourly reanalysis datasets were used as the lateral boundary condition for the runs, respectively. The output variables used for river discharge simulation from the WRF model were underground runoff and surface runoff. Four rivers (Mogami, Agano, Jinzu and Tone) were selected in this study. The results showed that the characteristic of river discharge in seasonal variation could be represented and there were overestimated compared with measured one.

Abundance of adverse environmental conditions during critical stages of crop production in Northern Germany.

PubMed

Strer, Maximilian; Svoboda, Nikolai; Herrmann, Antje

2018-01-01

Understanding the abundance of adverse environmental conditions e.g. frost, drought, and heat during critical crop growth stages, which are assumed to be altered by climate change, is crucial for an accurate risk assessment for cropping systems. While a lengthening of the vegetation period may be beneficial, higher frequencies of heat or frost events and drought spells are generally regarded as harmful. The objective of the present study was to quantify shifts in maize and wheat phenology and the occurrence of adverse environmental conditions during critical growth stages for four regions located in the North German Plain. First, a statistical analysis of phenological development was conducted based on recent data (1981-2010). Next, these data were used to calibrate the DSSAT-CERES wheat and maize models, which were then used to run three climate projections representing the maximum, intermediate and minimum courses of climate development within the RCP 8.5 continuum during the years 2021-2050. By means of model simulation runs and statistical analysis, the climate data were evaluated for the abundance of adverse environmental conditions during critical development stages, i.e. the stages of early crop development, anthesis, sowing and harvest. Proxies for adverse environmental conditions included thresholds of low and high temperatures as well as soil moisture. The comparison of the baseline climate and future climate projections showed a significant increase in the abundance of adverse environmental conditions during critical growth stages in the future. The lengthening of the vegetation period in spring did not compensate for the increased abundance of high temperatures, e.g. during anthesis. The results of this study indicate the need to develop adaptation strategies, such as implementing changes in cropping calendars. An increase in frost risk during early development, however, reveals the limited feasibility of early sowing as a mitigation strategy. In addition, the abundance of low soil water contents that hamper important production processes such as sowing and harvest were found to increase locally.

Linear Regression Quantile Mapping (RQM) - A new approach to bias correction with consistent quantile trends

NASA Astrophysics Data System (ADS)

Passow, Christian; Donner, Reik

2017-04-01

Quantile mapping (QM) is an established concept that allows to correct systematic biases in multiple quantiles of the distribution of a climatic observable. It shows remarkable results in correcting biases in historical simulations through observational data and outperforms simpler correction methods which relate only to the mean or variance. Since it has been shown that bias correction of future predictions or scenario runs with basic QM can result in misleading trends in the projection, adjusted, trend preserving, versions of QM were introduced in the form of detrended quantile mapping (DQM) and quantile delta mapping (QDM) (Cannon, 2015, 2016). Still, all previous versions and applications of QM based bias correction rely on the assumption of time-independent quantiles over the investigated period, which can be misleading in the context of a changing climate. Here, we propose a novel combination of linear quantile regression (QR) with the classical QM method to introduce a consistent, time-dependent and trend preserving approach of bias correction for historical and future projections. Since QR is a regression method, it is possible to estimate quantiles in the same resolution as the given data and include trends or other dependencies. We demonstrate the performance of the new method of linear regression quantile mapping (RQM) in correcting biases of temperature and precipitation products from historical runs (1959 - 2005) of the COSMO model in climate mode (CCLM) from the Euro-CORDEX ensemble relative to gridded E-OBS data of the same spatial and temporal resolution. A thorough comparison with established bias correction methods highlights the strengths and potential weaknesses of the new RQM approach. References: A.J. Cannon, S.R. Sorbie, T.Q. Murdock: Bias Correction of GCM Precipitation by Quantile Mapping - How Well Do Methods Preserve Changes in Quantiles and Extremes? Journal of Climate, 28, 6038, 2015 A.J. Cannon: Multivariate Bias Correction of Climate Model Outputs - Matching Marginal Distributions and Inter-variable Dependence Structure. Journal of Climate, 29, 7045, 2016

High resolution global climate modelling; the UPSCALE project, a large simulation campaign

NASA Astrophysics Data System (ADS)

Mizielinski, M. S.; Roberts, M. J.; Vidale, P. L.; Schiemann, R.; Demory, M.-E.; Strachan, J.; Edwards, T.; Stephens, A.; Lawrence, B. N.; Pritchard, M.; Chiu, P.; Iwi, A.; Churchill, J.; del Cano Novales, C.; Kettleborough, J.; Roseblade, W.; Selwood, P.; Foster, M.; Glover, M.; Malcolm, A.

2014-01-01

The UPSCALE (UK on PRACE: weather-resolving Simulations of Climate for globAL Environmental risk) project constructed and ran an ensemble of HadGEM3 (Hadley centre Global Environment Model 3) atmosphere-only global climate simulations over the period 1985-2011, at resolutions of N512 (25 km), N216 (60 km) and N96 (130 km) as used in current global weather forecasting, seasonal prediction and climate modelling respectively. Alongside these present climate simulations a parallel ensemble looking at extremes of future climate was run, using a time-slice methodology to consider conditions at the end of this century. These simulations were primarily performed using a 144 million core hour, single year grant of computing time from PRACE (the Partnership for Advanced Computing in Europe) in 2012, with additional resources supplied by the Natural Environmental Research Council (NERC) and the Met Office. Almost 400 terabytes of simulation data were generated on the HERMIT supercomputer at the high performance computing center Stuttgart (HLRS), and transferred to the JASMIN super-data cluster provided by the Science and Technology Facilities Council Centre for Data Archival (STFC CEDA) for analysis and storage. In this paper we describe the implementation of the project, present the technical challenges in terms of optimisation, data output, transfer and storage that such a project involves and include details of the model configuration and the composition of the UPSCALE dataset. This dataset is available for scientific analysis to allow assessment of the value of model resolution in both present and potential future climate conditions.

High-resolution global climate modelling: the UPSCALE project, a large-simulation campaign

NASA Astrophysics Data System (ADS)

Mizielinski, M. S.; Roberts, M. J.; Vidale, P. L.; Schiemann, R.; Demory, M.-E.; Strachan, J.; Edwards, T.; Stephens, A.; Lawrence, B. N.; Pritchard, M.; Chiu, P.; Iwi, A.; Churchill, J.; del Cano Novales, C.; Kettleborough, J.; Roseblade, W.; Selwood, P.; Foster, M.; Glover, M.; Malcolm, A.

2014-08-01

The UPSCALE (UK on PRACE: weather-resolving Simulations of Climate for globAL Environmental risk) project constructed and ran an ensemble of HadGEM3 (Hadley Centre Global Environment Model 3) atmosphere-only global climate simulations over the period 1985-2011, at resolutions of N512 (25 km), N216 (60 km) and N96 (130 km) as used in current global weather forecasting, seasonal prediction and climate modelling respectively. Alongside these present climate simulations a parallel ensemble looking at extremes of future climate was run, using a time-slice methodology to consider conditions at the end of this century. These simulations were primarily performed using a 144 million core hour, single year grant of computing time from PRACE (the Partnership for Advanced Computing in Europe) in 2012, with additional resources supplied by the Natural Environment Research Council (NERC) and the Met Office. Almost 400 terabytes of simulation data were generated on the HERMIT supercomputer at the High Performance Computing Center Stuttgart (HLRS), and transferred to the JASMIN super-data cluster provided by the Science and Technology Facilities Council Centre for Data Archival (STFC CEDA) for analysis and storage. In this paper we describe the implementation of the project, present the technical challenges in terms of optimisation, data output, transfer and storage that such a project involves and include details of the model configuration and the composition of the UPSCALE data set. This data set is available for scientific analysis to allow assessment of the value of model resolution in both present and potential future climate conditions.

Spontaneous abrupt climate change due to an atmospheric blocking-sea-ice-ocean feedback in an unforced climate model simulation.

PubMed

Drijfhout, Sybren; Gleeson, Emily; Dijkstra, Henk A; Livina, Valerie

2013-12-03

Abrupt climate change is abundant in geological records, but climate models rarely have been able to simulate such events in response to realistic forcing. Here we report on a spontaneous abrupt cooling event, lasting for more than a century, with a temperature anomaly similar to that of the Little Ice Age. The event was simulated in the preindustrial control run of a high-resolution climate model, without imposing external perturbations. Initial cooling started with a period of enhanced atmospheric blocking over the eastern subpolar gyre. In response, a southward progression of the sea-ice margin occurred, and the sea-level pressure anomaly was locked to the sea-ice margin through thermal forcing. The cold-core high steered more cold air to the area, reinforcing the sea-ice concentration anomaly east of Greenland. The sea-ice surplus was carried southward by ocean currents around the tip of Greenland. South of 70 °N, sea ice already started melting and the associated freshwater anomaly was carried to the Labrador Sea, shutting off deep convection. There, surface waters were exposed longer to atmospheric cooling and sea surface temperature dropped, causing an even larger thermally forced high above the Labrador Sea. In consequence, east of Greenland, anomalous winds changed from north to south, terminating the event with similar abruptness to its onset. Our results imply that only climate models that possess sufficient resolution to correctly represent atmospheric blocking, in combination with a sensitive sea-ice model, are able to simulate this kind of abrupt climate change.

Spontaneous abrupt climate change due to an atmospheric blocking–sea-ice–ocean feedback in an unforced climate model simulation

PubMed Central

Drijfhout, Sybren; Gleeson, Emily; Dijkstra, Henk A.; Livina, Valerie

2013-01-01

Abrupt climate change is abundant in geological records, but climate models rarely have been able to simulate such events in response to realistic forcing. Here we report on a spontaneous abrupt cooling event, lasting for more than a century, with a temperature anomaly similar to that of the Little Ice Age. The event was simulated in the preindustrial control run of a high-resolution climate model, without imposing external perturbations. Initial cooling started with a period of enhanced atmospheric blocking over the eastern subpolar gyre. In response, a southward progression of the sea-ice margin occurred, and the sea-level pressure anomaly was locked to the sea-ice margin through thermal forcing. The cold-core high steered more cold air to the area, reinforcing the sea-ice concentration anomaly east of Greenland. The sea-ice surplus was carried southward by ocean currents around the tip of Greenland. South of 70°N, sea ice already started melting and the associated freshwater anomaly was carried to the Labrador Sea, shutting off deep convection. There, surface waters were exposed longer to atmospheric cooling and sea surface temperature dropped, causing an even larger thermally forced high above the Labrador Sea. In consequence, east of Greenland, anomalous winds changed from north to south, terminating the event with similar abruptness to its onset. Our results imply that only climate models that possess sufficient resolution to correctly represent atmospheric blocking, in combination with a sensitive sea-ice model, are able to simulate this kind of abrupt climate change. PMID:24248352

The Use of a Mesoscale Climate Model to Validate the Nocturnal Carbon Flux over a Forested Site

NASA Astrophysics Data System (ADS)

Werth, D.; Parker, M.; Kurzeja, R.; Leclerc, M.; Watson, T.

2007-12-01

The Savannah River National Laboratory is initiating a comprehensive carbon dioxide monitoring and modeling program in collaboration with the University of Georgia and the Brookhaven National Laboratory. One of the primary goals is to study the dynamics of carbon dioxide in the stable nocturnal boundary layer (NBL) over a forested area of the Savannah River Site in southwest South Carolina. In the nocturnal boundary layer (NBL), eddy flux correlation is less effective in determining the release of CO2 due to respiration. Theoretically, however, the flux can be inferred by measuring the build up of CO2 in the stable layer throughout the night. This method of monitoring the flux will be validated and studied in more detail with both observations and the results of a high-resolution regional climate model. The experiment will involve two phases. First, an artificial tracer will be released into the forest boundary layer and observed through an array of sensors and at a flux tower. The event will be simulated with the RAMS climate model run at very high resolution. Ideally, the tracer will remain trapped within the stable layer and accumulate at rates which will allow us to infer the release rate, and this should compare well to the actual release rate. If an unknown mechanism allows the tracer to escape, the model simulation would be used to reveal it. In the second phase, carbon fluxes will be measured overnight through accumulation in the overlying layer. The RAMS model will be coupled with the SiB carbon model to simulate the nocturnal cycle of carbon dynamics, and this will be compared to the data collected during the night. As with the tracer study, the NBL method of flux measurement will be validated against the model. The RAMS-SiB coupled model has been run over the SRS at high-resolution to simulate the NBL, and results from simulations of both phases of the project will be presented.

Effects of Land-use/Land-cover and Climate Changes on Water Quantity and Quality in Sub-basins near Major US Cities in the Great Lakes Region

NASA Astrophysics Data System (ADS)

Murphy, L.; Al-Hamdan, M. Z.; Crosson, W. L.; Barik, M.

2017-12-01

Land-cover change over time to urbanized, less permeable surfaces, leads to reduced water infiltration at the location of water input while simultaneously transporting sediments, nutrients and contaminants farther downstream. With an abundance of agricultural fields bordering the greater urban areas of Milwaukee, Detroit, and Chicago, water and nutrient transport is vital to the farming industry, wetlands, and communities that rely on water availability. Two USGS stream gages each located within a sub-basin near each of these Great Lakes Region cities were examined, one with primarily urban land-cover between 1992 and 2011, and one with primarily agriculture land-cover. ArcSWAT, a watershed model and soil and water assessment tool used in extension with ArcGIS, was used to develop hydrologic models that vary the land-covers to simulate surface runoff during a model run period from 2004 to 2008. Model inputs that include a digital elevation model (DEM), Landsat-derived land-use/land-cover (LULC) satellite images from 1992, 2001, and 2011, soil classification, and meteorological data were used to determine the effect of different land-covers on the water runoff, nutrients and sediments. The models were then calibrated and validated to USGS stream gage data measurements over time. Additionally, the watershed model was run based on meteorological data from an IPCC CMIP5 high emissions climate change scenario for 2050. Model outputs from the different LCLU scenarios were statistically evaluated and results showed that water runoff, nutrients and sediments were impacted by LULC change in four out of the six sub-basins. In the 2050 climate scenario, only one out of the six sub-basin's water quantity and quality was affected. These results contribute to the importance of developing hydrologic models as the dependence on the Great Lakes as a freshwater resource competes with the expansion of urbanization leading to the movement of runoff, nutrients, and sediments off the land.

A Novel Observation-Guided Approach for Evaluating Mesoscale Convective Systems Simulated by the DOE ACME Model

NASA Astrophysics Data System (ADS)

Feng, Z.; Ma, P. L.; Hardin, J. C.; Houze, R.

2017-12-01

Mesoscale convective systems (MCSs) are the largest type of convective storms that develop when convection aggregates and induces mesoscale circulation features. Over North America, MCSs contribute over 60% of the total warm-season precipitation and over half of the extreme daily precipitation in the central U.S. Our recent study (Feng et al. 2016) found that the observed increases in springtime total and extreme rainfall in this region are dominated by increased frequency and intensity of long-lived MCSs*. To date, global climate models typically do not run at a resolution high enough to explicitly simulate individual convective elements and may not have adequate process representations for MCSs, resulting in a large deficiency in projecting changes of the frequency of extreme precipitation events in future climate. In this study, we developed a novel observation-guided approach specifically designed to evaluate simulated MCSs in the Department of Energy's climate model, Accelerated Climate Modeling for Energy (ACME). The ACME model has advanced treatments for convection and subgrid variability and for this study is run at 25 km and 100 km grid spacings. We constructed a robust MCS database consisting of over 500 MCSs from 3 warm-season observations by applying a feature-tracking algorithm to 4-km resolution merged geostationary satellite and 3-D NEXRAD radar network data over the Continental US. This high-resolution MCS database is then down-sampled to the 25 and 100 km ACME grids to re-characterize key MCS properties. The feature-tracking algorithm is adapted with the adjusted characteristics to identify MCSs from ACME model simulations. We demonstrate that this new analysis framework is useful for evaluating ACME's warm-season precipitation statistics associated with MCSs, and provides insights into the model process representations related to extreme precipitation events for future improvement. *Feng, Z., L. R. Leung, S. Hagos, R. A. Houze, C. D. Burleyson, and K. Balaguru (2016), More frequent intense and long-lived storms dominate the springtime trend in central US rainfall, Nat Commun, 7, 13429, doi: 10.1038/ncomms13429.

ParCAT: A Parallel Climate Analysis Toolkit

NASA Astrophysics Data System (ADS)

Haugen, B.; Smith, B.; Steed, C.; Ricciuto, D. M.; Thornton, P. E.; Shipman, G.

2012-12-01

Climate science has employed increasingly complex models and simulations to analyze the past and predict the future of our climate. The size and dimensionality of climate simulation data has been growing with the complexity of the models. This growth in data is creating a widening gap between the data being produced and the tools necessary to analyze large, high dimensional data sets. With single run data sets increasing into 10's, 100's and even 1000's of gigabytes, parallel computing tools are becoming a necessity in order to analyze and compare climate simulation data. The Parallel Climate Analysis Toolkit (ParCAT) provides basic tools that efficiently use parallel computing techniques to narrow the gap between data set size and analysis tools. ParCAT was created as a collaborative effort between climate scientists and computer scientists in order to provide efficient parallel implementations of the computing tools that are of use to climate scientists. Some of the basic functionalities included in the toolkit are the ability to compute spatio-temporal means and variances, differences between two runs and histograms of the values in a data set. ParCAT is designed to facilitate the "heavy lifting" that is required for large, multidimensional data sets. The toolkit does not focus on performing the final visualizations and presentation of results but rather, reducing large data sets to smaller, more manageable summaries. The output from ParCAT is provided in commonly used file formats (NetCDF, CSV, ASCII) to allow for simple integration with other tools. The toolkit is currently implemented as a command line utility, but will likely also provide a C library for developers interested in tighter software integration. Elements of the toolkit are already being incorporated into projects such as UV-CDAT and CMDX. There is also an effort underway to implement portions of the CCSM Land Model Diagnostics package using ParCAT in conjunction with Python and gnuplot. ParCAT is implemented in C to provide efficient file IO. The file IO operations in the toolkit use the parallel-netcdf library; this enables the code to use the parallel IO capabilities of modern HPC systems. Analysis that currently requires an estimated 12+ hours with the traditional CCSM Land Model Diagnostics Package can now be performed in as little as 30 minutes on a single desktop workstation and a few minutes for relatively small jobs completed on modern HPC systems such as ORNL's Jaguar.

The use of perturbed physics ensembles and emulation in palaeoclimate reconstruction (Invited)

NASA Astrophysics Data System (ADS)

Edwards, T. L.; Rougier, J.; Collins, M.

2010-12-01

Climate is a coherent process, with correlations and dependencies across space, time, and climate variables. However, reconstructions of palaeoclimate traditionally consider individual pieces of information independently, rather than making use of this covariance structure. Such reconstructions are at risk of being unphysical or at least implausible. Climate simulators such as General Circulation Models (GCMs), on the other hand, contain climate system theory in the form of dynamical equations describing physical processes, but are imperfect and computationally expensive. These two datasets - pointwise palaeoclimate reconstructions and climate simulator evaluations - contain complementary information, and a statistical synthesis can produce a palaeoclimate reconstruction that combines them while not ignoring their limitations. We use an ensemble of simulators with perturbed parameterisations, to capture the uncertainty about the simulator variant, and our method also accounts for structural uncertainty. The resulting reconstruction contains a full expression of climate uncertainty, not just pointwise but also jointly over locations. Such joint information is crucial in determining spatially extensive features such as isotherms, or the location of the tree-line. A second outcome of the statistical analysis is a refined distribution for the simulator parameters. In this way, information from palaeoclimate observations can be used directly in quantifying uncertainty in future climate projections. The main challenge is the expense of running a large scale climate simulator: each evaluation of an atmosphere-ocean GCM takes several months of computing time. The solution is to interpret the ensemble of evaluations within an 'emulator', which is a statistical model of the simulator. This technique has been used fruitfully in the statistical field of Computer Models for two decades, and has recently been applied in estimating uncertainty in future climate predictions in the UKCP09 (http://ukclimateprojections.defra.gov.uk). But only in the last couple of years has it developed to the point where it can be applied to large-scale spatial fields. We construct an emulator for the mid-Holocene (6000 calendar years BP) temperature anomaly over North America, at the resolution of our simulator (2.5° latitude by 3.75° longitude). This allows us to explore the behaviour of simulator variants that we could not afford to evaluate directly. We introduce the technique of 'co-emulation' of two versions of the climate simulator: the coupled atmosphere-ocean model HadCM3, and an equivalent with a simplified ocean, HadSM3. Running two different versions of a simulator is a powerful tool for increasing the information yield from a fixed budget of computer time, but the results must be combined statistically to account for the reduced fidelity of the quicker version. Emulators provide the appropriate framework.

Verification of GCM-generated regional seasonal precipitation for current climate and of statistical downscaling estimates under changing climate conditions

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

Busuioc, A.; Storch, H. von; Schnur, R.

Empirical downscaling procedures relate large-scale atmospheric features with local features such as station rainfall in order to facilitate local scenarios of climate change. The purpose of the present paper is twofold: first, a downscaling technique is used as a diagnostic tool to verify the performance of climate models on the regional scale; second, a technique is proposed for verifying the validity of empirical downscaling procedures in climate change applications. The case considered is regional seasonal precipitation in Romania. The downscaling model is a regression based on canonical correlation analysis between observed station precipitation and European-scale sea level pressure (SLP). Themore » climate models considered here are the T21 and T42 versions of the Hamburg ECHAM3 atmospheric GCM run in time-slice mode. The climate change scenario refers to the expected time of doubled carbon dioxide concentrations around the year 2050. Generally, applications of statistical downscaling to climate change scenarios have been based on the assumption that the empirical link between the large-scale and regional parameters remains valid under a changed climate. In this study, a rationale is proposed for this assumption by showing the consistency of the 2 x CO{sub 2} GCM scenarios in winter, derived directly from the gridpoint data, with the regional scenarios obtained through empirical downscaling. Since the skill of the GCMs in regional terms is already established, it is concluded that the downscaling technique is adequate for describing climatically changing regional and local conditions, at least for precipitation in Romania during winter.« less

Assessing changes to South African maize production areas in 2055 using empirical and process-based crop models

NASA Astrophysics Data System (ADS)

Estes, L.; Bradley, B.; Oppenheimer, M.; Beukes, H.; Schulze, R. E.; Tadross, M.

2010-12-01

Rising temperatures and altered precipitation patterns associated with climate change pose a significant threat to crop production, particularly in developing countries. In South Africa, a semi-arid country with a diverse agricultural sector, anthropogenic climate change is likely to affect staple crops and decrease food security. Here, we focus on maize production, South Africa’s most widely grown crop and one with high socio-economic value. We build on previous coarser-scaled studies by working at a finer spatial resolution and by employing two different modeling approaches: the process-based DSSAT Cropping System Model (CSM, version 4.5), and an empirical distribution model (Maxent). For climate projections, we use an ensemble of 10 general circulation models (GCMs) run under both high and low CO2 emissions scenarios (SRES A2 and B1). The models were down-scaled to historical climate records for 5838 quinary-scale catchments covering South Africa (mean area = 164.8 km2), using a technique based on self-organizing maps (SOMs) that generates precipitation patterns more consistent with observed gradients than those produced by the parent GCMs. Soil hydrological and mechanical properties were derived from textural and compositional data linked to a map of 26422 land forms (mean area = 46 km2), while organic carbon from 3377 soil profiles was mapped using regression kriging with 8 spatial predictors. CSM was run using typical management parameters for the several major dryland maize production regions, and with projected CO2 values. The Maxent distribution model was trained using maize locations identified using annual phenology derived from satellite images coupled with airborne crop sampling observations. Temperature and precipitation projections were based on GCM output, with an additional 10% increase in precipitation to simulate higher water-use efficiency under future CO2 concentrations. The two modeling approaches provide spatially explicit projections of gains and losses in maize productivity. We identify several areas-particularly along the southern and eastern boundaries of current production-with potential for increased productivity. However, larger areas, primarily in the more arid western and northern production regions, are likely to experience diminished productivity. The combination of process-based and distribution models for agricultural impacts assessments provides a useful comparison of two different crop modeling frameworks, as well as the finest scale investigation using a spatially-explicit implementation of a process-based model for South Africa. The large GCM ensemble and multiple emissions scenarios provide a broad climate risk assessment for current maize production. SOM downscaling can help improve climate impacts assessments by increasing their resolution, and by circumventing GCM precipitation schemes whose outcomes are highly divergent.

The Climate Effects of Deforestation the Amazon Rainforest under Global Warming Conditions

NASA Astrophysics Data System (ADS)

Werth, D.; Avissar, R.

2006-12-01

Replacement of tropical rainforests has been observed to have a strong drying effect in Amazon simulations, with effects reaching high into the atmospheric column and into the midlatitudes. The drying effects of deforestation, however, can be moderated by the effects of global warming, which should accelerate the hydrologic cycle of the Amazon. The effects of a prescribed, time-varying Amazon deforestation done in conjunction with a steady, moderate increase in CO2 concentrations are determined using a climate model. The model agrees with previous studies when each forcing is applied individually - compared to a control run, Amazon deforestation decreases the local precipitation and global warming increases it. When both are applied, however, the precipitation and other hydrologic variables decrease, but to a lesser extent than when deforestation alone was applied. In effect, the two effects act opposite to one another and bring the simulated climate closer to that of the control.

Reference aquaplanet climate in the Community Atmosphere Model, Version 5

DOE PAGES

Medeiros, Brian; Williamson, David L.; Olson, Jerry G.

2016-03-18

In this study, fundamental characteristics of the aquaplanet climate simulated by the Community Atmosphere Model, Version 5.3 (CAM5.3) are presented. The assumptions and simplifications of the configuration are described. A 16 year long, perpetual equinox integration with prescribed SST using the model’s standard 18 grid spacing is presented as a reference simulation. Statistical analysis is presented that shows similar aquaplanet configurations can be run for about 2 years to obtain robust climatological structures, including global and zonal means, eddy statistics, and precipitation distributions. Such a simulation can be compared to the reference simulation to discern differences in the climate, includingmore » an assessment of confidence in the differences. To aid such comparisons, the reference simulation has been made available via earthsystemgrid.org. Examples are shown comparing the reference simulation with simulations from the CAM5 series that make different microphysical assumptions and use a different dynamical core.« less

Incorporation of UK Met Office's radiation scheme into CPTEC's global model

NASA Astrophysics Data System (ADS)

Chagas, Júlio C. S.; Barbosa, Henrique M. J.

2009-03-01

Current parameterization of radiation in the CPTEC's (Center for Weather Forecast and Climate Studies, Cachoeira Paulista, SP, Brazil) operational AGCM has its origins in the work of Harshvardhan et al. (1987) and uses the formulation of Ramaswamy and Freidenreich (1992) for the short-wave absorption by water vapor. The UK Met Office's radiation code (Edwards and Slingo, 1996) was incorporated into CPTEC's global model, initially for short-wave only, and some impacts of that were shown by Chagas and Barbosa (2006). Current paper presents some impacts of the complete incorporation (both short-wave and long-wave) of UK Met Office's scheme. Selected results from off-line comparisons with line-by-line benchmark calculations are shown. Impacts on the AGCM's climate are assessed by comparing output of climate runs of current and modified AGCM with products from GEWEX/SRB (Surface Radiation Budget) project.

Assessing climate change impact on complementarity between solar and hydro power in areas affected by glacier shrinkage

NASA Astrophysics Data System (ADS)

Diah Puspitarini, Handriyanti; François, Baptiste; Zoccatelli, Davide; Brown, Casey; Creutin, Jean-Dominique; Zaramella, Mattia; Borga, Marco

2017-04-01

Variable Renewable Energy (VRE) sources such as wind, solar and runoff sources are variable in time and space, following their driving weather variables. In this work we aim to analyse optimal mixes of energy sources, i.e. mixes of sources which minimize the deviation between energy load and generation, for a region in the Upper Adige river basin (Eastern Italian Alps) affected by glacier shrinking. The study focuses on hydropower (run of the river - RoR) and solar energy, and analyses the current situation as well different climate change scenarios. Changes in glacier extent in response to climate warming and/or altered precipitation regimes have the potential to substantially alter the magnitude and timing, as well as the spatial variation of watershed-scale hydrologic fluxes. This may change the complementarity with solar power as well. In this study, we analyse the climate change impact on complementarity between RoR and solar using the Decision Scaling approach (Brown et al. 2012). With this approach, the system vulnerability is separated from the climatic hazard that can come from any set of past or future climate conditions. It departs from conventional top-down impact studies because it explores the sensitivity of the system response to a plausible range of climate variations rather than its sensitivity to the time-varying outcome of individual GCM projections. It mainly relies on the development of Climate Response Functions that bring together i) the sensitivity of some system success and/or failure indicators to key external drivers (i.e. mean features of regional climate) and ii) the future values of these drivers as simulated from climate simulation chains. The main VRE sources used in the study region are solar- and hydro-power (with an important fraction of run-of-the river hydropower). The considered indicator of success is the 'energy penetration' coefficient, defined as the long-run percentage of energy demand naturally met by the VRE on an hourly basis. Climate response functions, developed in a 2D climate change space (change in mean temperature and precipitation), are built from multiple hydro-climatic scenarios obtained by perturbing the observed weather time series with the change factor method, and considering given glacier storage states. Climate experiments are further used for assessing these change factors from different emission scenarios, climate models and future prediction lead times. Their positioning on the Climate Response Function allows discussing the risk/opportunities pertaining to changes in VRE penetration in the future. Results show i) the large impact of glacier shrinkage on the complementarity between solar and RoR energy sources and ii) that the impact is decreasing with time, with the main alterations to be expected in the coming 30 years. Brown, C., Ghile, Y., Laverty, M., Li, K., (2012). Decision scaling: Linking bottom up vulnerability analysis with climate projections in the water sector. Water Resour Res 48. 515 doi:10.1029/2011WR011212

Climate responses to SATIRE and SIM-based spectral solar forcing in a 3D atmosphere-ocean coupled GCM

NASA Astrophysics Data System (ADS)

Wen, Guoyong; Cahalan, Robert F.; Rind, David; Jonas, Jeffrey; Pilewskie, Peter; Wu, Dong L.; Krivova, Natalie A.

2017-03-01

We apply two reconstructed spectral solar forcing scenarios, one SIM (Spectral Irradiance Monitor) based, the other the SATIRE (Spectral And Total Irradiance REconstruction) modeled, as inputs to the GISS (Goddard Institute for Space Studies) GCMAM (Global Climate Middle Atmosphere Model) to examine climate responses on decadal to centennial time scales, focusing on quantifying the difference of climate response between the two solar forcing scenarios. We run the GCMAM for about 400 years with present day trace gas and aerosol for the two solar forcing inputs. We find that the SIM-based solar forcing induces much larger long-term response and 11-year variation in global averaged stratospheric temperature and column ozone. We find significant decreasing trends of planetary albedo for both forcing scenarios in the 400-year model runs. However the mechanisms for the decrease are very different. For SATIRE solar forcing, the decreasing trend of planetary albedo is associated with changes in cloud cover. For SIM-based solar forcing, without significant change in cloud cover on centennial and longer time scales, the apparent decreasing trend of planetary albedo is mainly due to out-of-phase variation in shortwave radiative forcing proxy (downwelling flux for wavelength >330 nm) and total solar irradiance (TSI). From the Maunder Minimum to present, global averaged annual mean surface air temperature has a response of 0.1 °C to SATIRE solar forcing compared to 0.04 °C to SIM-based solar forcing. For 11-year solar cycle, the global surface air temperature response has 3-year lagged response to either forcing scenario. The global surface air 11-year temperature response to SATIRE forcing is about 0.12 °C, similar to recent multi-model estimates, and comparable to the observational-based evidence. However, the global surface air temperature response to 11-year SIM-based solar forcing is insignificant and inconsistent with observation-based evidence.

Multimodel Estimates of Atmospheric Lifetimes of Long-lived Ozone-Depleting Substances: Present and Future

NASA Technical Reports Server (NTRS)

Chipperfield, M. P.; Liang, Q.; Strahan, S. E.; Morgenstern, O.; Dhomse, S. S.; Abraham, N. L.; Archibald, A. T.; Bekki, S.; Braesicke, P.; Di Genova, G.;

Multimodel Estimates of Atmospheric Lifetimes of Long-Lived Ozone-Depleting Substances: Present and Future

NASA Technical Reports Server (NTRS)

Chipperfield, M. P.; Liang, Q.; Strahan, S. E.; Morgenstern, O.; Dhomse, S. S.; Abraham, N. L.; Archibald, A. T.; Bekki, S.; Braesicke, P.; Di Genova, G.;

A stakeholder project to model water temperature under future climate scenarios in the Satus and Toppenish watersheds of the Yakima River Basinin Washington, USA

USGS Publications Warehouse

Graves, D.; Maule, A.

2014-01-01

The goal of this study was to support an assessment of the potential effects of climate change on select natural, social, and economic resources in the Yakima River Basin. A workshop with local stakeholders highlighted the usefulness of projecting climate change impacts on anadromous steelhead (Oncorhynchus mykiss), a fish species of importance to local tribes, fisherman, and conservationists. Stream temperature is an important environmental variable for the freshwater stages of steelhead. For this study, we developed water temperature models for the Satus and Toppenish watersheds, two of the key stronghold areas for steelhead in the Yakima River Basin. We constructed the models with the Stream Network Temperature Model (SNTEMP), a mechanistic approach to simulate water temperature in a stream network. The models were calibrated over the April 15, 2008 to September 30, 2008 period and validated over the April 15, 2009 to September 30, 2009 period using historic measurements of stream temperature and discharge provided by the Yakama Nation Fisheries Resource Management Program. Once validated, the models were run to simulate conditions during the spring and summer seasons over a baseline period (1981–2005) and two future climate scenarios with increased air temperature of 1°C and 2°C. The models simulated daily mean and maximum water temperatures at sites throughout the two watersheds under the baseline and future climate scenarios.

Improving synoptic and intraseasonal variability in CFSv2 via stochastic representation of organized convection

NASA Astrophysics Data System (ADS)

Goswami, B. B.; Khouider, B.; Phani, R.; Mukhopadhyay, P.; Majda, A.

2017-01-01

To better represent organized convection in the Climate Forecast System version 2 (CFSv2), a stochastic multicloud model (SMCM) parameterization is adopted and a 15 year climate run is made. The last 10 years of simulations are analyzed here. While retaining an equally good mean state (if not better) as the parent model, the CFS-SMCM simulation shows significant improvement in the synoptic and intraseasonal variability. The CFS-SMCM provides a better account of convectively coupled equatorial waves and the Madden-Julian oscillation. The CFS-SMCM exhibits improvements in northward and eastward propagation of intraseasonal oscillation of convection including the MJO propagation beyond the maritime continent barrier, which is the Achilles Heel for coarse-resolution global climate models (GCMs). The distribution of precipitation events is better simulated in CFSsmcm and spreads naturally toward high-precipitation events. Deterministic GCMs tend to simulate a narrow distribution with too much drizzling precipitation and too little high-precipitation events.

A Transient Initialization Routine of the Community Ice Sheet Model for the Greenland Ice Sheet

NASA Astrophysics Data System (ADS)

van der Laan, Larissa; van den Broeke, Michiel; Noël, Brice; van de Wal, Roderik

2017-04-01

The Community Ice Sheet Model (CISM) is to be applied in future simulations of the Greenland Ice Sheet under a range of climate change scenarios, determining the sensitivity of the ice sheet to individual climatic forcings. In order to achieve reliable results regarding ice sheet stability and assess the probability of future occurrence of tipping points, a realistic initial ice sheet geometry is essential. The current work describes and evaluates the development of a transient initialization routine, using NGRIP 18O isotope data to create a temperature anomaly field. Based on the latter, surface mass balance components runoff and precipitation are perturbed for the past 125k years. The precipitation and runoff fields originate from a downscaled 1 km resolution version of the regional climate model RACMO2.3 for the period 1961-1990. The result of the initialization routine is a present-day ice sheet with a transient memory of the last glacial-interglacial cycle, which will serve as the future runs' initial condition.

The North American Regional Climate Change Assessment Program (NARCCAP): Status and results

NASA Astrophysics Data System (ADS)

Arritt, R.

2009-04-01

NARCCAP is an international program that is generating projections of climate change for the U.S., Canada, and northern Mexico at decision-relevant regional scales. NARCCAP uses multiple limited-area regional climate models (RCMs) nested within multiple atmosphere-ocean general circulation models (AOGCMs). The use of multiple regional and global models allows us to investigate the uncertainty in model responses to future emissions (here, the A2 SRES scenario). The project also includes global time-slice experiments at the same discretization (50 km) using the GFDL atmospheric model (AM2.1) and the NCAR atmospheric model (CAM3). Phase I of the experiment uses the regional models nested within reanalysis in order to establish uncertainty attributable to the RCMs themselves. Phase II of the project then nests the RCMs within results from the current and future runs of the AOGCMs to explore the cascade of uncertainty from the global to the regional models. Phase I has been completed and the results to be shown include findings that spectral nudging is beneficial in some regions but not in others. Phase II is nearing completion and some preliminary results will be shown.

Application of scenario-neutral methods to quantify impacts of climate change on water resources in East Africa

NASA Astrophysics Data System (ADS)

Ascott, M.; Macdonald, D.; Lapworth, D.; Tindimugaya, C.

2017-12-01

Quantification of the impact of climate change on water resources is essential for future resource planning. Unfortunately, climate change impact studies in African regions are often hindered by the extent in variability in future rainfall predictions, which also diverge from current drying trends. To overcome this limitation, "scenario-neutral" methods have been developed which stress a hydrological system using a wide range of climate futures to build a "climate response surface". We developed a hydrological model and scenario-neutral framework to quantify climate change impacts on river flows in the Katonga catchment, Uganda. Using the lumped catchment model GR4J, an acceptable calibration to historic daily flows (1966 - 2010, NSE = 0.69) was achieved. Using a delta change approach, we then systematically changed rainfall and PET inputs to develop response surfaces for key metrics, developed with Ugandan water resources planners (e.g. Q5, Q95). Scenarios from the CMIP5 models for 2030s and 2050s were then overlain on the response surface. The CMIP5 scenarios show consistent increases in temperature but large variability in rainfall increases, which results in substantial variability in increases in river flows. The developed response surface covers a wide range of climate futures beyond the CMIP5 projections, and can help water resources planners understand the sensitivity of water resource systems to future changes. When future climate scenarios are available, these can be directly overlain on the response surface without the need to re-run the hydrological model. Further work will consider using scenario-neutral approaches in more complex, semi-distributed models (e.g. SWAT), and will consider land use and socioeconomic change.

Piloting Utility Modeling Applications (PUMA): Planning for Climate Change at the Portland Water Bureau

NASA Astrophysics Data System (ADS)

Heyn, K.; Campbell, E.

2016-12-01

The Portland Water Bureau has been studying the anticipated effects of climate change on its primary surface water source, the Bull Run Watershed, since the early 2000's. Early efforts by the bureau were almost exclusively reliant on outside expertise from climate modelers and researchers, particularly those at the Climate Impacts Group (CIG) at the University of Washington. Early work products from CIG formed the basis of the bureau's understanding of the most likely and consequential impacts to the watershed from continued GHG-caused warming. However, by mid-decade, as key supply and demand conditions for the bureau changed, it found it lacked the technical capacity and tools to conduct more refined and updated research to build on the outside analysis it had obtained. Beginning in 2010 through its participation in the Pilot Utility Modeling Applications (PUMA) project, the bureau identified and began working to address the holes in its technical and institutional capacity by embarking on a process to assess and select a hydrologic model while obtaining downscaled climate change data to utilize within it. Parallel to the development of these technical elements, the bureau made investments in qualified staff to lead the model selection, development and utilization, while working to establish productive, collegial and collaborative relationships with key climate research staff at the Oregon Climate Change Research Institute (OCCRI), the University of Washington and the University of Idaho. This presentation describes the learning process of a major metropolitan area drinking water utility as its approach to addressing the complex problem of climate change evolves, matures, and begins to influence broader aspects of the organization's planning efforts.

Poleward shift and weakening of summer season synoptic activity over India in a warming climate

NASA Astrophysics Data System (ADS)

Ravindran, A. M.; Sandeep, S.; Boos, W. R.; TP, S.; Praveen, V.

2017-12-01

One of the main components of the Indian summer monsoon is the presence of low intensity cyclonic systems popularly known as Low Pressure Systems (LPS), which contribute more than half of the precipitation received over the fertile Central Indian region. An average of 13 (±2.5) storms develop each boreal summer, with most originating over the Bay of Bengal (BoB) and adjoining land. These systems typically follow a north-west track along the monsoon trough. Despite its significance, the future variability of these storms is not studied, due to the inadequate representation of these systems in current generation climate models. A series of numerical experiments are performed here using the High Resolution Atmospheric Model (HiRAM) with a horizontal grid spacing of 50 km globally to simulate these rain-bearing systems. One set of simulations represents the historical (HIST) period and the other a late 21st century climate scenario based on the strongest Representative Concentration Pathway (RCP8.5). Four ensemble members of these simulations are run, with sea surface temperatures (SSTs) taken from different CMIP5 GCMs selected for their skill in simulating the Indian monsoon. In addition, ten ensemble members of `decadal' experiments are run for both HIST and RCP8.5 to assess model uncertainty, in which the model is forced with annual cycles of decadal mean SSTs. We show that the strength of monsoon LPS activity would decline as much as 50% by the end of the 21st century, under business as usual emission scenario. The overall reduction in the LPS activity is contributed by a 60% decrease in the frequency of storms over the Bay of Bengal, while the weaker systems that form over the land has increased 10% in a warmer climate. Further analysis suggests that a relatively slower rate of warming over the Bay of Bengal compared to the surrounding regions has resulted in an enhanced moist stability over the main genesis region of LPS, which in turn suppressed the growth of these storms in a warmer climate. The change in extreme precipitation may be mentioned as a consequence of ocean-to-land shift in LPS activity.

Regional Climate Modeling over the Glaciated Regions of the Canadian High Arctic

NASA Astrophysics Data System (ADS)

Gready, Benjamin P.

The Canadian Arctic Islands (CAI) contain the largest concentration of terrestrial ice outside of the continental ice sheets. Mass loss from this region has recently increased sharply due to above average summer temperatures. Thus, increasing the understanding of the mechanisms responsible for mass loss from this region is critical. Previously, Regional Climate Models (RCMs) have been utilized to estimate climatic balance over Greenland and Antarctica. This method offers the opportunity to study a full suite of climatic variables over extensive spatially distributed grids. However, there are doubts of the applicability of such models to the CAI, given the relatively complex topography of the CAI. To test RCMs in the CAI, the polar version of the regional climate model MM5 was run at high resolution over Devon Ice Cap. At low altitudes, residuals (computed through comparisons with in situ measurements) in the net radiation budget were driven primarily by residuals in net shortwave (NSW) radiation. Residuals in NSW are largely due to inaccuracies in modeled cloud cover and modeled albedo. Albedo on glaciers and ice sheets is oversimplified in Polar MM5 and its successor, the Polar version of the Weather Research and Forecast model (Polar WRF), and is an obvious place for model improvement. Subsequently, an inline parameterization of albedo for Polar WRF was developed as a function of the depth, temperature and age of snow. The parameterization was able to reproduce elevation gradients of seasonal mean albedo derived from satellite albedo measurements (MODIS MOD10A1 daily albedo), on the western slope of the Greenland Ice Sheet for three years. Feedbacks between modelled albedo and modelled surface energy budget components were identified. The shortwave radiation flux feeds back positively with changes to albedo, whereas the longwave, turbulent and ground energy fluxes all feed back negatively, with a maximum combined magnitude of two thirds of the shortwave feedback magnitude. These strong feedbacks demonstrate that an accurate albedo parameterization must be run inline within an RCM, to accurately quantify the net surface energy budget of an ice sheet. Finally, Polar WRF, with the improved albedo parameterization, was used to simulate climatic balance over the Queen Elizabeth Islands for the summers of 2001 to 2008. Climatic balance was derived from the output using energy balance and temperature index melt models. Regional mass balance was calculated by combining climatic balance with estimates of iceberg discharge. Mass balance estimates from the model agreed, within the bounds of uncertainty, with estimates from previous studies, thus supporting the assertion that mass loss from the QEI accelerated during the first decade of the 21st century. Melt rates on the seven major icecaps of the QEI became more correlated to one another during the period 2001-2008. However, precipitation became less correlated from 2003-2008. These observations are coincident with dramatic increases in melt on all of the ice caps, and it is speculated that both are caused by decreases in the scale of disturbances delivering precipitation to the region over time.

Characterization of extreme precipitation within atmospheric river events over California

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

Jeon, S.; Prabhat,; Byna, S.

Atmospheric rivers (ARs) are large, spatially coherent weather systems with high concentrations of elevated water vapor. These systems often cause severe downpours and flooding over the western coastal United States – and with the availability of more atmospheric moisture in the future under global warming we expect ARs to play an important role as potential causes of extreme precipitation changes. Therefore, we aim to investigate changes in extreme precipitation properties correlated with AR events in a warmer climate, which are large-scale meteorological patterns affecting the weather and climate of California. We have recently developed the TECA (Toolkit for Extreme Climatemore » Analysis) software for automatically identifying and tracking features in climate data sets. Specifically, we can now identify ARs that make landfall on the western coast of North America. Based on this detection procedure, we can investigate the impact of ARs by exploring the spatial extent of AR precipitation using climate model (CMIP5) simulations and characterize spatial patterns of dependence for future projections between AR precipitation extremes under climate change within the statistical framework. Our results show that AR events in the future RCP (Representative Concentration Pathway)8.5 scenario (2076–2100) tend to produce heavier rainfall with higher frequency and longer days than events from the historical run (1981–2005). We also find that the dependence between extreme precipitation events has a shorter spatial range, within localized areas in California, under the high future emissions scenario than under the historical run.« less

Characterization of extreme precipitation within atmospheric river events over California

DOE PAGES

Jeon, S.; Prabhat,; Byna, S.; ...

2015-11-17

Atmospheric rivers (ARs) are large, spatially coherent weather systems with high concentrations of elevated water vapor. These systems often cause severe downpours and flooding over the western coastal United States – and with the availability of more atmospheric moisture in the future under global warming we expect ARs to play an important role as potential causes of extreme precipitation changes. Therefore, we aim to investigate changes in extreme precipitation properties correlated with AR events in a warmer climate, which are large-scale meteorological patterns affecting the weather and climate of California. We have recently developed the TECA (Toolkit for Extreme Climatemore » Analysis) software for automatically identifying and tracking features in climate data sets. Specifically, we can now identify ARs that make landfall on the western coast of North America. Based on this detection procedure, we can investigate the impact of ARs by exploring the spatial extent of AR precipitation using climate model (CMIP5) simulations and characterize spatial patterns of dependence for future projections between AR precipitation extremes under climate change within the statistical framework. Our results show that AR events in the future RCP (Representative Concentration Pathway)8.5 scenario (2076–2100) tend to produce heavier rainfall with higher frequency and longer days than events from the historical run (1981–2005). We also find that the dependence between extreme precipitation events has a shorter spatial range, within localized areas in California, under the high future emissions scenario than under the historical run.« less

An assessment of irrigation needs and crop yield for the United States under potential climate changes

USGS Publications Warehouse

Brumbelow, Kelly; Georgakakos, Aris P.

2000-01-01

Past assessments of climate change on U.S. agriculture have mostly focused on changes in crop yield. Few studies have included the entire conterminous U.S., and few studies have assessed changing irrigation requirements. None have included the effects of changing soil moisture characteristics as determined by changing climatic forcing. This study assesses changes in irrigation requirements and crop yields for five crops in the areas of the U.S. where they have traditionally been grown. Physiologically-based crop models are used to incorporate inputs of climate, soils, agricultural management, and drought stress tolerance. Soil moisture values from a macroscale hydrologic model run under a future climate scenario are used to initialize soil moisture content at the beginning of each growing season. Historical crop yield data is used to calibrate model parameters and determine locally acceptable drought stress as a management parameter. Changes in irrigation demand and crop yield are assessed for both means and extremes by comparing results for atmospheric forcing close to the present climate with those for a future climate scenario. Assessments using the Canadian Center for Climate Modeling and Analysis General Circulation Model (CGCM1) indicate greater irrigation demands in the southern U.S. and decreased irrigation demands in the northern and western U.S. Crop yields typically increase except for winter wheat in the southern U.S. and corn. Variability in both irrigation demands and crop yields increases in most cases. Assessment results for the CGCM1 climate scenario are compared to those for the Hadley Centre for Climate Prediction and Research GCM (HadCM2) scenario for southwestern Georgia. The comparison shows significant differences in irrigation and yield trends, both in magnitude and direction. The differences reflect the high forecast uncertainty of current GCMs. Nonetheless, both GCMs indicate higher variability in future climatic forcing and, consequently, in the response of agricultural systems.

The significance of the interception in a Thornthwaite-type monthly step water balance model in context of the climate change

NASA Astrophysics Data System (ADS)

Herceg, András; Kalicz, Péter; Kisfaludi, Balázs

2017-04-01

The hydrological impacts of the climate change can be dramatic. Our main purpose is the methodical improvement of a previously established Thornthwaite-type monthly step water balance model, which takes the interception item into account, and compare the results of the evapotranspiration and the soil moisture projections for the 21st century of the original and the upgraded models. Both of the models will be calibrated and validated (using remote-sensed actual evapotranspiration data, called CREMAP) and requires only temperature and precipitation time series as inputs. The projections based on 4 bias-corrected regional climate models databases (FORESEE), and the 3 investigation periods are: 2015-2045, 2045-2075, and 2070-2100. The key parameter is the water storage capacity of the soil, which can be also calibrated using the actual evapotranspiration data. The maximal rooting depth is determinable if the physical properties of the soil are available. The interception can be ranges from 5-40% of gross precipitation, which rate are differing in the various plant communities. Generally, the forests canopy intercepts considerable amounts of rainfall and evaporates back into the atmosphere during and after precipitation event. Leaf area index (LAI) is one of the most significant factor, which determine the canopies storage capacity. Here, MODIS sensor based LAI time series are applied to estimate the storage capacity. A forest covered experimental catchment is utilized for testing the models near to Sopron, Hungary. The projections will expected to demonstrate increasing actual evapotranspiration values, but decreasing trends for the 10 percentile minimum soil moisture values at the end of the 21st century in both model runs. The seasonal periodicity of evapotranspiration may demonstrates the maximums in June or July, while in case of the soil moisture it may shows minimum values in autumn. With the comparison of the two model runs, we expect lower soil water storage capacity (SOILMAX) values for the upgraded model and for that very reason lower soil moisture reservoir as well. Consequently, we will possess a more reliable model, which represents a closer approximation of the reality. The water stress analysis (relative extractable water and soil water deficit) may indicate that more significant water stress assumed to occur in case of the upgraded model run during the 21st century. This research has been supported by the Agroclimate.2 VKSZ_12-1-2013-0034 project, and the second author's work was also supported by the János Bolyai Scholarship of the Hungarian Academy of Sciences.

Weak simulated extratropical responses to complete tropical deforestation

USGS Publications Warehouse

Findell, K.L.; Knutson, T.R.; Milly, P.C.D.

2006-01-01

The Geophysical Fluid Dynamics Laboratory atmosphere-land model version 2 (AM2/LM2) coupled to a 50-m-thick slab ocean model has been used to investigate remote responses to tropical deforestation. Magnitudes and significance of differences between a control run and a deforested run are assessed through comparisons of 50-yr time series, accounting for autocorrelation and field significance. Complete conversion of the broadleaf evergreen forests of South America, central Africa, and the islands of Oceania to grasslands leads to highly significant local responses. In addition, a broad but mild warming is seen throughout the tropical troposphere (<0.2??C between 700 and 150 mb), significant in northern spring and summer. However, the simulation results show very little statistically significant response beyond the Tropics. There are no significant differences in any hydroclimatic variables (e.g., precipitation, soil moisture, evaporation) in either the northern or the southern extratropics. Small but statistically significant local differences in some geopotential height and wind fields are present in the southeastern Pacific Ocean. Use of the same statistical tests on two 50-yr segments of the control run show that the small but significant extratropical differences between the deforested run and the control run are similar in magnitude and area to the differences between nonoverlapping segments of the control run. These simulations suggest that extratropical responses to complete tropical deforestation are unlikely to be distinguishable from natural climate variability.

Developing an Integrated Model Framework for the Assessment of Sustainable Agricultural Residue Removal Limits for Bioenergy Systems

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

David Muth, Jr.; Jared Abodeely; Richard Nelson

Agricultural residues have significant potential as a feedstock for bioenergy production, but removing these residues can have negative impacts on soil health. Models and datasets that can support decisions about sustainable agricultural residue removal are available; however, no tools currently exist capable of simultaneously addressing all environmental factors that can limit availability of residue. The VE-Suite model integration framework has been used to couple a set of environmental process models to support agricultural residue removal decisions. The RUSLE2, WEPS, and Soil Conditioning Index models have been integrated. A disparate set of databases providing the soils, climate, and management practice datamore » required to run these models have also been integrated. The integrated system has been demonstrated for two example cases. First, an assessment using high spatial fidelity crop yield data has been run for a single farm. This analysis shows the significant variance in sustainably accessible residue across a single farm and crop year. A second example is an aggregate assessment of agricultural residues available in the state of Iowa. This implementation of the integrated systems model demonstrates the capability to run a vast range of scenarios required to represent a large geographic region.« less

Assessing the impact of aerosol-atmosphere interactions in convection-permitting regional climate simulations: the Rolf medicane in 2011

NASA Astrophysics Data System (ADS)

José Gómez-Navarro, Juan; María López-Romero, José; Palacios-Peña, Laura; Montávez, Juan Pedro; Jiménez-Guerrero, Pedro

2017-04-01

A critical challenge for assessing regional climate change projections relies on improving the estimate of atmospheric aerosol impact on clouds and reducing the uncertainty associated with the use of parameterizations. In this sense, the horizontal grid spacing implemented in state-of-the-art regional climate simulations is typically 10-25 kilometers, meaning that very important processes such as convective precipitation are smaller than a grid box, and therefore need to be parameterized. This causes large uncertainties, as closure assumptions and a number of parameters have to be established by model tuning. Convection is a physical process that may be strongly conditioned by atmospheric aerosols, although the solution of aerosol-cloud interactions in warm convective clouds remains nowadays a very important scientific challenge, rendering parametrization of these complex processes an important bottleneck that is responsible from a great part of the uncertainty in current climate change projections. Therefore, the explicit simulation of convective processes might improve the quality and reliability of the simulations of the aerosol-cloud interactions in a wide range of atmospheric phenomena. Particularly over the Mediterranean, the role of aerosol particles is very important, being this a crossroad that fuels the mixing of particles from different sources (sea-salt, biomass burning, anthropogenic, Saharan dust, etc). Still, the role of aerosols in extreme events in this area such as medicanes has been barely addressed. This work aims at assessing the role of aerosol-atmosphere interaction in medicanes with the help of the regional chemistry/climate on-line coupled model WRF-CHEM run at a convection-permitting resolution. The analysis is exemplary based on the "Rolf" medicane (6-8 November 2011). Using this case study as reference, four sets of simulations are run with two spatial resolutions: one at a convection-permitting configuration of 4 km, and other at the lower resolution of 12 km, in whose case the convection has to be parameterized. Each configuration is used to produce two simulations, including and not including aerosol-radiation-cloud interactions. The comparison of the simulated output at different scales allows to evaluate the impact of sub-grid scale mixing of precursors on aerosol production. By focusing on these processes at different resolutions, the differences between convection-permitting models running at resolutions of 4 km to 12 km can be explored. Preliminary results indicate that the inclusion of aerosol effects may indeed impact the severity of this simulated medicane, especially sea salt aerosols, and leads to important spatial shifts and differences in intensity of surface precipitation.

Arctic climate response to geoengineering with stratospheric sulfate aerosols

NASA Astrophysics Data System (ADS)

McCusker, K. E.; Battisti, D. S.; Bitz, C. M.

2010-12-01

Recent warming and record summer sea-ice area minimums have spurred expressions of concern for arctic ecosystems, permafrost, and polar bear populations, among other things. Geoengineering by stratospheric sulfate aerosol injections to deliberately cancel the anthropogenic temperature rise has been put forth as a possible solution to restoring Arctic (and global) climate to modern conditions. However, climate is particularly sensitive in the northern high latitudes, responding easily to radiative forcing changes. To that end, we explore the extent to which tropical injections of stratospheric sulfate aerosol can accomplish regional cancellation in the Arctic. We use the Community Climate System Model version 3 global climate model to execute simulations with combinations of doubled CO2 and imposed stratospheric sulfate burdens to investigate the effects on high latitude climate. We further explore the sensitivity of the polar climate to ocean dynamics by running a suite of simulations with and without ocean dynamics, transiently and to equilibrium respectively. We find that, although annual, global mean temperature cancellation is accomplished, there is over-cooling on land in Arctic summer, but residual warming in Arctic winter, which is largely due to atmospheric circulation changes. Furthermore, the spatial extent of these features and their concurrent impacts on sea-ice properties are modified by the inclusion of ocean dynamical feedbacks.

TECA: Petascale pattern recognition for climate science

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

Prabhat, .; Byna, Surendra; Vishwanath, Venkatram

Climate Change is one of the most pressing challenges facing humanity in the 21st century. Climate simulations provide us with a unique opportunity to examine effects of anthropogenic emissions. Highresolution climate simulations produce “Big Data”: contemporary climate archives are ≈ 5PB in size and we expect future archives to measure on the order of Exa-Bytes. In this work, we present the successful application of TECA (Toolkit for Extreme Climate Analysis) framework, for extracting extreme weather patterns such as Tropical Cyclones, Atmospheric Rivers and Extra-Tropical Cyclones from TB-sized simulation datasets. TECA has been run at full-scale on Cray XE6 and IBMmore » BG/Q systems, and has reduced the runtime for pattern detection tasks from years to hours. TECA has been utilized to evaluate the performance of various computational models in reproducing the statistics of extreme weather events, and for characterizing the change in frequency of storm systems in the future.« less

Selection of meteorological conditions to apply in an Ecotron facility

NASA Astrophysics Data System (ADS)

Leemans, Vincent; De Cruz, Lesley; Dumont, Benjamin; Hamdi, Rafiq; Delaplace, Pierre; Heinesh, Bernard; Garré, Sarah; Verheggen, François; Theodorakopoulos, Nicolas; Longdoz, Bernard

2017-04-01

This presentation aims to propose a generic method to produce meteorological input data that is useful for climate research infrastructures such as an Ecotron, where researchers will face the need to generate representative actual or future climatic conditions. Depending on the experimental objectives and the research purposes, typical conditions or more extreme values such as dry or wet climatic scenarios might be requested. Four variables were considered here, the near-surface air temperature, the near-surface relative humidity, the cloud cover and precipitation. The meteorological datasets, among which a specific meteorological year can be picked up, are produced by the ALARO-0 model from the RMIB (Royal Meteorological Institute of Belgium). Two future climate scenarios (RCP 4.5 and 8.5) and two time periods (2041-2070 and 2071-2100) were used as well as a historical run of the model (1981-2010) which is used as a reference. When the data from a historical run were compared to the observed historical data, biases were noticed. A linear correction was proposed for all the variables except for precipitation, for which a non-linear correction (using a power function) was chosen to maintain a zero-precipitation occurrences. These transformations were able to remove most of the differences between the observed and historical run of the model for the means and for the standard deviations. For the relative humidity, because of non-linearities, only one half of the average bias was corrected and a different path might have to be chosen. For the selection of a meteorological year, a position and a dispersion parameter have been proposed to characterise each meteorological year for each variable. For precipitation, a third parameter quantifying the importance of dry and wet periods has been defined. In order to select a specific climate, for each of these nine parameters the experimenter should provide a percentile and a weight to prioritize the importance of each variable in the process of a global climate selection. The proposed algorithm computed the weighted distance for each year between the parameters and the point representing the position of the percentile in the nine-dimensional space. The five closest values were then selected and represented in different graphs. The proposed method is able to provide a decision aid in the selection of the meteorological conditions to be generated within an Ecotron. However, with a limited number of years available in each case (thirty years for each RCP and each time period), there is no perfect match and the ultimate trade-off will be the responsibility of the researcher. For typical years, close to the median, the relative frequency is higher and the trade-off is more easy than for more extreme years where the relative frequency is low.

The impact of future forest dynamics on climate: interactive effects of changing vegetation and disturbance regimes

PubMed Central

Thom, Dominik; Rammer, Werner; Seidl, Rupert

2018-01-01

Currently, the temperate forest biome cools the earth’s climate and dampens anthropogenic climate change. However, climate change will substantially alter forest dynamics in the future, affecting the climate regulation function of forests. Increasing natural disturbances can reduce carbon uptake and evaporative cooling, but at the same time increase the albedo of a landscape. Simultaneous changes in vegetation composition can mitigate disturbance impacts, but also influence climate regulation directly (e.g., via albedo changes). As a result of a number of interactive drivers (changes in climate, vegetation, and disturbance) and their simultaneous effects on climate-relevant processes (carbon exchange, albedo, latent heat flux) the future climate regulation function of forests remains highly uncertain. Here we address these complex interactions to assess the effect of future forest dynamics on the climate system. Our specific objectives were (1) to investigate the long-term interactions between changing vegetation composition and disturbance regimes under climate change, (2) to quantify the response of climate regulation to changes in forest dynamics, and (3) to identify the main drivers of the future influence of forests on the climate system. We investigated these issues using the individual-based forest landscape and disturbance model (iLand). Simulations were run over 200 yr for Kalkalpen National Park (Austria), assuming different future climate projections, and incorporating dynamically responding wind and bark beetle disturbances. To consistently assess the net effect on climate the simulated responses of carbon exchange, albedo, and latent heat flux were expressed as contributions to radiative forcing. We found that climate change increased disturbances (+27.7% over 200 yr) and specifically bark beetle activity during the 21st century. However, negative feedbacks from a simultaneously changing tree species composition (+28.0% broadleaved species) decreased disturbance activity in the long run (−10.1%), mainly by reducing the host trees available for bark beetles. Climate change and the resulting future forest dynamics significantly reduced the climate regulation function of the landscape, increasing radiative forcing by up to +10.2% on average over 200 yr. Overall, radiative forcing was most strongly driven by carbon exchange. We conclude that future changes in forest dynamics can cause amplifying climate feedbacks from temperate forest ecosystems. PMID:29628526

An approach to secure weather and climate models against hardware faults

NASA Astrophysics Data System (ADS)

Düben, Peter D.; Dawson, Andrew

2017-03-01

Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelization to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. In this paper, we present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform model simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13 % for the shallow water model.

Concept for Future Data Services at the Long-Term Archive of WDCC combining DOIs with common PIDs

NASA Astrophysics Data System (ADS)

Stockhause, Martina; Weigel, Tobias; Toussaint, Frank; Höck, Heinke; Thiemann, Hannes; Lautenschlager, Michael

2013-04-01

The World Data Center for Climate (WDCC) hosted at the German Climate Computing Center (DKRZ) maintains a long-term archive (LTA) of climate model data as well as observational data. WDCC distinguishes between two types of LTA data: Structured data: Data output of an instrument or of a climate model run consists of numerous, highly structured individual datasets in a uniform format. Part of these data is also published on an ESGF (Earth System Grid Federation) data node. Detailed metadata is available allowing for fine-grained user-defined data access. Unstructured data: LTA data of finished scientific projects are in general unstructured and consist of datasets of different formats, different sizes, and different contents. For these data compact metadata is available as content information. The structured data is suitable for WDCC's DataCite DOI process, the project data only in exceptional cases. The DOI process includes a thorough quality control process of technical as well as scientific aspects by the publication agent and the data creator. DOIs are assigned to data collections appropriate to be cited in scientific publications, like a simulation run. The data collection is defined in agreement with the data creator. At the moment there is no possibility to identify and cite individual datasets within this DOI data collection analogous to the citation of chapters in a book. Also missing is a compact citation regulation for a user-specified collection of data. WDCC therefore complements its existing LTA/DOI concept by Persistent Identifier (PID) assignment to datasets using Handles. In addition to data identification for internal and external use, the concept of PIDs allows to define relations among PIDs. Such structural information is stored as key-value pair directly in the handles. Thus, relations provide basic provenance or lineage information, even if part of the data like intermediate results are lost. WDCC intends to use additional PIDs on metadata entities with a relation to the data PID(s). These add background information on the data creation process (e.g. descriptions of experiment, model, model set-up, and platform for the model run etc.) to the data. These pieces of additional information increase the re-usability of the archived model data, significantly. Other valuable additional information for scientific collaboration could be added by the same mechanism, like quality information and annotations. Apart from relations among data and metadata entities, PIDs on collections are advantageous for model data: Collections allow for persistent references to single datasets or subsets of data assigned a DOI, Data objects and additional information objects can be consistently connected via relations (provenance, creation, quality information for data),

High Resolution Simulations of Future Climate in West Africa Using a Variable-Resolution Atmospheric Model

NASA Astrophysics Data System (ADS)

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

2013-12-01

In previous work demonstrated the application of a var¬iable-resolution global atmospheric model, the conformal-cubic atmospheric model (CCAM), across a wide range of spatial and time scales to investigate the ability of the model to provide realistic simulations of present-day climate and plausible projections of future climate change over sub-Saharan Africa. By applying the model in stretched-grid mode the versatility of the model dynamics, numerical formulation and physical parameterizations to function across a range of length scales over the region of interest, was also explored. We primarily used CCAM to illustrate the capability of the model to function as a flexible downscaling tool at the climate-change time scale. Here we report on additional long term climate projection studies performed by downscaling at much higher resolutions (8 Km) over an area that stretches from just south of Sahara desert to the southern coast of the Niger Delta and into the Gulf of Guinea. To perform these simulations, CCAM was provided with synoptic-scale forcing of atmospheric circulation from 2.5 deg resolution NCEP reanalysis at 6-hourly interval and SSTs from NCEP reanalysis data uses as lower boundary forcing. CCAM 60 Km resolution downscaled to 8 Km (Schmidt factor 24.75) then 8 Km resolution simulation downscaled to 1 Km (Schmidt factor 200) over an area approximately 50 Km x 50 Km in the southern Lake Chad Basin (LCB). Our intent in conducting these high resolution model runs was to obtain a deeper understanding of linkages between the projected future climate and the hydrological processes that control the surface water regime in this part of sub-Saharan Africa.

Climate Expressions in Cellulose Isotopologues Over the Southeast Asian Monsoon Domain

NASA Astrophysics Data System (ADS)

Herzog, M. G.; LeGrande, A. N.; Anchukaitis, K. J.

2013-12-01

Southeast Asia experiences a highly variant climate, strongly influenced by the Southeast Asian monsoon. Oxygen isotopes in the alpha cellulose of tree rings can be used as a proxy measure of climate, but it is not clear which parameter (precipitation, temperature, water vapor, etc) is the most influential. Earlier forward models using observed meteorological data have been successful simulating tree ring cellulose oxygen isotopes in the tropics. However, by creating a cellulose oxygen isotope model which uses input data from GISS ModelE climate runs, we are able to reduce model variability and integrate δ18O in tree ring cellulose over the entire monsoon domain for the past millennium. Simulated timescales of δ18O in cellulose show a consistent annual cycle, allowing confidence in the identification of interdecadal and interannual climate variability. By comparing paleoclimate data with Global Circulation Model (GCM) outputs and a forward tree cellulose δ18O model, this study explores how δ18O can be used as a proxy measure of the monsoon on both local and regional scales. Simulated δ18O in soil water and δ18O in water vapor were found to explain the most variability in the paleoclimate data. Precipitation amount and temperature held little significance. Our results suggest that δ18O in tree cellulose is most influenced by regional controls directly related to cellulose production. top: monthly modeled output for d18O cellulose center: annually averaged model output of d18O cellulose bottom: observed monthly paleoproxy data for d18O cellulose

Validation of two (parametric vs non-parametric) daily weather generators

NASA Astrophysics Data System (ADS)

Dubrovsky, M.; Skalak, P.

2015-12-01

As the climate models (GCMs and RCMs) fail to satisfactorily reproduce the real-world surface weather regime, various statistical methods are applied to downscale GCM/RCM outputs into site-specific weather series. The stochastic weather generators are among the most favourite downscaling methods capable to produce realistic (observed-like) meteorological inputs for agrological, hydrological and other impact models used in assessing sensitivity of various ecosystems to climate change/variability. To name their advantages, the generators may (i) produce arbitrarily long multi-variate synthetic weather series representing both present and changed climates (in the latter case, the generators are commonly modified by GCM/RCM-based climate change scenarios), (ii) be run in various time steps and for multiple weather variables (the generators reproduce the correlations among variables), (iii) be interpolated (and run also for sites where no weather data are available to calibrate the generator). This contribution will compare two stochastic daily weather generators in terms of their ability to reproduce various features of the daily weather series. M&Rfi is a parametric generator: Markov chain model is used to model precipitation occurrence, precipitation amount is modelled by the Gamma distribution, and the 1st order autoregressive model is used to generate non-precipitation surface weather variables. The non-parametric GoMeZ generator is based on the nearest neighbours resampling technique making no assumption on the distribution of the variables being generated. Various settings of both weather generators will be assumed in the present validation tests. The generators will be validated in terms of (a) extreme temperature and precipitation characteristics (annual and 30-years extremes and maxima of duration of hot/cold/dry/wet spells); (b) selected validation statistics developed within the frame of VALUE project. The tests will be based on observational weather series from several European stations available from the ECA&D database. Acknowledgements: The weather generator is developed and validated within the frame of projects WG4VALUE (sponsored by the Ministry of Education, Youth and Sports of CR), and VALUE (COST ES 1102 action).

Nutrient pollution mitigation measures across Europe are resilient under future climate

NASA Astrophysics Data System (ADS)

Wade, Andrew; Skeffington, Richard; Couture, Raoul; Erlandsson, Martin; Groot, Simon; Halliday, Sarah; Harezlak, Valesca; Hejzlar, Joseph; Jackson-Blake, Leah; Lepistö, Ahti; Papastergiadou, Eva; Psaltopoulos, Demetrios; Riera, Joan; Rankinen, Katri; Skuras, Dimitris; Trolle, Dennis; Whitehead, Paul; Dunn, Sarah; Bucak, Tuba

2016-04-01

The key results from the application of catchment-scale biophysical models to assess the likely effectiveness of nutrient pollution mitigation measures set in the context of projected land management and climate change are presented. The assessment is based on the synthesis of modelled outputs of daily river flow, river and lake nitrogen and phosphorus concentrations, and lake chlorophyll-a, for baseline (1981-2010) and scenario (2031-2060) periods for nine study sites across Europe. Together the nine sites represent a sample of key climate and land management types. The robustness and uncertainty in the daily, seasonal and long-term modelled outputs was assessed prior to the scenario runs. Credible scenarios of land-management changes were provided by social scientists and economists familiar with each study site, whilst likely mitigation measures were derived from local stakeholder consultations and cost-effectiveness assessments. Modelled mitigation options were able to reduce nutrient concentrations, and there was no evidence here that they were less effective under future climate. With less certainty, mitigation options could affect the ecological status of waters at these sites in a positive manner, leading to improvement in Water Framework Directive status at some sites. However, modelled outcomes for sites in southern Europe highlighted that increased evaporation and decreased precipitation will cause much lower flows leading to adverse impacts of river and lake ecology. Uncertainties in the climate models, as represented by three GCM-RCM combinations, did not affect this overall picture much.

The contribution of natural variability to GCM bias: Can we effectively bias-correct climate projections?

NASA Astrophysics Data System (ADS)

McAfee, S. A.; DeLaFrance, A.

2017-12-01

Investigating the impacts of climate change often entails using projections from inherently imperfect general circulation models (GCMs) to drive models that simulate biophysical or societal systems in great detail. Error or bias in the GCM output is often assessed in relation to observations, and the projections are adjusted so that the output from impacts models can be compared to historical or observed conditions. Uncertainty in the projections is typically accommodated by running more than one future climate trajectory to account for differing emissions scenarios, model simulations, and natural variability. The current methods for dealing with error and uncertainty treat them as separate problems. In places where observed and/or simulated natural variability is large, however, it may not be possible to identify a consistent degree of bias in mean climate, blurring the lines between model error and projection uncertainty. Here we demonstrate substantial instability in mean monthly temperature bias across a suite of GCMs used in CMIP5. This instability is greatest in the highest latitudes during the cool season, where shifts from average temperatures below to above freezing could have profound impacts. In models with the greatest degree of bias instability, the timing of regional shifts from below to above average normal temperatures in a single climate projection can vary by about three decades, depending solely on the degree of bias assessed. This suggests that current bias correction methods based on comparison to 20- or 30-year normals may be inappropriate, particularly in the polar regions.

Projected Climate Impacts to South African Maize and Wheat Production in 2055: A Comparison of Empirical and Mechanistic Modeling Approaches

NASA Technical Reports Server (NTRS)

Estes, Lyndon D.; Beukes, Hein; Bradley, Bethany A.; Debats, Stephanie R.; Oppenheimer, Michael; Ruane, Alex C.; Schulze, Roland; Tadross, Mark

2013-01-01

Crop model-specific biases are a key uncertainty affecting our understanding of climate change impacts to agriculture. There is increasing research focus on intermodel variation, but comparisons between mechanistic (MMs) and empirical models (EMs) are rare despite both being used widely in this field. We combined MMs and EMs to project future (2055) changes in the potential distribution (suitability) and productivity of maize and spring wheat in South Africa under 18 downscaled climate scenarios (9 models run under 2 emissions scenarios). EMs projected larger yield losses or smaller gains than MMs. The EMs' median-projected maize and wheat yield changes were 3.6% and 6.2%, respectively, compared to 6.5% and 15.2% for the MM. The EM projected a 10% reduction in the potential maize growing area, where the MM projected a 9% gain. Both models showed increases in the potential spring wheat production region (EM = 48%, MM = 20%), but these results were more equivocal because both models (particularly the EM) substantially overestimated the extent of current suitability. The substantial water-use efficiency gains simulated by the MMs under elevated CO2 accounted for much of the EMMM difference, but EMs may have more accurately represented crop temperature sensitivities. Our results align with earlier studies showing that EMs may show larger climate change losses than MMs. Crop forecasting efforts should expand to include EMMM comparisons to provide a fuller picture of crop-climate response uncertainties.

The Use of Statistical Downscaling to Project Regional Climate Changes as they Relate to Future Energy Production

NASA Astrophysics Data System (ADS)

Werth, D. W.; O'Steen, L.; Chen, K.; Altinakar, M. S.; Garrett, A.; Aleman, S.; Ramalingam, V.

2010-12-01

Global climate change has the potential for profound impacts on society, and poses significant challenges to government and industry in the areas of energy security and sustainability. Given that the ability to exploit energy resources often depends on the climate, the possibility of climate change means we cannot simply assume that the untapped potential of today will still exist in the future. Predictions of future climate are generally based on global climate models (GCMs) which, due to computational limitations, are run at spatial resolutions of hundreds of kilometers. While the results from these models can predict climatic trends averaged over large spatial and temporal scales, their ability to describe the effects of atmospheric phenomena that affect weather on regional to local scales is inadequate. We propose the use of several optimized statistical downscaling techniques that can infer climate change at the local scale from coarse resolution GCM predictions, and apply the results to assess future sustainability for two sources of energy production dependent on adequate water resources: nuclear power (through the dissipation of waste heat from cooling towers, ponds, etc.) and hydroelectric power. All methods will be trained with 20th century data, and applied to data from the years 2040-2049 to get the local-scale changes. Models of cooling tower operation and hydropower potential will then use the downscaled data to predict the possible changes in energy production, and the implications of climate change on plant siting, design, and contribution to the future energy grid can then be examined.

An approach to secure weather and climate models against hardware faults

NASA Astrophysics Data System (ADS)

Düben, Peter; Dawson, Andrew

2017-04-01

Enabling Earth System models to run efficiently on future supercomputers is a serious challenge for model development. Many publications study efficient parallelisation to allow better scaling of performance on an increasing number of computing cores. However, one of the most alarming threats for weather and climate predictions on future high performance computing architectures is widely ignored: the presence of hardware faults that will frequently hit large applications as we approach exascale supercomputing. Changes in the structure of weather and climate models that would allow them to be resilient against hardware faults are hardly discussed in the model development community. We present an approach to secure the dynamical core of weather and climate models against hardware faults using a backup system that stores coarse resolution copies of prognostic variables. Frequent checks of the model fields on the backup grid allow the detection of severe hardware faults, and prognostic variables that are changed by hardware faults on the model grid can be restored from the backup grid to continue model simulations with no significant delay. To justify the approach, we perform simulations with a C-grid shallow water model in the presence of frequent hardware faults. As long as the backup system is used, simulations do not crash and a high level of model quality can be maintained. The overhead due to the backup system is reasonable and additional storage requirements are small. Runtime is increased by only 13% for the shallow water model.

SWATShare- A Platform for Collaborative Hydrology Research and Education with Cyber-enabled Sharing, Running and Visualization of SWAT Models

NASA Astrophysics Data System (ADS)

Rajib, M. A.; Merwade, V.; Song, C.; Zhao, L.; Kim, I. L.; Zhe, S.

2014-12-01

Setting up of any hydrologic model requires a large amount of efforts including compilation of all the data, creation of input files, calibration and validation. Given the amount of efforts involved, it is possible that models for a watershed get created multiple times by multiple groups or organizations to accomplish different research, educational or policy goals. To reduce the duplication of efforts and enable collaboration among different groups or organizations around an already existing hydrology model, a platform is needed where anyone can search for existing models, perform simple scenario analysis and visualize model results. The creator and users of a model on such a platform can then collaborate to accomplish new research or educational objectives. From this perspective, a prototype cyber-infrastructure (CI), called SWATShare, is developed for sharing, running and visualizing Soil Water Assessment Tool (SWAT) models in an interactive GIS-enabled web environment. Users can utilize SWATShare to publish or upload their own models, search and download existing SWAT models developed by others, run simulations including calibration using high performance resources provided by XSEDE and Cloud. Besides running and sharing, SWATShare hosts a novel spatio-temporal visualization system for SWAT model outputs. In temporal scale, the system creates time-series plots for all the hydrology and water quality variables available along the reach as well as in watershed-level. In spatial scale, the system can dynamically generate sub-basin level thematic maps for any variable at any user-defined date or date range; and thereby, allowing users to run animations or download the data for subsequent analyses. In addition to research, SWATShare can also be used within a classroom setting as an educational tool for modeling and comparing the hydrologic processes under different geographic and climatic settings. SWATShare is publicly available at https://www.water-hub.org/swatshare.

On the role of ozone feedback in the ENSO amplitude response under global warming

NASA Astrophysics Data System (ADS)

Nowack, P. J.; Braesicke, P.; Abraham, N. L.; Pyle, J. A.

2017-12-01

The El Niño-Southern Oscillation (ENSO) in the tropical Pacific is of key importance to global climate and weather. However, climate models still disagree on the ENSO's response under climate change. Here we show that typical model representations of ozone can have a first-order impact on ENSO amplitude projections in climate sensitivity simulations (i.e. standard abrupt 4xCO2). We mainly explain this effect by the lapse rate adjustment of the tropical troposphere to ozone changes in the upper troposphere and lower stratosphere (UTLS) under 4xCO2. The ozone-induced lapse rate changes modify the Walker circulation response to the CO2 forcing and consequently tropical Pacific surface temperature gradients. Therefore, not including ozone feedbacks increases the number of extreme ENSO events in our model. In addition, we demonstrate that even if ozone changes in the tropical UTLS are included in the simulations, the neglect of the ozone response in the middle-upper stratosphere still leads to significantly larger ENSO amplitudes (compared to simulations run with a fully interactive atmospheric chemistry scheme). Climate modeling studies of the ENSO often neglect changes in ozone. Our results imply that this could affect the inter-model spread found in ENSO projections and, more generally, surface climate change simulations. We discuss the additional complexity in quantifying such ozone-related effects that arises from the apparent model dependency of chemistry-climate feedbacks and, possibly, their range of surface climate impacts. In conclusion, we highlight the need to understand better the coupling between ozone, the tropospheric circulation, and climate variability. Reference: Nowack PJ, Braesicke P, Abraham NL, and Pyle JA (2017), On the role of ozone feedback in the ENSO amplitude response under global warming, Geophys. Res. Lett. 44, 3858-3866, doi:10.1002/2016GL072418.

X-DRAIN and XDS: a simplified road erosion prediction method

Treesearch

William J. Elliot; David E. Hall; S. R. Graves

1998-01-01

To develop a simple road sediment delivery tool, the WEPP program modeled sedimentation from forest roads for more than 50,000 combinations of distance between cross drains, road gradient, soil texture, distance from stream, steepness of the buffer between the road and the stream, and climate. The sediment yield prediction from each of these runs was stored in a data...

RAP "Rapid Refresh" Products

Science.gov Websites

HOME PAGE Image of NCEP Logo WHERE AMERICA'S CLIMATE AND WEATHER SERVICES BEGIN NCEP Products Inventory Image of horizontal rule Rapid Refresh (RAP) Products Updated: 11/28/2016 * Information about the rap.tccz.awp243fxx.grib2 Not Available RAP - BUFR Sounding products Model Runs every hour (00z-23z) Filename Inventory

Earth Global Reference Atmospheric Model (GRAM99): Short Course

NASA Technical Reports Server (NTRS)

Leslie, Fred W.; Justus, C. G.

2007-01-01

Earth-GRAM is a FORTRAN software package that can run on a variety of platforms including PC's. For any time and location in the Earth's atmosphere, Earth-GRAM provides values of atmospheric quantities such as temperature, pressure, density, winds, constituents, etc.. Dispersions (perturbations) of these parameters are also provided and have realistic correlations, means, and variances - useful for Monte Carlo analysis. Earth-GRAM is driven by observations including a tropospheric database available from the National Climatic Data Center. Although Earth-GRAM can be run in a "stand-alone" mode, many users incorporate it into their trajectory codes. The source code is distributed free-of-charge to eligible recipients.

System dynamics approach for modeling of sugar beet yield considering the effects of climatic variables.

PubMed

Pervin, Lia; Islam, Md Saiful

2015-02-01

The aim of this study was to develop a system dynamics model for computation of yields and to investigate the dependency of yields on some major climatic parameters, i.e. temperature and rainfall, for Beta vulgaris subsp. (sugar beet crops) under future climate change scenarios. A system dynamics model was developed which takes account of the effects of rainfall and temperature on sugar beet yields under limited irrigation conditions. A relationship was also developed between the seasonal evapotranspiration and seasonal growing degree days for sugar beet crops. The proposed model was set to run for the present time period of 1993-2012 and for the future period 2013-2040 for Lethbridge region (Alberta, Canada). The model provides sugar beet yields on a yearly basis which are comparable to the present field data. It was found that the future average yield will be increased at about 14% with respect to the present average yield. The proposed model can help to improve the understanding of soil water conditions and irrigation water requirements of an area under certain climatic conditions and can be used for future prediction of yields for any crops in any region (with the required information to be provided). The developed system dynamics model can be used as a supporting tool for decision making, for improvement of agricultural management practice of any region. © 2014 Society of Chemical Industry.

Influence of future cropland expansion on regional and global tropospheric ozone

NASA Astrophysics Data System (ADS)

Squire, Oliver; Archibald, Alex; Telford, Paul; Pyle, John

2013-04-01

With the global population set to rise over the next 100 years, the fraction of land used for crop cultivation is likely to increase, the trend being most pronounced in developing regions such as Brazil and South East Asia. In these regions currently there stands natural rainforest, a high emitter of isoprene. As many staple crops, such as soy bean, are low emitters of isoprene, increasing the crop fraction in these regions will decrease isoprene emissions. Ozone over ~35 ppb has been shown to be damaging to plants, and as ground level ozone is sensitive to isoprene concentrations, altering isoprene emissions could increase ground level ozone, potentially resulting in crop damage. This mechanism was investigated by comparing two configurations of an atmospheric chemistry-climate model (UM-UKCA) under a 2100 climate following an IPCC scenario of moderate climate change. The first run had a present day crop distribution but isoprene emissions concurrent with 2100 temperatures and climatic conditions. The second run had isoprene emissions representative of both a 2100 climate and a 2100 crop distribution in accordance with the IMAGE model. By comparing these runs it was established that ozone increased by up to 8 ppb (~30%) due to crop land expansion. Over the Amazon (the most affected region) it was found that crops were exposed to a daily maximum 8-hour (DM8H) ozone above the 35 ppb threshold for up to 65 days more per year than in the base case. These conclusions suggest that increasing the crop fraction in current areas of natural rainforest could increase regional ground level ozone, having a significant effect on crop yield and air quality. The sensitivity of such conclusions to isoprene chemistry was examined by varying the isoprene chemistry scheme within the model. The CheT isoprene scheme used here (50 reactions) was compared with the AQUM (23 reactions) and CESM Superfast (2 reactions) isoprene schemes, all of which are currently used in Earth-system models. It was found that the effect of transplanting these isoprene schemes into the base CheT chemistry scheme lead, in both cases, to higher ozone over isoprene rich regions by up to ~40 ppb. Furthermore, upon repeating the land use change experiment with these other isoprene schemes, it was found that the AQUM scheme produced more ozone (up to ~20 ppb more) in isoprene rich regions due to crop expansion than CheT. However the CESM Superfast scheme showed the opposite effect, producing less ozone than the CheT scheme in isoprene-rich regions. These varied responses highlight the sensitivity of future trends in surface ozone to isoprene chemistry within the range of some currently used chemical schemes, and suggest that further research is needed in order to most effectively parameterise this complex chemistry.

Decision-relevant evaluation of climate models: A case study of chill hours in California

NASA Astrophysics Data System (ADS)

Jagannathan, K. A.; Jones, A. D.; Kerr, A. C.

2017-12-01

The past decade has seen a proliferation of different climate datasets with over 60 climate models currently in use. Comparative evaluation and validation of models can assist practitioners chose the most appropriate models for adaptation planning. However, such assessments are usually conducted for `climate metrics' such as seasonal temperature, while sectoral decisions are often based on `decision-relevant outcome metrics' such as growing degree days or chill hours. Since climate models predict different metrics with varying skill, the goal of this research is to conduct a bottom-up evaluation of model skill for `outcome-based' metrics. Using chill hours (number of hours in winter months where temperature is lesser than 45 deg F) in Fresno, CA as a case, we assess how well different GCMs predict the historical mean and slope of chill hours, and whether and to what extent projections differ based on model selection. We then compare our results with other climate-based evaluations of the region, to identify similarities and differences. For the model skill evaluation, historically observed chill hours were compared with simulations from 27 GCMs (and multiple ensembles). Model skill scores were generated based on a statistical hypothesis test of the comparative assessment. Future projections from RCP 8.5 runs were evaluated, and a simple bias correction was also conducted. Our analysis indicates that model skill in predicting chill hour slope is dependent on its skill in predicting mean chill hours, which results from the non-linear nature of the chill metric. However, there was no clear relationship between the models that performed well for the chill hour metric and those that performed well in other temperature-based evaluations (such winter minimum temperature or diurnal temperature range). Further, contrary to conclusions from other studies, we also found that the multi-model mean or large ensemble mean results may not always be most appropriate for this outcome metric. Our assessment sheds light on key differences between global versus local skill, and broad versus specific skill of climate models, highlighting that decision-relevant model evaluation may be crucial for providing practitioners with the best available climate information for their specific needs.

Named Data Networking in Climate Research and HEP Applications

NASA Astrophysics Data System (ADS)

Shannigrahi, Susmit; Papadopoulos, Christos; Yeh, Edmund; Newman, Harvey; Jerzy Barczyk, Artur; Liu, Ran; Sim, Alex; Mughal, Azher; Monga, Inder; Vlimant, Jean-Roch; Wu, John

2015-12-01

The Computing Models of the LHC experiments continue to evolve from the simple hierarchical MONARC[2] model towards more agile models where data is exchanged among many Tier2 and Tier3 sites, relying on both large scale file transfers with strategic data placement, and an increased use of remote access to object collections with caching through CMS's AAA, ATLAS' FAX and ALICE's AliEn projects, for example. The challenges presented by expanding needs for CPU, storage and network capacity as well as rapid handling of large datasets of file and object collections have pointed the way towards future more agile pervasive models that make best use of highly distributed heterogeneous resources. In this paper, we explore the use of Named Data Networking (NDN), a new Internet architecture focusing on content rather than the location of the data collections. As NDN has shown considerable promise in another data intensive field, Climate Science, we discuss the similarities and differences between the Climate and HEP use cases, along with specific issues HEP faces and will face during LHC Run2 and beyond, which NDN could address.

How Hot was Africa during the Mid-Holocene? Reexamining Africa's Thermal History via integrated Climate and Proxy System Modeling

NASA Astrophysics Data System (ADS)

Dee, S.; Russell, J. M.; Morrill, C.

2017-12-01

Climate models predict Africa will warm by up to 5°C in the coming century. Reconstructions of African temperature since the Last Glacial Maximum (LGM) have made fundamental contributions to our understanding of past, present, and future climate and can help constrain predictions from general circulation models (GCMs). However, many of these reconstructions are based on proxies of lake temperature, so the confounding influences of lacustrine processes may complicate our interpretations of past changes in tropical climate. These proxy-specific uncertainties require robust methodology for data-model comparison. We develop a new proxy system model (PSM) for paleolimnology to facilitate data-model comparison and to fully characterize uncertainties in climate reconstructions. Output from GCMs are used to force the PSM to simulate lake temperature, hydrology, and associated proxy uncertainties. We compare reconstructed East African lake and air temperatures in individual records and in a stack of 9 lake records to those predicted by our PSM forced with Paleoclimate Model Intercomparison Project (PMIP3) simulations, focusing on the mid-Holocene (6 kyr BP). We additionally employ single-forcing transient climate simulations from TraCE (10 kyr to 4 kyr B.P. and historical), as well as 200-yr time slice simulations from CESM1.0 to run the lake PSM. We test the sensitivity of African climate change during the mid-Holocene to orbital, greenhouse gas, and ice-sheet forcing in single-forcing simulations, and investigate dynamical hypotheses for these changes. Reconstructions of tropical African temperature indicate 1-2ºC warming during the mid-Holocene relative to the present, similar to changes predicted in the coming decades. However, most climate models underestimate the warming observed in these paleoclimate data (Fig. 1, 6kyr B.P.). We investigate this discrepancy using the new lake PSM and climate model simulations, with attention to the (potentially non-stationary) relationship between lake surface temperature and air temperature. The data-model comparison helps partition the impacts of lake-specific processes such as energy balance, mixing, sedimentation and bioturbation. We provide new insight into the patterns, amplitudes, sensitivity, and mechanisms of African temperature change.

Strategies for Teaching Regional Climate Modeling: Online Professional Development for Scientists and Decision Makers

NASA Astrophysics Data System (ADS)

Walton, P.; Yarker, M. B.; Mesquita, M. D. S.; Otto, F. E. L.

2014-12-01

There is a clear role for climate science in supporting decision making at a range of scales and in a range of contexts: from Global to local, from Policy to Industry. However, clear a role climate science can play, there is also a clear discrepancy in the understanding of how to use the science and associated tools (such as climate models). Despite there being a large body of literature on the science there is clearly a need to provide greater support in how to apply appropriately. However, access to high quality professional development courses can be problematic, due to geographic, financial and time constraints. In attempt to address this gap we independently developed two online professional courses that focused on helping participants use and apply two regional climate models, WRF and PRECIS. Both courses were designed to support participants' learning through tutor led programs that covered the basic climate scientific principles of regional climate modeling and how to apply model outputs. The fundamental differences between the two courses are: 1) the WRF modeling course expected participants to design their own research question that was then run on a version of the model, whereas 2) the PRECIS course concentrated on the principles of regional modeling and how the climate science informed the modeling process. The two courses were developed to utilise the cost and time management benefits associated with eLearning, with the recognition that this mode of teaching can also be accessed internationally, providing professional development courses in countries that may not be able to provide their own. The development teams saw it as critical that the courses reflected sound educational theory, to ensure that participants had the maximum opportunity to learn successfully. In particular, the role of reflection is central to both course structures to help participants make sense of the science in relation to their own situation. This paper details the different structures of both courses, evaluating the advantages and disadvantages of each, along with the educational approaches used. We conclude by proposing a framework for the develop of educationally robust online professional development programs that actively supports decision makers in understanding, developing and applying regional climate models.

Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios

DOE PAGES

Kim, John B.; Monier, Erwan; Sohngen, Brent; ...

2017-03-28

We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 °C global mean warming from pre-industrial by 2100. Evaluating the outcomesmore » of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world’s forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO 2 fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Finally, our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO 2 fertilization effects may considerably reduce the range of projections.« less

Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios

NASA Astrophysics Data System (ADS)

Kim, John B.; Monier, Erwan; Sohngen, Brent; Pitts, G. Stephen; Drapek, Ray; McFarland, James; Ohrel, Sara; Cole, Jefferson

2017-04-01

We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 °C global mean warming from pre-industrial by 2100. Evaluating the outcomes of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world’s forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO2 fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO2 fertilization effects may considerably reduce the range of projections.

Assessing climate change impacts, benefits of mitigation, and uncertainties on major global forest regions under multiple socioeconomic and emissions scenarios

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

Kim, John B.; Monier, Erwan; Sohngen, Brent

We analyze a set of simulations to assess the impact of climate change on global forests where MC2 dynamic global vegetation model (DGVM) was run with climate simulations from the MIT Integrated Global System Model-Community Atmosphere Model (IGSM-CAM) modeling framework. The core study relies on an ensemble of climate simulations under two emissions scenarios: a business-as-usual reference scenario (REF) analogous to the IPCC RCP8.5 scenario, and a greenhouse gas mitigation scenario, called POL3.7, which is in between the IPCC RCP2.6 and RCP4.5 scenarios, and is consistent with a 2 °C global mean warming from pre-industrial by 2100. Evaluating the outcomesmore » of both climate change scenarios in the MC2 model shows that the carbon stocks of most forests around the world increased, with the greatest gains in tropical forest regions. Temperate forest regions are projected to see strong increases in productivity offset by carbon loss to fire. The greatest cost of mitigation in terms of effects on forest carbon stocks are projected to be borne by regions in the southern hemisphere. We compare three sources of uncertainty in climate change impacts on the world’s forests: emissions scenarios, the global system climate response (i.e. climate sensitivity), and natural variability. The role of natural variability on changes in forest carbon and net primary productivity (NPP) is small, but it is substantial for impacts of wildfire. Forest productivity under the REF scenario benefits substantially from the CO 2 fertilization effect and that higher warming alone does not necessarily increase global forest carbon levels. Finally, our analysis underlines why using an ensemble of climate simulations is necessary to derive robust estimates of the benefits of greenhouse gas mitigation. It also demonstrates that constraining estimates of climate sensitivity and advancing our understanding of CO 2 fertilization effects may considerably reduce the range of projections.« less

On the appropriate definition of soil profile configuration and initial conditions for land surface-hydrology models in cold regions

NASA Astrophysics Data System (ADS)

Sapriza-Azuri, Gonzalo; Gamazo, Pablo; Razavi, Saman; Wheater, Howard S.

2018-06-01

Arctic and subarctic regions are amongst the most susceptible regions on Earth to global warming and climate change. Understanding and predicting the impact of climate change in these regions require a proper process representation of the interactions between climate, carbon cycle, and hydrology in Earth system models. This study focuses on land surface models (LSMs) that represent the lower boundary condition of general circulation models (GCMs) and regional climate models (RCMs), which simulate climate change evolution at the global and regional scales, respectively. LSMs typically utilize a standard soil configuration with a depth of no more than 4 m, whereas for cold, permafrost regions, field experiments show that attention to deep soil profiles is needed to understand and close the water and energy balances, which are tightly coupled through the phase change. To address this gap, we design and run a series of model experiments with a one-dimensional LSM, called CLASS (Canadian Land Surface Scheme), as embedded in the MESH (Modélisation Environmentale Communautaire - Surface and Hydrology) modelling system, to (1) characterize the effect of soil profile depth under different climate conditions and in the presence of parameter uncertainty; (2) assess the effect of including or excluding the geothermal flux in the LSM at the bottom of the soil column; and (3) develop a methodology for temperature profile initialization in permafrost regions, where the system has an extended memory, by the use of paleo-records and bootstrapping. Our study area is in Norman Wells, Northwest Territories of Canada, where measurements of soil temperature profiles and historical reconstructed climate data are available. Our results demonstrate a dominant role for parameter uncertainty, that is often neglected in LSMs. Considering such high sensitivity to parameter values and dependency on the climate condition, we show that a minimum depth of 20 m is essential to adequately represent the temperature dynamics. We further show that our proposed initialization procedure is effective and robust to uncertainty in paleo-climate reconstructions and that more than 300 years of reconstructed climate time series are needed for proper model initialization.

Water cycle changes over the Mediterranean: a comparison study of a super-high-resolution global model with CMIP3.

PubMed

Jin, Fengjun; Kitoh, Akio; Alpert, Pinhas

2010-11-28

Water cycle components over the Mediterranean for both a current run (1979-2007) and a future run (2075-2099) are studied with the Japan Meteorological Agency's 20 km grid global climate model. Results are compared with another study using the Coupled Model Intercomparison Project Phase 3 ensemble model (hereafter, the Mariotti model). Our results are surprisingly close to Mariotti's. The projected mean annual change rates of precipitation (P) between the future and the current run for sea and land are -11 per cent and -10 per cent, respectively, which are not as high as Mariotti's. Projected changes for evaporation (E) are +9.3 per cent and -3.6 per cent, compared with +7.2 per cent and -8.1 per cent in Mariotti's study, respectively. However, no significant difference in the change in P-E over the sea body was found between these two studies. The increased E over the eastern Mediterranean was found to be higher than that in the western Mediterranean, but the P decrease was lower. The net moisture budget, P-E, shows that the eastern Mediterranean will become even drier than the western Mediterranean. The river model suggests decreasing water inflow to the Mediterranean of approximately 36 per cent (excluding the Nile).

Climate Penalty on Air Quality and Human Health in China and India

NASA Astrophysics Data System (ADS)

Li, M.; Zhang, S.; Garcia-Menendez, F.; Monier, E.; Selin, N. E.

2017-12-01

Climate change, favoring more heat waves and episodes of stagnant air, may deteriorate air quality by increasing ozone and fine particulate matter (PM2.5) concentrations and high pollution episodes. This effect, termed as "climate penalty", has been quantified and explained by many earlier studies in the U.S. and Europe, but research efforts in Asian countries are limited. We evaluate the impact of climate change on air quality and human health in China and India using a modeling framework that links the Massachusetts Institute of Technology Integrated Global System Model to the Community Atmosphere Model (MIT IGSM-CAM). Future climate fields are projected under three climate scenarios including a no-policy reference scenario and two climate stabilization scenarios with 2100 total radiative forcing targets of 9.7, 4.5 and 3.7 W m-2, respectively. Each climate scenario is run for five representations of climate variability to account for the role of natural variability. Thirty-year chemical transport simulations are conducted in 1981-2010 and 2086-2115 under the three climate scenarios with fixed anthropogenic emissions at year 2000 levels. We find that 2000—2100 climate change under the no-policy reference scenario would increase ozone concentrations in eastern China and northern India by up to 5 ppb through enhancing biogenic emissions and ozone production efficiency. Ozone extreme episodes also become more frequent in these regions, while climate policies can offset most of the increase in ozone episodes. Climate change between 2000 and 2100 would slightly increase anthropogenic PM2.5 concentrations in northern China and Sichuan province, but significantly reduce anthropogenic PM2.5 concentrations in southern China and northern India, primarily due to different chemical responses of sulfate-nitrate-ammonium aerosols to climate change in these regions. Our study also suggests that the mitigation costs of climate policies can be partially offset by health benefits from reduced climate-induced air pollution in China.

Heat uptake in the Southern Ocean in a warmer, windier world: a process-based analysis using an AOGCM with an eddy-permitting ocean

NASA Astrophysics Data System (ADS)

Kuhlbrodt, T.; Gregory, J. M.

2016-02-01

About 90% of the anthropogenic increase in heat stored in the climate system is found the oceans. Therefore it is relevant to understand the details of ocean heat uptake. Here we present a detailed, process-based analysis of ocean heat uptake (OHU) processes in HiGEM1.2, an atmosphere-ocean general circulation model (AOGCM) with an eddy-permitting ocean component of 1/3° resolution. Similarly to various other models, HiGEM1.2 shows that the global heat budget is dominated by a downward advection of heat compensated by upward isopycnal diffusion. This upward isopycnal diffusion of heat is located mostly in the Southern Ocean (Fig. 1a).We compare the responses to a 4xCO2 forcing and an enhancement of the windstress forcing in the Southern Ocean. In line with the CMIP5 models, HiGEM1.2 shows a band of strong OHU in the mid-latitude Southern Ocean in the 4xCO2 run, which is mostly advective. By contrast, in the high-latitude Southern Ocean regions it is the suppression of convection that leads to OHU (Fig. 1b). In the enhanced windstress run, convection is strengthened at high Southern latitudes (Fig. 1c), leading to heat loss, while the magnitude of the OHU in the Southern mid-latitudes is very similar to the 4xCO2 results. Remarkably, there is only very small global OHU in the enhanced windstress run. The wind stress forcing just leads to a redistribution of heat. We relate the ocean changes at high southern latitudes to the effect of climate change on the Antarctic Circumpolar Current (ACC). It weakens in the 4xCO2 run and strengthens in the wind stress run. The weakening is due to a narrowing of the ACC, caused by an expansion of the Weddell Gyre, and a flattening of the isopycnals, which are explained by a combination of the wind stress forcing and increased precipitation. The presentation will also try to clarify the definitions of terms like "advective", "diffusive" and "eddy-induced" when used for observed and modelled (at various resolutions) ocean heat uptake processes. Fig. 1: Horizontally averaged temperature tendency diagnostics for the high-latitude Southern Ocean, for (a) the control run, (b) the 4xCO2 anomalies and (c) the windstress anomalies. Both axes are scaled according to a power law. "VM"- vertical mixing, which includes convection ("conv").

Warm Mediterranean mid-Holocene summers inferred from fossil midge assemblages

NASA Astrophysics Data System (ADS)

Samartin, Stéphanie; Heiri, Oliver; Joos, Fortunat; Renssen, Hans; Franke, Jörg; Brönnimann, Stefan; Tinner, Willy

2017-02-01

Understanding past climate trends is key for reliable projections of global warming and associated risks and hazards. Uncomfortably large discrepancies between vegetation-based summer temperature reconstructions (mainly based on pollen) and climate model results have been reported for the current interglacial, the Holocene. For the Mediterranean region these reconstructions indicate cooler-than-present mid-Holocene summers, in contrast with expectations based on climate models and long-term changes in summer insolation. We present new quantitative and replicated Holocene summer temperature reconstructions based on fossil chironomid midges from the northern central Mediterranean region. The Holocene thermal maximum is reconstructed 9,000-5,000 years ago and estimated to have been 1-2 °C warmer in mean July temperature than the recent pre-industrial period, consistent with glacier and marine records, and with transient climate model runs. This combined evidence implies that widely used pollen-based summer temperature reconstructions in the Mediterranean area are significantly biased by precipitation or other forcings such as early land use. Our interpretation can resolve the previous discrepancy between climate models and quantitative palaeotemperature records for millennial-scale Holocene summer temperature trends in the Mediterranean region. It also suggests that pollen-based evidence for cool mid-Holocene summers in other semi-arid to arid regions of the Northern Hemisphere may have to be reconsidered, with potential implications for global-scale reconstructions.