Mechanisms influencing seasonal to inter-annual prediction skill of sea ice extent in the Arctic Ocean in MIROC

NASA Astrophysics Data System (ADS)

Ono, Jun; Tatebe, Hiroaki; Komuro, Yoshiki; Nodzu, Masato I.; Ishii, Masayoshi

2018-02-01

To assess the skill of seasonal to inter-annual predictions of the detrended sea ice extent in the Arctic Ocean (SIEAO) and to clarify the underlying physical processes, we conducted ensemble hindcasts, started on 1 January, 1 April, 1 July and 1 October for each year from 1980 to 2011, for lead times up to three years, using the Model for Interdisciplinary Research on Climate (MIROC) version 5 initialised with the observed atmosphere and ocean anomalies and sea ice concentration. Significant skill is found for the winter months: the December SIEAO can be predicted up to 11 months ahead (anomaly correlation coefficient is 0.42). This skill might be attributed to the subsurface ocean heat content originating in the North Atlantic. A plausible mechanism is as follows: the subsurface water flows into the Barents Sea from spring to fall and emerges at the surface in winter by vertical mixing, and eventually affects the sea ice variability there. Meanwhile, the September SIEAO predictions are skillful for lead times of up to two months, due to the persistence of sea ice in the Beaufort, Chukchi, and East Siberian seas initialised in July, as suggested by previous studies.

New Methods for Estimating Seasonal Potential Climate Predictability

NASA Astrophysics Data System (ADS)

Feng, Xia

This study develops two new statistical approaches to assess the seasonal potential predictability of the observed climate variables. One is the univariate analysis of covariance (ANOCOVA) model, a combination of autoregressive (AR) model and analysis of variance (ANOVA). It has the advantage of taking into account the uncertainty of the estimated parameter due to sampling errors in statistical test, which is often neglected in AR based methods, and accounting for daily autocorrelation that is not considered in traditional ANOVA. In the ANOCOVA model, the seasonal signals arising from external forcing are determined to be identical or not to assess any interannual variability that may exist is potentially predictable. The bootstrap is an attractive alternative method that requires no hypothesis model and is available no matter how mathematically complicated the parameter estimator. This method builds up the empirical distribution of the interannual variance from the resamplings drawn with replacement from the given sample, in which the only predictability in seasonal means arises from the weather noise. These two methods are applied to temperature and water cycle components including precipitation and evaporation, to measure the extent to which the interannual variance of seasonal means exceeds the unpredictable weather noise compared with the previous methods, including Leith-Shukla-Gutzler (LSG), Madden, and Katz. The potential predictability of temperature from ANOCOVA model, bootstrap, LSG and Madden exhibits a pronounced tropical-extratropical contrast with much larger predictability in the tropics dominated by El Nino/Southern Oscillation (ENSO) than in higher latitudes where strong internal variability lowers predictability. Bootstrap tends to display highest predictability of the four methods, ANOCOVA lies in the middle, while LSG and Madden appear to generate lower predictability. Seasonal precipitation from ANOCOVA, bootstrap, and Katz, resembling that

Seasonal and Interannual Variabilities in Tropical Tropospheric Ozone

NASA Technical Reports Server (NTRS)

Ziemke, J. R.; Chandra, S.

1999-01-01

This paper presents a detailed characterization of seasonal and interannual variability in tropical tropospheric column ozone (TCO). TCO time series are derived from 20 years (1979-1998) of total ozone mapping spectrometer (TOMS) data using the convective cloud differential (CCD) method. Our study identifies three regions in the tropics with distinctly different zonal characteristics related to seasonal and interannual variability. These three regions are the eastern Pacific, Atlantic, and western Pacific. Results show that in both the eastern and western Pacific seasonal-cycle variability of northern hemisphere (NH) TCO exhibits maximum amount during NH spring whereas largest amount in southern hemisphere (SH) TCO occurs during SH spring. In the Atlantic, maximum TCO in both hemispheres occurs in SH spring. These seasonal cycles are shown to be comparable to seasonal cycles present in ground-based ozonesonde measurements. Interannual variability in the Atlantic region indicates a quasi-biennial oscillation (QBO) signal that is out of phase with the QBO present in stratospheric column ozone (SCO). This is consistent with high pollution and high concentrations of mid-to-upper tropospheric O3-producing precursors in this region. The out of phase relation suggests a UV modulation of tropospheric photochemistry caused by the QBO in stratospheric O3. During El Nino events there is anomalously low TCO in the eastern Pacific and high values in the western Pacific, indicating the effects of convectively-driven transport of low-value boundary layer O3 (reducing TCO) and O3 precursors including H2O and OH. A simplified technique is proposed to derive high-resolution maps of TCO in the tropics even in the absence of tropopause-level clouds. This promising approach requires only total ozone gridded measurements and utilizes the small variability observed in TCO near the dateline. This technique has an advantage compared to the CCD method because the latter requires high

Communicating uncertainty in seasonal and interannual climate forecasts in Europe.

PubMed

Taylor, Andrea L; Dessai, Suraje; de Bruin, Wändi Bruine

2015-11-28

Across Europe, organizations in different sectors are sensitive to climate variability and change, at a range of temporal scales from the seasonal to the interannual to the multi-decadal. Climate forecast providers face the challenge of communicating the uncertainty inherent in these forecasts to these decision-makers in a way that is transparent, understandable and does not lead to a false sense of certainty. This article reports the findings of a user-needs survey, conducted with 50 representatives of organizations in Europe from a variety of sectors (e.g. water management, forestry, energy, tourism, health) interested in seasonal and interannual climate forecasts. We find that while many participating organizations perform their own 'in house' risk analysis most require some form of processing and interpretation by forecast providers. However, we also find that while users tend to perceive seasonal and interannual forecasts to be useful, they often find them difficult to understand, highlighting the need for communication formats suitable for both expert and non-expert users. In addition, our results show that people tend to prefer familiar formats for receiving information about uncertainty. The implications of these findings for both the providers and users of climate information are discussed. © 2015 The Authors.

Communicating uncertainty in seasonal and interannual climate forecasts in Europe

PubMed Central

Taylor, Andrea L.; Dessai, Suraje; de Bruin, Wändi Bruine

2015-01-01

Across Europe, organizations in different sectors are sensitive to climate variability and change, at a range of temporal scales from the seasonal to the interannual to the multi-decadal. Climate forecast providers face the challenge of communicating the uncertainty inherent in these forecasts to these decision-makers in a way that is transparent, understandable and does not lead to a false sense of certainty. This article reports the findings of a user-needs survey, conducted with 50 representatives of organizations in Europe from a variety of sectors (e.g. water management, forestry, energy, tourism, health) interested in seasonal and interannual climate forecasts. We find that while many participating organizations perform their own ‘in house’ risk analysis most require some form of processing and interpretation by forecast providers. However, we also find that while users tend to perceive seasonal and interannual forecasts to be useful, they often find them difficult to understand, highlighting the need for communication formats suitable for both expert and non-expert users. In addition, our results show that people tend to prefer familiar formats for receiving information about uncertainty. The implications of these findings for both the providers and users of climate information are discussed. PMID:26460115

Seasonal and Inter-annual Variation in Wood Production in Tropical Trees on Barro Colorado Island, Panama, is Related to Local Climate and Species Functional Traits

NASA Astrophysics Data System (ADS)

Cushman, K.; Muller-Landau, H. C.; Kellner, J. R.; Wright, S. J.; Condit, R.; Detto, M.; Tribble, C. M.

2015-12-01

Tropical forest carbon budgets play a major role in global carbon dynamics, but the responses of tropical forests to current and future inter-annual climatic variation remains highly uncertain. Better predictions of future tropical forest carbon fluxes require an improved understanding of how different species of tropical trees respond to changes in climate at seasonal and inter-annual temporal scales. We installed dendrometer bands on a size-stratified sample of 2000 trees in old growth forest on Barro Colorado Island, Panama, a moist lowland forest that experiences an annual dry season of approximately four months. Tree diameters were measured at the beginning and end of the rainy season since 2008. Additionally, we recorded the canopy illumination level, canopy intactness, and liana coverage of all trees during each census. We used linear mixed-effects models to evaluate how tree growth was related to seasonal and interannual variation in local climate, tree condition, and species identity, and how species identity effects related to tree functional traits. Climatic variables considered included precipitation, solar radiation, soil moisture, and climatological water deficit, and were all calculated from high-quality on-site measurements. Functional traits considered included wood density, maximum adult stature, deciduousness, and drought tolerance. We found that annual wood production was positively related to water availability, with higher growth in wetter years. Species varied in their response to seasonal water availability, with some species showing more pronounced reduction of growth during the dry season when water availability is limited. Interspecific variation in seasonal and interannual growth patterns was related to life-history strategies and species functional traits. The finding of higher growth in wetter years is consistent with previous tree ring studies conducted on a small subset of species with reliable annual rings. Together with previous

Seasonal-to-Interannual Variability and Land Surface Processes

NASA Technical Reports Server (NTRS)

Koster, Randal

2004-01-01

Atmospheric chaos severely limits the predictability of precipitation on subseasonal to interannual timescales. Hope for accurate long-term precipitation forecasts lies with simulating atmospheric response to components of the Earth system, such as the ocean, that can be predicted beyond a couple of weeks. Indeed, seasonal forecasts centers now rely heavily on forecasts of ocean circulation. Soil moisture, another slow component of the Earth system, is relatively ignored by the operational seasonal forecasting community. It is starting, however, to garner more attention. Soil moisture anomalies can persist for months. Because these anomalies can have a strong impact on evaporation and other surface energy fluxes, and because the atmosphere may respond consistently to anomalies in the surface fluxes, an accurate soil moisture initialization in a forecast system has the potential to provide additional forecast skill. This potential has motivated a number of atmospheric general circulation model (AGCM) studies of soil moisture and its contribution to variability in the climate system. Some of these studies even suggest that in continental midlatitudes during summer, oceanic impacts on precipitation are quite small relative to soil moisture impacts. The model results, though, are strongly model-dependent, with some models showing large impacts and others showing almost none at all. A validation of the model results with observations thus naturally suggests itself, but this is exceedingly difficult. The necessary contemporaneous soil moisture, evaporation, and precipitation measurements at the large scale are virtually non-existent, and even if they did exist, showing statistically that soil moisture affects rainfall would be difficult because the other direction of causality - wherein rainfall affects soil moisture - is unquestionably active and is almost certainly dominant. Nevertheless, joint analyses of observations and AGCM results do reveal some suggestions of

Impact of Soil Moisture Initialization on Seasonal Weather Prediction

NASA Technical Reports Server (NTRS)

Koster, Randal D.; Suarez, Max J.; Houser, Paul (Technical Monitor)

2002-01-01

The potential role of soil moisture initialization in seasonal forecasting is illustrated through ensembles of simulations with the NASA Seasonal-to-Interannual Prediction Project (NSIPP) model. For each boreal summer during 1997-2001, we generated two 16-member ensembles of 3-month simulations. The first, "AMIP-style" ensemble establishes the degree to which a perfect prediction of SSTs would contribute to the seasonal prediction of precipitation and temperature over continents. The second ensemble is identical to the first, except that the land surface is also initialized with "realistic" soil moisture contents through the continuous prior application (within GCM simulations leading up to the start of the forecast period) of a daily observational precipitation data set and the associated avoidance of model drift through the scaling of all surface prognostic variables. A comparison of the two ensembles shows that soil moisture initialization has a statistically significant impact on summertime precipitation and temperature over only a handful of continental regions. These regions agree, to first order, with regions that satisfy three conditions: (1) a tendency toward large initial soil moisture anomalies, (2) a strong sensitivity of evaporation to soil moisture, and (3) a strong sensitivity of precipitation to evaporation. The degree to which the initialization improves forecasts relative to observations is mixed, reflecting a critical need for the continued development of model parameterizations and data analysis strategies.

The Stochastic predictability limits of GCM internal variability and the Stochastic Seasonal to Interannual Prediction System (StocSIPS)

NASA Astrophysics Data System (ADS)

Del Rio Amador, Lenin; Lovejoy, Shaun

2017-04-01

Over the past ten years, a key advance in our understanding of atmospheric variability is the discovery that between the weather and climate regime lies an intermediate "macroweather" regime, spanning the range of scales from ≈10 days to ≈30 years. Macroweather statistics are characterized by two fundamental symmetries: scaling and the factorization of the joint space-time statistics. In the time domain, the scaling has low intermittency with the additional property that successive fluctuations tend to cancel. In space, on the contrary the scaling has high (multifractal) intermittency corresponding to the existence of different climate zones. These properties have fundamental implications for macroweather forecasting: a) the temporal scaling implies that the system has a long range memory that can be exploited for forecasting; b) the low temporal intermittency implies that mathematically well-established (Gaussian) forecasting techniques can be used; and c), the statistical factorization property implies that although spatial correlations (including teleconnections) may be large, if long enough time series are available, they are not necessarily useful in improving forecasts. Theoretically, these conditions imply the existence of stochastic predictability limits in our talk, we show that these limits apply to GCM's. Based on these statistical implications, we developed the Stochastic Seasonal and Interannual Prediction System (StocSIPS) for the prediction of temperature from regional to global scales and from one month to many years horizons. One of the main components of StocSIPS is the separation and prediction of both the internal and externally forced variabilities. In order to test the theoretical assumptions and consequences for predictability and predictions, we use 41 different CMIP5 model outputs from preindustrial control runs that have fixed external forcings: whose variability is purely internally generated. We first show that these statistical

Seasonal divergence in the interannual responses of Northern Hemisphere vegetation activity to variations in diurnal climate.

PubMed

Wu, Xiuchen; Liu, Hongyan; Li, Xiaoyan; Liang, Eryuan; Beck, Pieter S A; Huang, Yongmei

2016-01-11

Seasonal asymmetry in the interannual variations in the daytime and nighttime climate in the Northern Hemisphere (NH) is well documented, but its consequences for vegetation activity remain poorly understood. Here, we investigate the interannual responses of vegetation activity to variations of seasonal mean daytime and nighttime climate in NH (>30 °N) during the past decades using remote sensing retrievals, FLUXNET and tree ring data. Despite a generally significant and positive response of vegetation activity to seasonal mean maximum temperature (Tmax) in ~22-25% of the boreal (>50 °N) NH between spring and autumn, spring-summer progressive water limitations appear to decouple vegetation activity from the mean summer Tmax, particularly in climate zones with dry summers. Drought alleviation during autumn results in vegetation recovery from the marked warming-induced drought limitations observed in spring and summer across 24-26% of the temperate NH. Vegetation activity exhibits a pervasively negative correlation with the autumn mean minimum temperature, which is in contrast to the ambiguous patterns observed in spring and summer. Our findings provide new insights into how seasonal asymmetry in the interannual variations in the mean daytime and nighttime climate interacts with water limitations to produce spatiotemporally variable responses of vegetation growth.

Seasonal divergence in the interannual responses of Northern Hemisphere vegetation activity to variations in diurnal climate

PubMed Central

Wu, Xiuchen; Liu, Hongyan; Li, Xiaoyan; Liang, Eryuan; Beck, Pieter S. A.; Huang, Yongmei

2016-01-01

Seasonal asymmetry in the interannual variations in the daytime and nighttime climate in the Northern Hemisphere (NH) is well documented, but its consequences for vegetation activity remain poorly understood. Here, we investigate the interannual responses of vegetation activity to variations of seasonal mean daytime and nighttime climate in NH (>30 °N) during the past decades using remote sensing retrievals, FLUXNET and tree ring data. Despite a generally significant and positive response of vegetation activity to seasonal mean maximum temperature () in ~22–25% of the boreal (>50 °N) NH between spring and autumn, spring-summer progressive water limitations appear to decouple vegetation activity from the mean summer , particularly in climate zones with dry summers. Drought alleviation during autumn results in vegetation recovery from the marked warming-induced drought limitations observed in spring and summer across 24–26% of the temperate NH. Vegetation activity exhibits a pervasively negative correlation with the autumn mean minimum temperature, which is in contrast to the ambiguous patterns observed in spring and summer. Our findings provide new insights into how seasonal asymmetry in the interannual variations in the mean daytime and nighttime climate interacts with water limitations to produce spatiotemporally variable responses of vegetation growth. PMID:26751166

Seasonal to Interannual Surface Ocean Salinity Trends With Aquarius Data

NASA Astrophysics Data System (ADS)

Lagerloef, G. S. E.; Kao, H. Y.; Carey, D.

2017-12-01

An important scientific goal for satellite salinity observations is to document oceanic climate trends and their link to changes in the water cycle. This study is a re-examination of seasonal to interannual sea surface salinity (SSS) variations from more recent analyses of V5.0 reprocessing of the Aquarius satellite data, Sep 2011 to May 2015. Sensor calibration over these time scales has been a concern, and the V5.0 includes improved calibration reference data compared to previous versions, which will be explained. Orthogonal mode analyses show that the annual cycle dominates the variability, and is strongest in the tropics. Interannual trends indicate the principal salinity patterns during onset of the 2015-16 El Niño. Recognizing that the Aquarius data record is now finite (Sep 2011 through May 2015) due to the mission failure in early June 2015, we will conclude with a status summary of the disposition of the Aquarius data and the prospects for continuing satellite salinity measurements.

Prediction Activities at NASA's Global Modeling and Assimilation Office

NASA Technical Reports Server (NTRS)

Schubert, Siegfried

2010-01-01

The Global Modeling and Assimilation Office (GMAO) is a core NASA resource for the development and use of satellite observations through the integrating tools of models and assimilation systems. Global ocean, atmosphere and land surface models are developed as components of assimilation and forecast systems that are used for addressing the weather and climate research questions identified in NASA's science mission. In fact, the GMAO is actively engaged in addressing one of NASA's science mission s key questions concerning how well transient climate variations can be understood and predicted. At weather time scales the GMAO is developing ultra-high resolution global climate models capable of resolving high impact weather systems such as hurricanes. The ability to resolve the detailed characteristics of weather systems within a global framework greatly facilitates addressing fundamental questions concerning the link between weather and climate variability. At sub-seasonal time scales, the GMAO is engaged in research and development to improve the use of land information (especially soil moisture), and in the improved representation and initialization of various sub-seasonal atmospheric variability (such as the MJO) that evolves on time scales longer than weather and involves exchanges with both the land and ocean The GMAO has a long history of development for advancing the seasonal-to-interannual (S-I) prediction problem using an older version of the coupled atmosphere-ocean general circulation model (AOGCM). This includes the development of an Ensemble Kalman Filter (EnKF) to facilitate the multivariate assimilation of ocean surface altimetry, and an EnKF developed for the highly inhomogeneous nature of the errors in land surface models, as well as the multivariate assimilation needed to take advantage of surface soil moisture and snow observations. The importance of decadal variability, especially that associated with long-term droughts is well recognized by the

An Assessment of the Predictability of Northern Winter Seasonal Means with the NSIPP 1 AGCM

NASA Technical Reports Server (NTRS)

Suarez, Max J. (Editor); Pegion, Philip J.; Schubert, Siegfried D.

2000-01-01

This atlas assesses the predictability of January-February-March (JFM) means using version 1 of the NASA Seasonal-to-Interannual Prediction Project Atmospheric General Circulation Model (the NSIPP 1 AGCM). The AGCM is part of the NSIPP coupled atmosphere-land-ocean model. For these results, the atmosphere was run uncoupled from the ocean, but coupled with an interactive land model. The results are based on 20 ensembles of nine JFM hindcasts for the period 1980-1999, with sea surface temperature (SST) and sea ice specified from observations. The model integrations were started from initial atmospheric conditions (taken from NCEP/NCAR reanalyses) centered on December 15. The analysis focuses on 200 mb height, precipitation, surface temperature, and sea-level pressure. The results address issues of both predictability and forecast skill. Various signal-to-noise measures are computed to demonstrate the potential for skillful prediction on seasonal time scales under the assumption of a perfect model and perfectly known oceanic boundary forcings. The results show that the model produces a realistic ENSO response in both the tropics and extratropics.

Technical Report Series on Global Modeling and Data Assimilation. Volume 13; Interannual Variability and Potential Predictability in Reanalysis Products

NASA Technical Reports Server (NTRS)

Min, Wei; Schubert, Siegfried D.; Suarez, Max J. (Editor)

1997-01-01

The Data Assimilation Office (DAO) at Goddard Space Flight Center and the National Center for Environmental Prediction and National Center for Atmospheric Research (NCEP/NCAR) have produced multi-year global assimilations of historical data employing fixed analysis systems. These "reanalysis" products are ideally suited for studying short-term climatic variations. The availability of multiple reanalysis products also provides the opportunity to examine the uncertainty in the reanalysis data. The purpose of this document is to provide an updated estimate of seasonal and interannual variability based on the DAO and NCEP/NCAR reanalyses for the 15-year period 1980-1995. Intercomparisons of the seasonal means and their interannual variations are presented for a variety of prognostic and diagnostic fields. In addition, atmospheric potential predictability is re-examined employing selected DAO reanalysis variables.

Potential Seasonal Predictability of Water Cycle in Observations and Reanalysis

NASA Astrophysics Data System (ADS)

Feng, X.; Houser, P.

2012-12-01

Identification of predictability of water cycle variability is crucial for climate prediction, water resources availability, ecosystem management and hazard mitigation. An analysis that can assess the potential skill in seasonal prediction was proposed by the authors, named as analysis of covariance (ANOCOVA). This method tests whether interannual variability of seasonal means exceeds that due to weather noise under the null hypothesis that seasonal means are identical every year. It has the advantage of taking into account autocorrelation structure in the daily time series but also accounting for the uncertainty of the estimated parameters in the significance test. During the past several years, multiple reanalysis datasets have become available for studying climate variability and understanding climate system. We are motivated to compare the potential predictability of water cycle variation from different reanalysis datasets against observations using the newly proposed ANOCOVA method. The selected eight reanalyses include the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP/NCAR) 40-year Reanalysis Project (NNRP), the National Centers for Environmental Prediction-Department of Energy (NCEP/DOE) Reanalysis Project (NDRP), the European Centre for Medium-Range Weather Forecasts (ECMWF) 40-year Reanalysis, The Japan Meteorological Agency 25-year Reanalysis Project (JRA25), the ECMWF) Interim Reanalysis (ERAINT), the NCEP Climate Forecast System Reanalysis (CFSR), the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA), and the National Oceanic and Atmospheric Administration-Cooperative Institute for Research in Environmental Sciences (NOAA/CIRES) 20th Century Reanalysis Version 2 (20CR). For key water cycle components, precipitation and evaporation, all reanalyses consistently show high fraction of predictable variance in the tropics, low

Rectification of Atmospheric Intraseasonal Oscillations on Seasonal to Interannual Sea Surface Temperature in the Indian Ocean

NASA Astrophysics Data System (ADS)

Duncan, B.; Han, W.

2010-12-01

An ocean general circulation model (the Hybrid Coordinate Ocean Model, HYCOM) is used to examine the rectification of atmospheric intraseasonal oscillations (ISOs) on lower-frequency seasonal to interannual sea surface temperatures (SSTs) in the Indian Ocean (IO). Existing studies have shown that ISOs rectify on low-frequency equatorial surface currents, suggesting that they may also have important impacts on low-frequency SST variability. To evaluate these impacts, a hierarchy of experiments is run with HYCOM that isolates the ocean response to atmospheric forcing by 10-30 day (submonthly), 30-90 day (dominated by the Madden-Julian Oscillation), and 10-90 day (all ISO) events. Other experiments isolate the ocean response to a range of forcing processes including shortwave radiation, precipitation, and winds. Results indicate that ISOs have a non-negligible effect on the seasonal and annual cycles of SST in the Arabian Sea. The maximum seasonal SST variability in the Arabian Sea is 1.6°C, while the ISO-forced seasonal SST variability has a maximum of 0.4°C. Because SSTs in the Arabian Sea are already warm (>28°C), a change of 0.4°C can affect convection there. ISOs also have non-negligible effects on the seasonal variability of SST in the south- and west- equatorial IO. The ISO contribution to the seasonal cycle of mixed layer thickness (hmix) in the eastern equatorial IO has a maximum of 9m, while the total hmix seasonal cycle has a maximum of 14m. ISOs affect the hmix seasonal cycle by up to 10m in the Arabian Sea, where the total seasonal cycle has a maximum of 75m. Further work will seek to explain the causes of this observed rectification of ISOs on seasonal SST and mixed layer variability, and to extend our results to include interannual timescales.

Seasonal and interannual variability of mesozooplankton in two contrasting estuaries of the Bay of Biscay: Relationship to environmental factors

NASA Astrophysics Data System (ADS)

Villate, Fernando; Iriarte, Arantza; Uriarte, Ibon; Sanchez, Iraide

2017-12-01

Seasonal and interannual variations of total mesozooplankton abundance and community variability were assessed for the period 1998-2005 at 3 salinity sites (35, 33 and 30) of the estuaries of Bilbao and Urdaibai (southeast Bay of Biscay). Spatial differences in mesozooplankton seasonality were recognized, both within and between estuaries, related to differences between sites in hydrodynamic features and anthropogenic nutrient enrichment that drive phytoplankton biomass seasonal cycles. The within estuary seasonal differences in mesozooplankton community were mainly shown through seaward time-advances in the seasonal peak from summer to spring along the salinity gradient, linked to differences in phytoplankton availability during the summer, in turn, related to nutrient availability. These differences were most marked in the estuary of Urdaibai, where zooplankton seasonal pattern at 35 salinity (high tidal flushing) resembled that of shelf waters, while at 35 of the estuary of Bilbao zooplankton showed an estuarine seasonal pattern due to the influence of the estuarine plume. Cirripede larvae contributed most to the mesozooplankton seasonal variability, except at the outer estuary of Bilbao, where cladocerans and fish eggs and larvae were the major contributors, and the inner estuary of Urdaibai, where gastropod larvae contributed most. Total mesozooplankton increased at 30 salinity of the estuary of Bilbao and 35 salinity of the estuary of Urdaibai. Interannual variability of mesozooplankton at the lowest salinity of the estuary of Bilbao was mainly accounted for by copepods due to the introduction of non-indigenous species during estuarine rehabilitation from intense pollution. However, bivalve larvae and gastropod larvae showed the highest contributions at 35 salinity of the estuary of Urdaibai. At the rest of sites, the opposite interannual trends of polychaete larvae and hydromedusae generally made the highest contribution.

Seasonal and interannual temperature variations in the tropical stratosphere

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

Reid, G.C.

1994-09-20

Temperature variations in the tropical lower and middle stratosphere are influenced by at least five distinct driving forces. These are (1) the mechanism of the regular seasonal cycle, (2) the quasi-biennial oscillation (QBO) in zonal winds, (3) the semiannual zonal wind oscillation (SAO) at higher levels, (4) El Nino-Southern Oscillation (ENSO) effects driven by the underlying troposphere, and (5) radiative effects, including volcanic aerosol heating. Radiosonde measurements of temperatures from a number of tropical stations, mostly in the western Pacific region, are used in this paper to examine the characteristic annual and interannual temperature variability in the stratosphere below themore » 10-hPa pressure level ({approximately} 31 km) over a time period of 17 years, chosen to eliminate or at least minimize the effect of volcanic eruptions. Both annual and interannual variations are found to show a fairly distinct transition between the lower and the middle stratosphere at about the 35-hPa level ({approximately} 23 km). The lower stratosphere, below this transition level, is strongly influenced by the ENSO cycle as well as by the QBO. The overall result of the interaction is to modulate the amplitude of the normal stratospheric seasonal cycle and to impose a biennial component on it, so that alternate seasonal cycles are stronger or weaker than normal. Additional modulation by the ENSO cycle occurs at its quasi-period of 3-5 years, giving rise to a complex net behavior. In the middle stratosphere above the transition level, there is no discernible ENSO influence, and departures from the regular semiannual seasonal cycle are dominated by the QBO. Recent ideas on the underlying physical mechanisms governing these variations are discussed, as is the relationship of the radiosonde measurements to recent satellite remote-sensing observations. 37 refs., 8 figs., 1 tab.« less

Seasonal and Interannual Variations of Heat Fluxes in the Barents Sea Region

NASA Astrophysics Data System (ADS)

Bashmachnikov, I. L.; Yurova, A. Yu.; Bobylev, L. P.; Vesman, A. V.

2018-03-01

Seasonal and interannual variations in adjective heat fluxes in the ocean ( dQ oc) and the convergence of advective heat fluxes in the atmosphere ( dQ atm) in the Barents Sea region have been investigated over the period of 1993-2012 using the results of the MIT regional eddy-permitting model and ERA-Interim atmospheric reanalysis. Wavelet analysis and singular spectrum analysis are used to reveal concealed periodicities. Seasonal 2- to 4- and 5- to 8-year cycles are revealed in the dQ oc and dQ atm data. It is also found that seasonal variations in dQ oc are primarily determined by the integrated volume fluxes through the western boundary of the Barents Sea, whereas the 20-year trend is determined by the temperature variation of the transported water. A cross-wavelet analysis of dQ oc and dQ atm in the Barents Sea region shows that the seasonal variations in dQ oc and dQ atm are nearly in-phase, while their interannual variations are out-of-phase. It is concluded that the basin of the Barents Sea plays an important role in maintaining the feedback mechanism (the Bjerknes compensation) of the ocean-atmosphere system in the Arctic region.

Operational seasonal and interannual predictions of ocean conditions

NASA Technical Reports Server (NTRS)

Leetmaa, Ants

1992-01-01

Dr. Leetmaa described current work at the U.S. National Meteorological Center (NMC) on coupled systems leading to a seasonal prediction system. He described the way in which ocean thermal data is quality controlled and used in a four dimensional data assimilation system. This consists of a statistical interpolation scheme, a primitive equation ocean general circulation model, and the atmospheric fluxes that are required to force this. This whole process generated dynamically consist thermohaline and velocity fields for the ocean. Currently routine weekly analyses are performed for the Atlantic and Pacific oceans. These analyses are used for ocean climate diagnostics and as initial conditions for coupled forecast models. Specific examples of output products were shown both in the Pacific and the Atlantic Ocean.

CDEP Consortium on Ocean Data Assimilation for Seasonal-to-Interannual Prediction (ODASI)

NASA Technical Reports Server (NTRS)

Rienecker, Michele; Zebiak, Stephen; Kinter, James; Behringer, David; Rosati, Antonio; Kaplan, Alexey

2005-01-01

The ODASI consortium is focused activity of the NOAA/OGP/Climate Diagnostics and Experimental Prediction Program with the goal of improving ocean data assimilation methods and their implementations in support of seasonal forecasts with coupled general circulation models. The consortium is undertaking coordinated assimilation experiments, with common forcing data sets and common input data streams. With different assimilation systems and different models, we aim to understand what approach works best in improving forecast skill in the equatorial Pacific. The presentation will provide an overview of the consortium goals and plans and recent results focused towards evaluating data impacts.

Seasonal and interannual cross-shelf transport over the Texas and Louisiana continental shelf

NASA Astrophysics Data System (ADS)

Thyng, Kristen M.; Hetland, Robert D.

2018-05-01

Numerical drifters are tracked in a hydrodynamic simulation of circulation over the Texas-Louisiana shelf to analyze patterns in cross-shelf transport of materials. While the important forcing mechanisms in the region (wind, river, and deep eddies) and associated flow patterns are known, the resultant material transport is less well understood. The primary metric used in the calculations is the percent of drifters released within a region that cross the 100 m isobath. Results of the analysis indicate that, averaged over the eleven years of the simulation, there are two regions on the shelf - over the Texas shelf during winter, and over the Louisiana shelf in summer - with increased seasonal probability for offshore transport. Among the two other distinct regions, the big bend region in Texas has increased probability for onshore transport, and the Mississippi Delta region has an increase in offshore transport, for both seasons. Some of these regions of offshore transport have marked interannual variability. This interannual variability is correlated to interannual changes in forcing conditions. Winter transport off of the Texas shelf is correlated with winter mean wind direction, with more northerly winds enhancing offshore transport; summer transport off the Louisiana shelf is correlated with Mississippi River discharge.

Enhancing seasonal climate prediction capacity for the Pacific countries

NASA Astrophysics Data System (ADS)

Kuleshov, Y.; Jones, D.; Hendon, H.; Charles, A.; Cottrill, A.; Lim, E.-P.; Langford, S.; de Wit, R.; Shelton, K.

2012-04-01

Seasonal and inter-annual climate variability is a major factor in determining the vulnerability of many Pacific Island Countries to climate change and there is need to improve weekly to seasonal range climate prediction capabilities beyond what is currently available from statistical models. In the seasonal climate prediction project under the Australian Government's Pacific Adaptation Strategy Assistance Program (PASAP), we describe a comprehensive project to strengthen the climate prediction capacities in National Meteorological Services in 14 Pacific Island Countries and East Timor. The intent is particularly to reduce the vulnerability of current services to a changing climate, and improve the overall level of information available assist with managing climate variability. Statistical models cannot account for aspects of climate variability and change that are not represented in the historical record. In contrast, dynamical physics-based models implicitly include the effects of a changing climate whatever its character or cause and can predict outcomes not seen previously. The transition from a statistical to a dynamical prediction system provides more valuable and applicable climate information to a wide range of climate sensitive sectors throughout the countries of the Pacific region. In this project, we have developed seasonal climate outlooks which are based upon the current dynamical model POAMA (Predictive Ocean-Atmosphere Model for Australia) seasonal forecast system. At present, meteorological services of the Pacific Island Countries largely employ statistical models for seasonal outlooks. Outcomes of the PASAP project enhanced capabilities of the Pacific Island Countries in seasonal prediction providing National Meteorological Services with an additional tool to analyse meteorological variables such as sea surface temperatures, air temperature, pressure and rainfall using POAMA outputs and prepare more accurate seasonal climate outlooks.

Modeling seasonal and interannual variability in ecosystem carbon cycling for the Brazilian Amazon region

NASA Astrophysics Data System (ADS)

Potter, Christopher; Klooster, Steven; de Carvalho, Claudio Reis; Genovese, Vanessa Brooks; Torregrosa, Alicia; Dungan, Jennifer; Bobo, Matthew; Coughlan, Joseph

2001-05-01

Previous field measurements have implied that undisturbed Amazon forests may represent a substantial terrestrial sink for atmospheric carbon dioxide. We investigated this hypothesis using a regional ecosystem model for net primary production (NPP) and soil biogeochemical cycling. Seasonal and interannual controls on net ecosystem production (NEP) were studied with integration of high-resolution (8-km) multiyear satellite data to characterize Amazon land surface properties over time. Background analysis of temporal and spatial relationships between regional rainfall patterns and satellite observations (for vegetation land cover, fire counts, and smoke aerosol effects) reveals several notable patterns in the model driver data. Autocorrelation analysis for monthly vegetation "greenness" index (normalized difference vegetation index, NDVI) from the advanced very high resolution radiometer (AVHRR) and monthly rainfall indicates a significant lag time correlation of up to 12 months. At lag times approaching 36 months, autocorrelation function (ACF) values did not exceed the 95% confidence interval at locations west of about 47°W, which is near the transition zone of seasonal tropical forest and other (nonforest) vegetation types. Even at lag times of 12 months or less, the location near Manaus (approximately 60°W) represents the farthest western point in the Amazon region where seasonality of rainfall accounts significantly for monthly variations in forest phenology, as observed using NDVI. Comparisons of NDVI seasonal profiles in areas of the eastern Amazon widely affected by fires (as observed from satellite) suggest that our adjusted AVHRR-NDVI captures year-to-year variation in land cover greenness with minimal interference from small fires and smoke aerosols. Ecosystem model results using this newly generated combination of regional forcing data from satellite suggest that undisturbed Amazon forests can be strong net sinks for atmospheric carbon dioxide

Quantifying the Contribution of Wind-Driven Linear Response to the Seasonal and Interannual Variability of Amoc Volume Transports Across 26.5ºN

NASA Astrophysics Data System (ADS)

Shimizu, K.; von Storch, J. S.; Haak, H.; Nakayama, K.; Marotzke, J.

2014-12-01

Surface wind stress is considered to be an important forcing of the seasonal and interannual variability of Atlantic Meridional Overturning Circulation (AMOC) volume transports. A recent study showed that even linear response to wind forcing captures observed features of the mean seasonal cycle. However, the study did not assess the contribution of wind-driven linear response in realistic conditions against the RAPID/MOCHA array observation or Ocean General Circulation Model (OGCM) simulations, because it applied a linear two-layer model to the Atlantic assuming constant upper layer thickness and density difference across the interface. Here, we quantify the contribution of wind-driven linear response to the seasonal and interannual variability of AMOC transports by comparing wind-driven linear simulations under realistic continuous stratification against the RAPID observation and OCGM (MPI-OM) simulations with 0.4º resolution (TP04) and 0.1º resolution (STORM). All the linear and MPI-OM simulations capture more than 60% of the variance in the observed mean seasonal cycle of the Upper Mid-Ocean (UMO) and Florida Strait (FS) transports, two components of the upper branch of the AMOC. The linear and TP04 simulations also capture 25-40% of the variance in the observed transport time series between Apr 2004 and Oct 2012; the STORM simulation does not capture the observed variance because of the stochastic signal in both datasets. Comparison of half-overlapping 12-month-long segments reveals some periods when the linear and TP04 simulations capture 40-60% of the observed variance, as well as other periods when the simulations capture only 0-20% of the variance. These results show that wind-driven linear response is a major contributor to the seasonal and interannual variability of the UMO and FS transports, and that its contribution varies in an interannual timescale, probably due to the variability of stochastic processes.

Interannual, seasonal and diurnal Mars surface environmental cycles observed from Viking to Curiosity

NASA Astrophysics Data System (ADS)

Martinez, German; Vicente-Retortillo, Álvaro; Kemppinen, Osku; Fischer, Erik; Fairen, Alberto G.; Guzewich, Scott David; Haberle, Robert; Lemmon, Mark T.; Newman, Claire E.; Renno, Nilton O.; Richardson, Mark I.; Smith, Michael D.; De la Torre, Manuel; Vasavada, Ashwin R.

2016-10-01

We analyze in-situ environmental data from the Viking landers to the Curiosity rover to estimate atmospheric pressure, near-surface air and ground temperature, relative humidity, wind speed and dust opacity with the highest confidence possible. We study the interannual, seasonal and diurnal variability of these quantities at the various landing sites over a span of more than twenty Martian years to characterize the climate on Mars and its variability. Additionally, we characterize the radiative environment at the various landing sites by estimating the daily UV irradiation (also called insolation and defined as the total amount of solar UV energy received on flat surface during one sol) and by analyzing its interannual and seasonal variability.In this study we use measurements conducted by the Viking Meteorology Instrument System (VMIS) and Viking lander camera onboard the Viking landers (VL); the Atmospheric Structure Instrument/Meteorology (ASIMET) package and the Imager for Mars Pathfinder (IMP) onboard the Mars Pathfinder (MPF) lander; the Miniature Thermal Emission Spectrometer (Mini-TES) and Pancam instruments onboard the Mars Exploration Rovers (MER); the Meteorological Station (MET), Thermal Electrical Conductivity Probe (TECP) and Phoenix Surface Stereo Imager (SSI) onboard the Phoenix (PHX) lander; and the Rover Environmental Monitoring Station (REMS) and Mastcam instrument onboard the Mars Science Laboratory (MSL) rover.A thorough analysis of in-situ environmental data from past and present missions is important to aid in the selection of the Mars 2020 landing site. We plan to extend our analysis of Mars surface environmental cycles by using upcoming data from the Temperature and Wind sensors (TWINS) instrument onboard the InSight mission and the Mars Environmental Dynamics Analyzer (MEDA) instrument onboard the Mars 2020 mission.

The role of ecosystem memory in predicting inter-annual variations of the tropical carbon balance.

NASA Astrophysics Data System (ADS)

Bloom, A. A.; Liu, J.; Bowman, K. W.; Konings, A. G.; Saatchi, S.; Worden, J. R.; Worden, H. M.; Jiang, Z.; Parazoo, N.; Williams, M. D.; Schimel, D.

2017-12-01

Understanding the trajectory of the tropical carbon balance remains challenging, in part due to large uncertainties in the integrated response of carbon cycle processes to climate variability. Satellite observations atmospheric CO2 from GOSAT and OCO-2, together with ancillary satellite measurements, provide crucial constraints on continental-scale terrestrial carbon fluxes. However, an integrated understanding of both climate forcings and legacy effects (or "ecosystem memory") on the terrestrial carbon balance is ultimately needed to reduce uncertainty on its future trajectory. Here we use the CARbon DAta-MOdel fraMework (CARDAMOM) diagnostic model-data fusion approach - constrained by an array of C cycle satellite surface observations, including MODIS leaf area, biomass, GOSAT solar-induced fluorescence, as well as "top-down" atmospheric inversion estimates of CO2 and CO surface fluxes from the NASA Carbon Monitoring System Flux (CMS-Flux) - to constrain and predict spatially-explicit tropical carbon state variables during 2010-2015. We find that the combined assimilation of land surface and atmospheric datasets places key constraints on the temperature sensitivity and first order carbon-water feedbacks throughout the tropics and combustion factors within biomass burning regions. By varying the duration of the assimilation period, we find that the prediction skill on inter-annual net biospheric exchange is primarily limited by record length rather than model structure and process representation. We show that across all tropical biomes, quantitative knowledge of memory effects - which account for 30-50% of interannual variations across the tropics - is critical for understanding and ultimately predicting the inter-annual tropical carbon balance.

Skilful Seasonal Predictions of Summer European Rainfall

NASA Astrophysics Data System (ADS)

Dunstone, Nick; Smith, Doug; Scaife, Adam; Hermanson, Leon; Fereday, David; O'Reilly, Chris; Stirling, Alison; Eade, Rosie; Gordon, Margaret; MacLachlan, Craig; Woollings, Tim; Sheen, Katy; Belcher, Stephen

2018-04-01

Year-to-year variability in Northern European summer rainfall has profound societal and economic impacts; however, current seasonal forecast systems show no significant forecast skill. Here we show that skillful predictions are possible (r 0.5, p < 0.001) using the latest high-resolution Met Office near-term prediction system over 1960-2017. The model predictions capture both low-frequency changes (e.g., wet summers 2007-2012) and some of the large individual events (e.g., dry summer 1976). Skill is linked to predictable North Atlantic sea surface temperature variability changing the supply of water vapor into Northern Europe and so modulating convective rainfall. However, dynamical circulation variability is not well predicted in general—although some interannual skill is found. Due to the weak amplitude of the forced model signal (likely caused by missing or weak model responses), very large ensembles (>80 members) are required for skillful predictions. This work is promising for the development of European summer rainfall climate services.

Seasonal-to-Interannual Variability in Antarctic Sea-Ice Dynamics, and Its Impact on Surface Fluxes and Water Mass Production

NASA Technical Reports Server (NTRS)

Drinkwater, Mark R.

1999-01-01

Strong seasonal and interannual signals in Antarctic bottom-water outflow remain unexplained yet are highly correlated with anomalies in net sea-ice growth in coastal polynyas. The mechanisms responsible for driving salination and replenishment and rejuvenation of the dense shelf "source" waters likely also generate pulses of bottom water outflow. The objective of this research is to investigate time-scales of variability in the dynamics of sea-ice in the Southern Ocean in order to determine the primary sites for production of dense shelf waters. We are using a merged satellite/buoy sea-ice motion data set for the period 1978-present day to compute the dynamics of opening and closing of coastal polynyas over the continental shelf. The Ocean Circulation and Climate Advanced Model (OCCAM) ocean general circulation model with coupled sea-ice dynamics is presently forced using National Center for Environmental Prediction (NCEP) data to simulate fluxes and the salination impact of the ocean shelf regions. This work is relevant in the context of measuring the influence of polar sea-ice dynamics upon polar ocean characteristics, and thereby upon global thermohaline ocean circulation. Interannual variability in simulated net freezing rate in the Southern Weddell Sea is shown for the period 1986-1993. There is a pronounced maximum of ice production in 1988 and minimum in 1991 in response to anomalies in equatorward meridional wind velocity. This follows a similar approximate 8-year interannual cycle in Sea Surface Temperature (SST) and satellite-derived ice-edge anomalies reported elsewhere as the "Antarctic Circumpolar Wave." The amplitude of interannual fluctuations in annual net ice production are about 40% of the mean value, implying significant interannual variance in brine rejection and upper ocean heat loss. Southward anomalies in wind stress induce negative anomalies in open water production, which are observed in passive microwave satellite images. Thus, cycles of

Interannual variability and predictability over the Arabian Penuinsula Winter monsoon region

NASA Astrophysics Data System (ADS)

Adnan Abid, Muhammad; Kucharski, Fred; Almazroui, Mansour; Kang, In-Sik

2016-04-01

Interannual winter rainfall variability and its predictability are analysed over the Arabian Peninsula region by using observed and hindcast datasets from the state-of-the-art European Centre for Medium-Range Weather Forecasts (ECMWF) seasonal prediction System 4 for the period 1981-2010. An Arabian winter monsoon index (AWMI) is defined to highlight the Arabian Peninsula as the most representative region for the Northern Hemispheric winter dominating the summer rainfall. The observations show that the rainfall variability is relatively large over the northeast of the Arabian Peninsula. The correlation coefficient between the Nino3.4 index and rainfall in this region is 0.33, suggesting potentially some modest predictability, and indicating that El Nino increases and La Nina decreases the rainfall. Regression analysis shows that upper-level cyclonic circulation anomalies that are forced by El Nino Southern Oscillation (ENSO) are responsible for the winter rainfall anomalies over the Arabian region. The stronger (weaker) mean transient-eddy activity related to the upper-level trough induced by the warm (cold) sea-surface temperatures during El Nino (La Nina) tends to increase (decrease) the rainfall in the region. The model hindcast dataset reproduces the ENSO-rainfall connection. The seasonal mean predictability of the northeast Arabian rainfall index is 0.35. It is shown that the noise variance is larger than the signal over the Arabian Peninsula region, which tends to limit the prediction skill. The potential predictability is generally increased in ENSO years and is, in particular, larger during La Nina compared to El Nino years in the region. Furthermore, central Pacific ENSO events and ENSO events with weak signals in the Indian Ocean tend to increase predictability over the Arabian region.

The seasonal cycle revisited: interannual variation and ecosystem consequences

NASA Astrophysics Data System (ADS)

Bertram, Douglas F.; Mackas, David L.; McKinnell, Stewart M.

The annual seasonal cycle accounts for much of the total temporal variability of mid- and high-latitude marine ecosystems. Although the general pattern of the seasons repeats each year, climatic variability of the atmosphere and the ocean produce detectable changes in intensity and onset timing. We use a combination of time series data from oceanographic, zooplankton and seabird breeding data to ask if and how these variations in the timing of the spring growing season affect marine populations. For the physical environment, we develop an annual index of spring timing by fitting a non-linear 2-parameter periodic function to the average weekly SST data observed in British Columbia from 1 January to the end of August each year. For each year, the phase parameter describes the timing of seasonal warming (the timing index) and the amplitude parameter describes the magnitude of the temperature increase between the fitted winter minimum and summer maximum. For the zooplankton, which have annual and strongly synchronous cycles of biomass, productivity, and developmental sequence, we use copepodite stage composition to index the timing of the annual maximum. For seabirds, we examine (1975-1999) the timing of hatching, nestling growth performance, and diet for four species of alcids at Triangle Island, British Columbia's largest seabird colony and the world's largest population of the planktivorous Cassin's auklet. Temperature, zooplankton, and seabirds have all shown recent decadal trends toward ‘earlier spring’, but the magnitudes of the timing perturbations have differed from variable to variable and from year to year. Recent (1996-1999) extreme interannual variation in spring timing and April SST helped to facilitate a mechanistic investigation of oceanographic features that affect the reproductive performance of seabirds. Our results demonstrate a significant negative relationship between the annual spring timing index (and April mean SST) and nestling growth rates

Intra-Seasonal Rainfall Variations and Linkage with Kharif Crop Production: An Attempt to Evaluate Predictability of Sub-Seasonal Rainfall Events

NASA Astrophysics Data System (ADS)

Singh, Ankita; Ghosh, Kripan; Mohanty, U. C.

2018-03-01

The sub-seasonal variation of Indian summer monsoon rainfall highly impacts Kharif crop production in comparison with seasonal total rainfall. The rainfall frequency and intensity corresponding to various rainfall events are found to be highly related to crop production and therefore, the predictability of such events are considered to be diagnosed. Daily rainfall predictions are made available by one of the coupled dynamical model National Centers for Environmental Prediction Climate Forecast System (NCEPCFS). A large error in the simulation of daily rainfall sequence influences to take up a bias correction and for that reason, two approaches are used. The bias-corrected GCM is able to capture the inter-annual variability in rainfall events. Maximum prediction skill of frequency of less rainfall (LR) event is observed during the month of September and a similar result is also noticed for moderate rainfall event with maximum skill over the central parts of the country. On the other hand, the impact of rainfall weekly rainfall intensity is evaluated against the Kharif rice production. It is found that weekly rainfall intensity during July is having a significant impact on Kharif rice production, but the corresponding skill was found very low in GCM. The GCM are able to simulate the less and moderate rainfall frequency with significant skill.

Seasonal and inter-annual variability of the net ecosystem CO2 exchange of a temperate mountain grassland: effects of climate and management.

PubMed

Wohlfahrt, Georg; Hammerle, Albin; Haslwanter, Alois; Bahn, Michael; Tappeiner, Ulrike; Cernusca, Alexander

2008-04-27

The role and relative importance of climate and cutting for the seasonal and inter-annual variability of the net ecosystem CO 2 (NEE) of a temperate mountain grassland was investigated. Eddy covariance CO 2 flux data and associated measurements of the green area index and the major environmental driving forces acquired during 2001-2006 at the study site Neustift (Austria) were analyzed. Driven by three cutting events per year which kept the investigated grassland in a stage of vigorous growth, the seasonal variability of NEE was primarily modulated by gross primary productivity (GPP). The role of environmental parameters in modulating the seasonal variability of NEE was obscured by the strong response of GPP to changes in the amount of green area, as well as the cutting-mediated decoupling of phenological development and the seasonal course of climate drivers. None of the climate and management metrics examined was able to explain the inter-annual variability of annual NEE. This is thought to result from (1) a high covariance between GPP and ecosystem respiration (R eco ) at the annual time scale which results in a comparatively small inter-annual variation of NEE, (2) compensating effects between carbon exchange during and outside the management period, and (3) changes in the biotic response to rather than the climate variables per se. GPP was more important in modulating inter-annual variations in NEE in spring and before the first and second cut, while R eco explained a larger fraction of the inter-annual variability of NEE during the remaining, in particular the post-cut, periods.

Understanding the Role of Biology in the Global Environment: NASA'S Mission to Planet Earth

NASA Technical Reports Server (NTRS)

Townsend, William F.

1996-01-01

NASA has long used the unique perspective of space as a means of expanding our understanding of how the Earth's environment functions. In particular, the linkages between land, air, water, and life-the elements of the Earth system-are a focus for NASA's Mission to Planet Earth. This approach, called Earth system science, blends together fields like meteorology, biology, oceanography, and atmospheric science. Mission to Planet Earth uses observations from satellites, aircraft, balloons, and ground researchers as the basis for analysis of the elements of the Earth system, the interactions between those elements, and possible changes over the coming years and decades. This information is helping scientists improve our understanding of how natural processes affect us and how we might be affecting them. Such studies will yield improved weather forecasts, tools for managing agriculture and forests, information for fishermen and local planners, and, eventually, an enhanced ability to predict how the climate will change in the future. NASA has designed Mission to Planet Earth to focus on five primary themes: Land Cover and Land Use Change; Seasonal to Interannual Climate Prediction; Natural Hazards; Long-Term Climate Variability; and Atmosphere Ozone.

Seasonal to interannual morphodynamics along a high-energy dissipative littoral cell

USGS Publications Warehouse

Ruggiero, P.; Kaminsky, G.M.; Gelfenbaum, G.; Voigt, B.

2005-01-01

A beach morphology monitoring program was initiated during summer 1997 along the Columbia River littoral cell (CRLC) on the coasts of northwest Oregon and southwest Washington, USA. This field program documents the seasonal through interannual morphological variability of these high-energy dissipative beaches over a variety of spatial scales. Following the installation of a dense network of geodetic control monuments, a nested sampling scheme consisting of cross-shore topographic beach profiles, three-dimensional topographic beach surface maps, nearshore bathymetric surveys, and sediment size distribution analyses was initiated. Beach monitoring is being conducted with state-of-the-art real-time kinematic differential global positioning system survey methods that combine both high accuracy and speed of measurement. Sampling methods resolve variability in beach morphology at alongshore length scales of approximately 10 meters to approximately 100 kilometers and cross-shore length scales of approximately 1 meter to approximately 2 kilometers. During the winter of 1997/1998, coastal change in the US Pacific Northwest was greatly influenced by one of the strongest El Nin??o events on record. Steeper than typical southerly wave angles resulted in alongshore sediment transport gradients and shoreline reorientation on a regional scale. The La Nin??a of 1998/1999, dominated by cross-shore processes associated with the largest recorded wave year in the region, resulted in net beach erosion along much of the littoral cell. The monitoring program successfully documented the morphological response to these interannual forcing anomalies as well as the subsequent beach recovery associated with three consecutive moderate wave years. These morphological observations within the CRLC can be generalized to explain overall system patterns; however, distinct differences in large-scale coastal behavior (e.g., foredune ridge morphology, sandbar morphometrics, and nearshore beach slopes

Prediction and Predictability of the Madden Julian Oscillation in the NASA GEOS-5 Seasonal-to-Subseasonal System

NASA Technical Reports Server (NTRS)

Achuthavarier, Deepthi; Koster, Randal; Marshak, Jelena; Schubert, Siegfried; Molod, Andrea

2018-01-01

In this study, we examine the prediction skill and predictability of the Madden Julian Oscillation (MJO) in a recent version of the NASA GEOS-5 atmosphere-ocean coupled model run at at 1/2 degree horizontal resolution. The results are based on a suite of hindcasts produced as part of the NOAA SubX project, consisting of seven ensemble members initialized every 5 days for the period 1999-2015. The atmospheric initial conditions were taken from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the ocean and the sea ice were taken from a GMAO ocean analysis. The land states were initialized from the MERRA-2 land output, which is based on observation-corrected precipitation fields. We investigated the MJO prediction skill in terms of the bivariate correlation coefficient for the real-time multivariate MJO (RMM) indices. The correlation coefficient stays at or above 0.5 out to forecast lead times of 26-36 days, with a pronounced increase in skill for forecasts initialized from phase 3, when the MJO convective anomaly is located in the central tropical Indian Ocean. A corresponding estimate of the upper limit of the predictability is calculated by considering a single ensemble member as the truth and verifying the ensemble mean of the remaining members against that. The predictability estimates fall between 35-37 days (taken as forecast lead when the correlation reaches 0.5) and are rather insensitive to the initial MJO phase. The model shows slightly higher skill when the initial conditions contain strong MJO events compared to weak events, although the difference in skill is evident only from lead 1 to 20. Similar to other models, the RMM-index-based skill arises mostly from the circulation components of the index. The skill of the convective component of the index drops to 0.5 by day 20 as opposed to day 30 for circulation fields. The propagation of the MJO anomalies over the Maritime Continent does not appear problematic in

Seasonal and interannual variability in wetland methane emissions simulated by CLM4Me' and CAM-chem and comparisons to observations of concentrations

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

Meng, L.; Paudel, R.; Hess, P. G. M.

Understanding the temporal and spatial variation of wetland methane emissions is essential to the estimation of the global methane budget. Our goal for this study is three-fold: (i) to evaluate the wetland methane fluxes simulated in two versions of the Community Land Model, the Carbon-Nitrogen (CN; i.e., CLM4.0) and the Biogeochemistry (BGC; i.e., CLM4.5) versions using the methane emission model CLM4Me' so as to determine the sensitivity of the emissions to the underlying carbon model; (ii) to compare the simulated atmospheric methane concentrations to observations, including latitudinal gradients and interannual variability so as to determine the extent to which themore » atmospheric observations constrain the emissions; (iii) to understand the drivers of seasonal and interannual variability in atmospheric methane concentrations. Simulations of the transport and removal of methane use the Community Atmosphere Model with chemistry (CAM-chem) model in conjunction with CLM4Me' methane emissions from both CN and BGC simulations and other methane emission sources from literature. In each case we compare model-simulated atmospheric methane concentration with observations. In addition, we simulate the atmospheric concentrations based on the TransCom wetland and rice paddy emissions derived from a different terrestrial ecosystem model, Vegetation Integrative Simulator for Trace gases (VISIT). Our analysis indicates CN wetland methane emissions are higher in the tropics and lower at high latitudes than emissions from BGC. In CN, methane emissions decrease from 1993 to 2004 while this trend does not appear in the BGC version. In the CN version, methane emission variations follow satellite-derived inundation wetlands closely. However, they are dissimilar in BGC due to its different carbon cycle. CAM-chem simulations with CLM4Me' methane emissions suggest that both prescribed anthropogenic and predicted wetlands methane emissions contribute substantially to seasonal and

Seasonal and interannual variability in wetland methane emissions simulated by CLM4Me' and CAM-chem and comparisons to observations of concentrations

DOE PAGES

Meng, L.; Paudel, R.; Hess, P. G. M.; ...

2015-07-03

Understanding the temporal and spatial variation of wetland methane emissions is essential to the estimation of the global methane budget. Our goal for this study is three-fold: (i) to evaluate the wetland methane fluxes simulated in two versions of the Community Land Model, the Carbon-Nitrogen (CN; i.e., CLM4.0) and the Biogeochemistry (BGC; i.e., CLM4.5) versions using the methane emission model CLM4Me' so as to determine the sensitivity of the emissions to the underlying carbon model; (ii) to compare the simulated atmospheric methane concentrations to observations, including latitudinal gradients and interannual variability so as to determine the extent to which themore » atmospheric observations constrain the emissions; (iii) to understand the drivers of seasonal and interannual variability in atmospheric methane concentrations. Simulations of the transport and removal of methane use the Community Atmosphere Model with chemistry (CAM-chem) model in conjunction with CLM4Me' methane emissions from both CN and BGC simulations and other methane emission sources from literature. In each case we compare model-simulated atmospheric methane concentration with observations. In addition, we simulate the atmospheric concentrations based on the TransCom wetland and rice paddy emissions derived from a different terrestrial ecosystem model, Vegetation Integrative Simulator for Trace gases (VISIT). Our analysis indicates CN wetland methane emissions are higher in the tropics and lower at high latitudes than emissions from BGC. In CN, methane emissions decrease from 1993 to 2004 while this trend does not appear in the BGC version. In the CN version, methane emission variations follow satellite-derived inundation wetlands closely. However, they are dissimilar in BGC due to its different carbon cycle. CAM-chem simulations with CLM4Me' methane emissions suggest that both prescribed anthropogenic and predicted wetlands methane emissions contribute substantially to seasonal and

Determination of Interannual to Decadal Changes in Ice Sheet Mass Balance from Satellite Altimetry

NASA Technical Reports Server (NTRS)

Zwally, H. Jay; Busalacchi, Antonioa J. (Technical Monitor)

2001-01-01

A major uncertainty in predicting sea level rise is the sensitivity of ice sheet mass balance to climate change, as well as the uncertainty in present mass balance. Since the annual water exchange is about 8 mm of global sea level equivalent, the +/- 25% uncertainty in current mass balance corresponds to +/- 2 mm/yr in sea level change. Furthermore, estimates of the sensitivity of the mass balance to temperature change range from perhaps as much as - 10% to + 10% per K. Although the overall ice mass balance and seasonal and inter-annual variations can be derived from time-series of ice surface elevations from satellite altimetry, satellite radar altimeters have been limited in spatial coverage and elevation accuracy. Nevertheless, new data analysis shows mixed patterns of ice elevation increases and decreases that are significant in terms of regional-scale mass balances. In addition, observed seasonal and interannual variations in elevation demonstrate the potential for relating the variability in mass balance to changes in precipitation, temperature, and melting. From 2001, NASA's ICESat laser altimeter mission will provide significantly better elevation accuracy and spatial coverage to 86 deg latitude and to the margins of the ice sheets. During 3 to 5 years of ICESat-1 operation, an estimate of the overall ice sheet mass balance and sea level contribution will be obtained. The importance of continued ice monitoring after the first ICESat is illustrated by the variability in the area of Greenland surface melt observed over 17-years and its correlation with temperature. In addition, measurement of ice sheet changes, along with measurements of sea level change by a series of ocean altimeters, should enable direct detection of ice level and global sea level correlations.

Seasonal and interannual dynamics of soil microbial biomass and available nitrogen in an alpine meadow in the eastern part of Qinghai-Tibet Plateau, China

NASA Astrophysics Data System (ADS)

Xu, Bo; Wang, Jinniu; Wu, Ning; Wu, Yan; Shi, Fusun

2018-01-01

Soil microbial activity varies seasonally in frozen alpine soils during cold seasons and plays a crucial role in available N pool accumulation in soil. The intra- and interannual patterns of microbial and nutrient dynamics reflect the influences of changing weather factors, and thus provide important insights into the biogeochemical cycles and ecological functions of ecosystems. We documented the seasonal and interannual dynamics of soil microbial and available N in an alpine meadow in the eastern part of Qinghai-Tibet Plateau, China, between April 2011 and October 2013. Soil was collected in the middle of each month and analyzed for water content, microbial biomass C (MBC) and N (MBN), dissolved organic C and N, and inorganic N. Soil microbial community composition was measured by the dilution-plate method. Fungi and actinomycetes dominated the microbial community during the nongrowing seasons, and the proportion of bacteria increased considerably during the early growing seasons. Trends of consistently increasing MBC and available N pools were observed during the nongrowing seasons. MBC sharply declined during soil thaw and was accompanied by a peak in available N pool. Induced by changes in soil temperatures, significant shifts in the structures and functions of microbial communities were observed during the winter-spring transition and largely contributed to microbial reduction. The divergent seasonal dynamics of different N forms showed a complementary nutrient supply pattern during the growing season. Similarities between the interannual dynamics of microbial biomass and available N pools were observed, and soil temperature and water conditions were the primary environmental factors driving interannual fluctuations. Owing to the changes in climate, seasonal soil microbial activities and nutrient supply patterns are expected to change further, and these changes may have crucial implications for the productivity and biodiversity of alpine ecosystems.

Land-surface initialisation improves seasonal climate prediction skill for maize yield forecast.

PubMed

Ceglar, Andrej; Toreti, Andrea; Prodhomme, Chloe; Zampieri, Matteo; Turco, Marco; Doblas-Reyes, Francisco J

2018-01-22

Seasonal crop yield forecasting represents an important source of information to maintain market stability, minimise socio-economic impacts of crop losses and guarantee humanitarian food assistance, while it fosters the use of climate information favouring adaptation strategies. As climate variability and extremes have significant influence on agricultural production, the early prediction of severe weather events and unfavourable conditions can contribute to the mitigation of adverse effects. Seasonal climate forecasts provide additional value for agricultural applications in several regions of the world. However, they currently play a very limited role in supporting agricultural decisions in Europe, mainly due to the poor skill of relevant surface variables. Here we show how a combined stress index (CSI), considering both drought and heat stress in summer, can predict maize yield in Europe and how land-surface initialised seasonal climate forecasts can be used to predict it. The CSI explains on average nearly 53% of the inter-annual maize yield variability under observed climate conditions and shows how concurrent heat stress and drought events have influenced recent yield anomalies. Seasonal climate forecast initialised with realistic land-surface achieves better (and marginally useful) skill in predicting the CSI than with climatological land-surface initialisation in south-eastern Europe, part of central Europe, France and Italy.

Seasonal and inter-annual snowmelt patterns in the southern Sierra Nevada, California

NASA Astrophysics Data System (ADS)

Musselman, K. N.; Molotch, N. P.; Margulis, S. A.

2012-12-01

In the Sierra Nevada, seasonal snow represents a critical component of California's water resource infrastructure in that it affords reliable water during otherwise arid summers. Complex spatial, seasonal and inter-annual snowmelt patterns determine when and where that meltwater is available. Our knowledge of snowmelt dynamics is typically limited to what we can infer from sparse, point-scale snow measurement stations. Limitations such as these motivate the use of numerical snowmelt models. We evaluate the ability of the Alpine3D model system to represent three years of snow dynamics over an 1800 km2 area of Sequoia National Park. The domain spans a 3600 m elevation gradient and ecosystems ranging from semi-arid grasslands to massive sequoia stands to alpine tundra. The model results were evaluated against data from a multi-scale measurement campaign that included airborne LiDAR, clusters of snow depth sensors, repeated manual snow surveys, and automated SWE stations. Compared to these measurements, Alpine3D consistently performed well in middle elevation conifer forests; compared to LiDAR data, the mean snow depth error in forested regions was < 2%. The model also simulated the snow disappearance date within two days of that measured by regional automated sensors. At upper elevations, however, the model tended to overestimate SWE by 50% to as much as 100% in some areas and the errors were linearly correlated (R2 > 0.80, p<0.01) with the distance of the SWE measurements from the nearest precipitation gauge used to derive the model forcing. The results suggest that Alpine3D is highly accurate during the melt season and that precipitation uncertainty may be a critical limitation on snow model accuracy. Finally, an analysis of seasonal and inter-annual snowmelt patterns highlighted distinct melt differences between lower, middle, and upper elevations. Snowmelt was generally most frequent (70% - 95% of the snow-covered season) at the lower elevations where snow cover

MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center

NASA Astrophysics Data System (ADS)

Liu, Xiangwen; Wu, Tongwen; Yang, Song; Li, Tim; Jie, Weihua; Zhang, Li; Wang, Zaizhi; Liang, Xiaoyun; Li, Qiaoping; Cheng, Yanjie; Ren, Hongli; Fang, Yongjie; Nie, Suping

2017-05-01

By conducting several sets of hindcast experiments using the Beijing Climate Center Climate System Model, which participates in the Sub-seasonal to Seasonal (S2S) Prediction Project, we systematically evaluate the model's capability in forecasting MJO and its main deficiencies. In the original S2S hindcast set, MJO forecast skill is about 16 days. Such a skill shows significant seasonal-to-interannual variations. It is found that the model-dependent MJO forecast skill is more correlated with the Indian Ocean Dipole (IOD) than with the El Niño-Southern Oscillation. The highest skill is achieved in autumn when the IOD attains its maturity. Extended skill is found when the IOD is in its positive phase. MJO forecast skill's close association with the IOD is partially due to the quickly strengthening relationship between MJO amplitude and IOD intensity as lead time increases to about 15 days, beyond which a rapid weakening of the relationship is shown. This relationship transition may cause the forecast skill to decrease quickly with lead time, and is related to the unrealistic amplitude and phase evolutions of predicted MJO over or near the equatorial Indian Ocean during anomalous IOD phases, suggesting a possible influence of exaggerated IOD variability in the model. The results imply that the upper limit of intraseasonal predictability is modulated by large-scale external forcing background state in the tropical Indian Ocean. Two additional sets of hindcast experiments with improved atmosphere and ocean initial conditions (referred to as S2S_IEXP1 and S2S_IEXP2, respectively) are carried out, and the results show that the overall MJO forecast skill is increased to 21-22 days. It is found that the optimization of initial sea surface temperature condition largely accounts for the increase of the overall MJO forecast skill, even though the improved initial atmosphere conditions also play a role. For the DYNAMO/CINDY field campaign period, the forecast skill increases

Interannual and seasonal variability of water use efficiency in a tropical rainforest: Results from a 9 year eddy flux time series

NASA Astrophysics Data System (ADS)

Tan, Zheng-Hong; Zhang, Yi-Ping; Deng, Xiao-Bao; Song, Qing-Hai; Liu, Wen-Jie; Deng, Yun; Tang, Jian-Wei; Liao, Zhi-Yong; Zhao, Jun-Fu; Song, Liang; Yang, Lian-Yan

2015-01-01

used a continuous 9 year (2003-2011) eddy flux time series with 30 min resolution to examine water use efficiency in a tropical rainforest and determine its environmental controls. The multiyear mean water use efficiency (Wue) of this rainforest was 3.16 ± 0.33 gC per kg H2O, which is close to that of boreal forests, but higher than subtropical forests, and lower than temperate forests. The water vapor deficit (VPD) had a strong impact on instantaneous Wue, in the manner predicted by stomatal optimization theory. At the seasonal scale, temperature was the dominant controller of Wue. The negative correlation between temperature and Wue was probably caused by high continuous photosynthesis during low-temperature periods. The VPD did not correlate with Wue at the interannual scale. No interannual trend was detected in Wue or inherent water use efficiency (Wei), either annually or seasonally. The fact that no increasing trend of Wei was found in the studied tropical rainforest, along with other evidence of CO2 stimulation in tropical rainforests, requires special attention and data validation. There was no significant difference between Wue during a drought and the 9 year mean values in the forest we studied, but we found that dry season transpiration (Tr) was consistently lower during the drought compared to the mean values. Finally, whether Wue increases or decreases during a drought is determined by the drought sensitivity of gross primary production (GPP).

Links between the Amundsen Sea Low and sea ice in the Ross Sea: seasonal and interannual relationships

NASA Astrophysics Data System (ADS)

Raphael, Marilyn N.; Holland, Marika M.; Landrum, Laura; Hobbs, William R.

2018-05-01

Previous studies have shown that sea ice extent in the Southern Ocean is influenced by the intensity and location of the Amundsen Sea Low (ASL), through their effect on the meridional winds. However, the inhomogeneous nature of the influence of the ASL on sea ice as well as its influence during critical periods of the sea ice annual cycle is not clear. In this study, we do a spatio-temporal analysis of links between the ASL and the sea ice during the advance and retreat periods of the ice over the period 1979-2013 focusing on the role of the meridional and zonal winds. We use the ERA-Interim monthly-averaged 500 mb geopotential height and 10 m wind data along with monthly Passive Microwave Sea Ice Concentrations (SIC) to examine the seasonal and interannual relationships between the ASL and SIC in the Ross-Amundsen sea ice sector. To characterize the state of the ASL we use indices that describe its location and its intensity. We show that the ASL has preferred locations and intensities during ice advance and retreat seasons. The strength and direction of the influence of the ASL are not spatially homogeneous and can change from advance to retreat season and there are strong significant relationships between the characteristics of the ASL and SIC, within and across seasons and interannually.

Subseasonal to Seasonal Predictions of U.S. West Coast High Water Levels

NASA Astrophysics Data System (ADS)

Khouakhi, A.; Villarini, G.; Zhang, W.; Slater, L. J.

2017-12-01

Extreme sea levels pose a significant threat to coastal communities, ecosystems, and assets, as they are conducive to coastal flooding, coastal erosion and inland salt-water intrusion. As sea levels continue to rise, these sea level extremes - including occasional minor coastal flooding experienced during high tide (nuisance floods) - are of concern. Extreme sea levels are increasing at many locations around the globe and have been attributed largely to rising mean sea levels associated with intra-seasonal to interannual climate processes such as the El Niño-Southern Oscillation (ENSO). Here, intra-seasonal to seasonal probabilistic forecasts of high water levels are computed at the Toke Point tide gage station on the US west coast. We first identify the main climate drivers that are responsible for high water levels and examine their predictability using General Circulation Models (GCMs) from the North American Multi-Model Ensemble (NMME). These drivers are then used to develop a probabilistic framework for the seasonal forecasting of high water levels. We focus on the climate controls on the frequency of high water levels using the number of exceedances above the 99.5th percentile and above the nuisance flood level established by the National Weather Service. Our findings indicate good forecast skill at the shortest lead time, with the skill that decreases as we increase the lead time. In general, these models aptly capture the year-to-year variability in the observational records.

Predictable and unpredictable modes of seasonal mean precipitation over Northeast China

NASA Astrophysics Data System (ADS)

Ying, Kairan; Frederiksen, Carsten S.; Zhao, Tianbao; Zheng, Xiaogu; Xiong, Zhe; Yi, Xue; Li, Chunxiang

2018-04-01

This study investigates the patterns of interannual variability that arise from the potentially predictable (slow) and unpredictable (intraseasonal) components of seasonal mean precipitation over Northeast (NE) China, using observations from a network of 162 meteorological stations for the period 1961-2014. A variance decomposition method is applied to identify the sources of predictability, as well as the sources of prediction uncertainty, for January-February-March (JFM), April-May-June (AMJ), July-August-September (JAS) and October-November-December (OND). The averaged potential predictability (ratio of slow to total variance) of NE China precipitation has the highest value of 0.32 during JAS and lowest value of 0.1 in AMJ. Possible sources of seasonal prediction for the leading predictable precipitation EOF modes come from the SST anomalies in the Japan Sea, as well as the North Atlantic during JFM, the Indian Ocean SST in AMJ, and the eastern tropical Pacific SST in JAS and OND. The prolonged linear trend, which is seen in the principal component time series of the leading predictable mode in JFM and OND, may also serve as a source of predictability. The Polar-Eurasia and Northern Annular Mode atmospheric teleconnection patterns are closely connected with the leading and the second predictable mode of JAS, respectively. The Hadley cell circulation is closely related to the leading predictable mode of OND. The leading/second unpredictable precipitation modes for all these four seasons show a similar monopole/dipole structure, and can be largely attributed to the intraseasonal variabilities of the atmosphere.

Twelve year interannual and seasonal variability of stream carbon export from a boreal peatland catchment

NASA Astrophysics Data System (ADS)

Leach, J. A.; Larsson, A.; Wallin, M. B.; Nilsson, M. B.; Laudon, H.

2016-07-01

Understanding stream carbon export dynamics is needed to accurately predict how the carbon balance of peatland catchments will respond to climatic and environmental change. We used a 12 year record (2003-2014) of continuous streamflow and manual spot measurements of total organic carbon (TOC), dissolved inorganic carbon (DIC), methane (CH4), and organic carbon quality (carbon-specific ultraviolet absorbance at 254 nm per dissolved organic carbon) to assess interannual and seasonal variability in stream carbon export for a peatland catchment (70% mire and 30% forest cover) in northern Sweden. Mean annual total carbon export for the 12 year period was 12.2 gCm-2 yr-1, but individual years ranged between 6 and 18 gCm-2 yr-1. TOC, which was primarily composed of dissolved organic carbon (>99%), was the dominant form of carbon being exported, comprising 63% to 79% of total annual exports, and DIC contributed between 19% and 33%. CH4 made up less than 5% of total export. When compared to previously published annual net ecosystem exchange (NEE) for the studied peatland system, stream carbon export typically accounted for 12 to 50% of NEE for most years. However, in 2006 stream carbon export accounted for 63 to 90% (estimated uncertainty range) of NEE due to a dry summer which suppressed NEE, followed by a wet autumn that resulted in considerable stream export. Runoff exerted a primary control on stream carbon export from this catchment; however, our findings suggest that seasonal variations in biologic and hydrologic processes responsible for production and transport of carbon within the peatland were secondary influences on stream carbon export. Consideration of these seasonal dynamics is needed when predicting stream carbon export response to environmental change.

Application of a GCM Ensemble Seasonal Climate Forecasts to Crop Yield Prediction in East Africa

NASA Astrophysics Data System (ADS)

Ogutu, G.; Franssen, W.; Supit, I.; Hutjes, R. W. A.

2016-12-01

We evaluated the potential use of ECMWF System-4 seasonal climate forecasts (S4) for impacts analysis over East Africa. Using the 15 member, 7 months ensemble forecasts initiated every month for 1981-2010, we tested precipitation (tp), air temperature (tas) and surface shortwave radiation (rsds) forecast skill against the WATCH forcing Data ERA-Interim (WFDEI) re-analysis and other data. We used these forecasts as input in the WOFOST crop model to predict maize yields. Forecast skill is assessed using anomaly correlation (ACC), Ranked Probability Skill Score (RPSS) and the Relative Operating Curve Skill Score (ROCSS) for MAM, JJA and OND growing seasons. Predicted maize yields (S4-yields) are verified against historical observed FAO and nationally reported (NAT) yield statistics, and yields from the same crop model forced by WFDEI (WFDEI-yields). Predictability of the climate forecasts vary with season, location and lead-time. The OND tp forecasts show skill over a larger area up to three months lead-time compared to MAM and JJA. Upper- and lower-tercile tp forecasts are 20-80% better than climatology. Good tas forecast skill is apparent with three months lead-time. The rsds is less skillful than tp and tas in all seasons when verified against WFDEI but higher against others. S4-forecasts captures ENSO related anomalous years with region dependent skill. Anomalous ENSO influence is also seen in simulated yields. Focussing on the main sowing dates in the northern (July), equatorial (March-April) and southern (December) regions, WFDEI-yields are lower than FAO and NAT but anomalies are comparable. Yield anomalies are predictable 3-months before sowing in most of the regions. Differences in interannual variability in the range of ±40% may be related to sensitivity of WOFOST to drought stress while the ACCs are largely positive ranging from 0.3 to 0.6. Above and below-normal yields are predictable with 2-months lead time. We evidenced a potential use of seasonal

Evaluating the applicability of using daily forecasts from seasonal prediction systems (SPSs) for agriculture: a case study of Nepal's Terai with the NCEP CFSv2

NASA Astrophysics Data System (ADS)

Jha, Prakash K.; Athanasiadis, Panos; Gualdi, Silvio; Trabucco, Antonio; Mereu, Valentina; Shelia, Vakhtang; Hoogenboom, Gerrit

2018-03-01

Ensemble forecasts from dynamic seasonal prediction systems (SPSs) have the potential to improve decision-making for crop management to help cope with interannual weather variability. Because the reliability of crop yield predictions based on seasonal weather forecasts depends on the quality of the forecasts, it is essential to evaluate forecasts prior to agricultural applications. This study analyses the potential of Climate Forecast System version 2 (CFSv2) in predicting the Indian summer monsoon (ISM) for producing meteorological variables relevant to crop modeling. The focus area was Nepal's Terai region, and the local hindcasts were compared with weather station and reanalysis data. The results showed that the CFSv2 model accurately predicts monthly anomalies of daily maximum and minimum air temperature (Tmax and Tmin) as well as incoming total surface solar radiation (Srad). However, the daily climatologies of the respective CFSv2 hindcasts exhibit significant systematic biases compared to weather station data. The CFSv2 is less capable of predicting monthly precipitation anomalies and simulating the respective intra-seasonal variability over the growing season. Nevertheless, the observed daily climatologies of precipitation fall within the ensemble spread of the respective daily climatologies of CFSv2 hindcasts. These limitations in the CFSv2 seasonal forecasts, primarily in precipitation, restrict the potential application for predicting the interannual variability of crop yield associated with weather variability. Despite these limitations, ensemble averaging of the simulated yield using all CFSv2 members after applying bias correction may lead to satisfactory yield predictions.

Ecophysiological and phenological strategies in seasonally-dry ecosystems: an ecohydrological approach

NASA Astrophysics Data System (ADS)

Vico, Giulia; Manzoni, Stefano; Thompson, Sally; Molini, Annalisa; Porporato, Amilcare

2015-04-01

Seasonally-dry climates are particularly challenging for vegetation, as they are characterized by prolonged dry periods and often marked inter-annual variability. During the dry season plants face predictable physiological stress due to lack of water, whereas the inter-annual variability in rainfall timing and amounts requires plants to develop flexible adaptation strategies. The variety of strategies observed across seasonally-dry (Mediterranean and tropical) ecosystems is indeed wide - ranging from near-isohydric species that adjust stomatal conductance to avoid drought, to anisohydric species that maintain gas exchange during the dry season. A suite of phenological strategies are hypothesized to be associated to ecophysiological strategies. Here we synthetize current knowledge on ecophysiological and phenological adaptations through a comprehensive ecohydrological model linking a soil water balance to a vegetation carbon balance. Climatic regimes are found to select for different phenological strategies that maximize the long-term plant carbon uptake. Inter-annual variability of the duration of the wet season allows coexistence of different drought-deciduous strategies. In contrast, short dry seasons or access to groundwater favour evergreen species. Climatic changes causing more intermittent rainfall and/or shorter wet seasons are predicted to favour drought-deciduous species with opportunistic water use.

Interannual similarity in the Martian atmosphere during the dust storm season

NASA Astrophysics Data System (ADS)

Kass, D. M.; Kleinböhl, A.; McCleese, D. J.; Schofield, J. T.; Smith, M. D.

2016-06-01

We find that during the dusty season on Mars (southern spring and summer) of years without a global dust storm there are three large regional-scale dust storms. The storms are labeled A, B, and C in seasonal order. This classification is based on examining the zonal mean 50 Pa (˜25 km) daytime temperature retrievals from TES/MGS and MCS/MRO over 6 Mars Years. Regional-scale storms are defined as events where the temperature exceeds 200 K. Examining the MCS dust field at 50 Pa indicates that warming in the Southern Hemisphere is dominated by direct heating, while northern high latitude warming is a dynamical response. A storms are springtime planet encircling Southern Hemisphere events. B storms are southern polar events that begin near perihelion and last through the solstice. C storms are southern summertime events starting well after the end of the B storm. C storms show the most interannual variability.

Interannual Similarity in the Martian Atmosphere During the Dust Storm Season

NASA Technical Reports Server (NTRS)

Kass, D. M.; Kleinboehl, A.; McCleese, D. J.; Schofield, J. T.; Smith, M. D.

2016-01-01

We find that during the dusty season on Mars (southern spring and summer) of years without a global dust storm there are three large regional-scale dust storms. The storms are labeled A, B, and C in seasonal order. This classification is based on examining the zonal mean 50 Pa (approximately 25 km) daytime temperature retrievals from TES/MGS and MCS/MRO over 6 Mars Years. Regional-scale storms are defined as events where the temperature exceeds 200 K. Examining the MCS dust field at 50 Pa indicates that warming in the Southern Hemisphere is dominated by direct heating, while northern high latitude warming is a dynamical response. A storms are springtime planet encircling Southern Hemisphere events. B storms are southern polar events that begin near perihelion and last through the solstice. C storms are southern summertime events starting well after the end of the B storm. C storms show the most interannual variability.

The predicted CLARREO sampling error of the inter-annual SW variability

NASA Astrophysics Data System (ADS)

Doelling, D. R.; Keyes, D. F.; Nguyen, C.; Macdonnell, D.; Young, D. F.

2009-12-01

The NRC Decadal Survey has called for SI traceability of long-term hyper-spectral flux measurements in order to monitor climate variability. This mission is called the Climate Absolute Radiance and Refractivity Observatory (CLARREO) and is currently defining its mission requirements. The requirements are focused on the ability to measure decadal change of key climate variables at very high accuracy. The accuracy goals are set using anticipated climate change magnitudes, but the accuracy achieved for any given climate variable must take into account the temporal and spatial sampling errors based on satellite orbits and calibration accuracy. The time period to detect a significant trend in the CLARREO record depends on the magnitude of the sampling calibration errors relative to the current inter-annual variability. The largest uncertainty in climate feedbacks remains the effect of changing clouds on planetary energy balance. Some regions on earth have strong diurnal cycles, such as maritime stratus and afternoon land convection; other regions have strong seasonal cycles, such as the monsoon. However, when monitoring inter-annual variability these cycles are only important if the strength of these cycles vary on decadal time scales. This study will attempt to determine the best satellite constellations to reduce sampling error and to compare the error with the current inter-annual variability signal to ensure the viability of the mission. The study will incorporate Clouds and the Earth's Radiant Energy System (CERES) (Monthly TOA/Surface Averages) SRBAVG product TOA LW and SW climate quality fluxes. The fluxes are derived by combining Terra (10:30 local equator crossing time) CERES fluxes with 3-hourly 5-geostationary satellite estimated broadband fluxes, which are normalized using the CERES fluxes, to complete the diurnal cycle. These fluxes were saved hourly during processing and considered the truth dataset. 90°, 83° and 74° inclination precessionary orbits as

Modeling the Martian seasonal CO2 cycle. I - Fitting the Viking Lander pressure curves. II - Interannual variability

NASA Technical Reports Server (NTRS)

Wood, Stephen E.; Paige, David A.

1992-01-01

The present diurnal and seasonal thermal model for Mars, in which surface CO2 frost condensation and sublimation are determined by the net effects of radiation, latent heat, and heat conduction in subsurface soil layers, in order to simulate seasonal exchanges of CO2 between the polar caps and atmosphere, successfully reproduces the measured pressured variations at the Viking Lander 1 site. In the second part of this work, the year-to-year differences between measured surface pressures at Viking sites as a function of season are used as upper limits on the potential magnitudes of interannual variations in the Martian atmosphere's mass. Simulations indicate that the dust layers deposited onto the condensing north seasonal polar cap during dust storms can darken seasonal frost deposits upon their springtime uncovering, while having little effect on seasonal pressure variations.

Potential Predictability of the Monsoon Subclimate Systems

NASA Technical Reports Server (NTRS)

Yang, Song; Lau, K.-M.; Chang, Y.; Schubert, S.

1999-01-01

While El Nino/Southern Oscillation (ENSO) phenomenon can be predicted with some success using coupled oceanic-atmospheric models, the skill of predicting the tropical monsoons is low regardless of the methods applied. The low skill of monsoon prediction may be either because the monsoons are not defined appropriately or because they are not influenced significantly by boundary forcing. The latter characterizes the importance of internal dynamics in monsoon variability and leads to many eminent chaotic features of the monsoons. In this study, we analyze results from nine AMIP-type ensemble experiments with the NASA/GEOS-2 general circulation model to assess the potential predictability of the tropical climate system. We will focus on the variability and predictability of tropical monsoon rainfall on seasonal-to-interannual time scales. It is known that the tropical climate is more predictable than its extratropical counterpart. However, predictability is different from one climate subsystem to another within the tropics. It is important to understand the differences among these subsystems in order to increase our skill of seasonal-to-interannual prediction. We assess potential predictability by comparing the magnitude of internal and forced variances as defined by Harzallah and Sadourny (1995). The internal variance measures the spread among the various ensemble members. The forced part of rainfall variance is determined by the magnitude of the ensemble mean rainfall anomaly and by the degree of consistency of the results from the various experiments.

Evaluation and attribution of vegetation contribution to seasonal climate predictability

NASA Astrophysics Data System (ADS)

Catalano, Franco; Alessandri, Andrea; De Felice, Matteo

2015-04-01

The land surface model of EC-Earth has been modified to include dependence of vegetation densities on the Leaf Area Index (LAI), based on the Lambert-Beer formulation. Effective vegetation fractional coverage can now vary at seasonal and interannual time-scales and therefore affect biophysical parameters such as the surface roughness, albedo and soil field capacity. The modified model is used to perform a real predictability seasonal hindcast experiment. LAI is prescribed using a recent observational dataset based on the third generation GIMMS and MODIS satellite data. Hindcast setup is: 7 months forecast length, 2 start dates (1st May and 1st November), 10 members, 28 years (1982-2009). The effect of the realistic LAI prescribed from observation is evaluated with respect to a control experiment where LAI does not vary. Hindcast results demonstrate that a realistic representation of vegetation significantly improves the forecasts of temperature and precipitation. The sensitivity is particularly large for temperature during boreal winter over central North America and Central Asia. This may be attributed in particular to the effect of the high vegetation component on the snow cover. Summer forecasts are improved in particular for precipitation over Europe, Sahel, North America, West Russia and Nordeste. Correlation improvements depends on the links between targets (temperature and precipitation) and drivers (surface heat fluxes, albedo, soil moisture, evapotranspiration, moisture divergence) which varies from region to region.

Toward Better Intraseasonal and Seasonal Prediction: Verification and Evaluation of the NOGAPS Model Forecasts

DTIC Science & Technology

2013-09-30

Circulation (HC) in terms of the meridional streamfunction. The interannual variability of the Atlantic HC in boreal summer was examined using the EOF...large-scale circulations in the NAVGEM model and the source of predictability for the seasonal variation of the Atlantic TCs. We have been working...EOF analysis of Meridional Circulation (JAS). (a) The leading mode (M1); (b) variance explained by the first 10 modes. 9

Atlas of Seasonal Means Simulated by the NSIPP 1 Atmospheric GCM. Volume 17

NASA Technical Reports Server (NTRS)

Suarez, Max J. (Editor); Bacmeister, Julio; Pegion, Philip J.; Schubert, Siegfried D.; Busalacchi, Antonio J. (Technical Monitor)

2000-01-01

This atlas documents the climate characteristics of version 1 of the NASA Seasonal-to-Interannual Prediction Project (NSIPP) Atmospheric General Circulation Model (AGCM). The AGCM includes an interactive land model (the Mosaic scheme), and is part of the NSIPP coupled atmosphere-land-ocean model. The results presented here are based on a 20-year (December 1979-November 1999) "ANIIP-style" integration of the AGCM in which the monthly-mean sea-surface temperature and sea ice are specified from observations. The climate characteristics of the AGCM are compared with the National Centers for Environmental Prediction (NCEP) and the European Center for Medium-Range Weather Forecasting (ECMWF) reanalyses. Other verification data include Special Sensor Microwave/Imager (SSNM) total precipitable water, the Xie-Arkin estimates of precipitation, and Earth Radiation Budget Experiment (ERBE) measurements of short and long wave radiation. The atlas is organized by season. The basic quantities include seasonal mean global maps and zonal and vertical averages of circulation, variance/covariance statistics, and selected physics quantities.

Role of monsoon intraseasonal oscillation and its interannual variability in simulation of seasonal mean in CFSv2

NASA Astrophysics Data System (ADS)

Pillai, Prasanth A.; Aher, Vaishali R.

2018-01-01

Intraseasonal oscillation (ISO), which appears as "active" and "break" spells of rainfall, is an important component of Indian summer monsoon (ISM). The present study investigates the potential of new National Centre for Environmental Prediction (NCEP) climate forecast system version 2 (CFSv2) in simulating the ISO with emphasis to its interannual variability (IAV) and its possible role in the seasonal mean rainfall. The present analysis shows that the spatial distribution of CFSv2 rainfall has noticeable differences with observations in both ISO and IAV time scales. Active-break cycle of CFSv2 has similar evolution during both strong and weak years. Regardless of a reasonable El Niño Southern Oscillation (ENSO)-monsoon teleconnection in the model, the overestimated Arabian Sea (AS) sea surface temperature (SST)-convection relationship hinters the large-scale influence of ENSO over the ISM region and adjacent oceans. The ISO scale convections over AS and Bay of Bengal (BoB) have noteworthy contribution to the seasonal mean rainfall, opposing the influence of boundary forcing in these areas. At the same time, overwhelming contribution of ISO component over AS towards the seasonal mean modifies the effect of slow varying boundary forcing to large-scale summer monsoon. The results here underline that, along with the correct simulation of monsoon ISO, its IAV and relationship with the boundary forcing also need to be well captured in coupled models for the accurate simulation of seasonal mean anomalies of the monsoon and its teleconnections.

Seasonal, inter-annual and decadal drivers of tree and grass productivity in an Australian tropical savanna.

NASA Astrophysics Data System (ADS)

Moore, C.; Beringer, J.; Hutley, L. B.; Evans, B. J.; Tapper, N. J.; Donohue, R. J.; Exbrayat, J. F.

2016-12-01

Tree-grass savannas are a widespread biome and are highly valued for their ecosystem services. Natural or anthropogenic shifts in the savanna tree-grass ratio have wide-reaching implications for food production, timber harvesting, biodiversity, the water cycle and carbon sequestration. It is important to understand the long-term dynamics and drivers of both tree and grass productivity separately, in order to successfully manage savannas in the future. This study investigates the inter-annual variability (IAV) of tree (overstory) and grass (understory) productivity at the Howard Springs OzFlux/Fluxnet site by combining a long-term (15 year) eddy covariance flux record and DIFFUSE model estimates of tree and grass productivity inferred from satellite remote sensing. On a seasonal basis, the primary drivers of overstory and understory productivity were solar radiation in the wet season and soil moisture in the dry season, with deeper soil layers becoming more important as the dry season progressed. On an inter-annual basis, variability in the amount of annual rainfall and length of the rainy season determined soil water availability, which had a positive effect on overstory productivity and a negative effect on understory productivity. No linear trend in the tree-grass ratio was observed over the 15-year study period, indicating that woody encroachment was not occurring to a significant degree at the study site. However, the tree-grass ratio was well correlated with modes of climate variability, namely the Southern Oscillation Index. This study has provided important insight into the long-term contributions of trees and grasses to savanna productivity, along with the respective drivers of IAV. The results will contribute towards model development and building better links with remote sensing techniques in order to more comprehensively monitor savanna structure and function across space and time.

Chlorophyll Fluorescence Better Captures Seasonal and Interannual Gross Primary Productivity Dynamics Across Dryland Ecosystems of Southwestern North America

NASA Astrophysics Data System (ADS)

Smith, W. K.; Biederman, J. A.; Scott, R. L.; Moore, D. J. P.; He, M.; Kimball, J. S.; Yan, D.; Hudson, A.; Barnes, M. L.; MacBean, N.; Fox, A. M.; Litvak, M. E.

2018-01-01

Satellite remote sensing provides unmatched spatiotemporal information on vegetation gross primary productivity (GPP). Yet understanding of the relationship between GPP and remote sensing observations and how it changes with factors such as scale, biophysical constraint, and vegetation type remains limited. This knowledge gap is especially apparent for dryland ecosystems, which have characteristic high spatiotemporal variability and are under-represented by long-term field measurements. Here we utilize an eddy covariance (EC) data synthesis for southwestern North America in an assessment of how accurately satellite-derived vegetation proxies capture seasonal to interannual GPP dynamics across dryland gradients. We evaluate the enhanced vegetation index, solar-induced fluorescence (SIF), and the photochemical reflectivity index. We find evidence that SIF is more accurately capturing seasonal GPP dynamics particularly for evergreen-dominated EC sites and more accurately estimating the full magnitude of interannual GPP dynamics for all dryland EC sites. These results suggest that incorporation of SIF could significantly improve satellite-based GPP estimates.

An Evaluation of the Predictability of Austral Summer Season Precipitation over South America.

NASA Astrophysics Data System (ADS)

Misra, Vasubandhu

2004-03-01

In this study predictability of austral summer seasonal precipitation over South America is investigated using a 12-yr set of a 3.5-month range (seasonal) and a 17-yr range (continuous multiannual) five-member ensemble integrations of the Center for Ocean Land Atmosphere Studies (COLA) atmospheric general circulation model (AGCM). These integrations were performed with prescribed observed sea surface temperature (SST); therefore, skill attained represents an estimate of the upper bound of the skill achievable by COLA AGCM with predicted SST. The seasonal runs outperform the multiannual model integrations both in deterministic and probabilistic skill. The simulation of the January February March (JFM) seasonal climatology of precipitation is vastly superior in the seasonal runs except over the Nordeste region where the multiannual runs show a marginal improvement. The teleconnection of the ensemble mean JFM precipitation over tropical South America with global contemporaneous observed sea surface temperature in the seasonal runs conforms more closely to observations than in the multiannual runs. Both the sets of runs clearly beat persistence in predicting the interannual precipitation anomalies over the Amazon River basin, Nordeste, South Atlantic convergence zone, and subtropical South America. However, both types of runs display poorer simulations over subtropical regions than the tropical areas of South America. The examination of probabilistic skill of precipitation supports the conclusions from deterministic skill analysis that the seasonal runs yield superior simulations than the multiannual-type runs.

Comparison of the seasonal and interannual variability of phytoplankton pigment concentrations in the Peru and California Current systems

NASA Technical Reports Server (NTRS)

Thomas, A. C.; Huang, F.; Strub, P. T.; James, C.

1994-01-01

Monthly composite images from the global coastal zone color scanner (CZCS) data set are used to provide an initial illustration and comparison of seasonal and interannual variability of phytoplankton pigment concentration along the western coasts of South and North America in the Peru Current system (PCS) and California Current system (CCS). The analysis utilizes the entire time series of available data (November 1978 to June 1986) to form a mean annual cycle and an index of interannual variability for a series of both latitudinal and cross-shelf regions within each current system. Within 100 km of the coast, the strongest seasonal cycles in the CCS are in two regions, one between 34 deg and 45 deg N and the second between 24 deg and 29 deg N, each with maximum concentrations (greater than 3.0 mg m(exp-3)) in May-June. Weaker seasonal variability is present north of 45 deg N and in the Southern California Bight region (32 deg N). Within the PCS, in the same 100-km-wide coastal region, highest (greater than 45 deg S) and lowest (less than 20 deg S) latitude regions have a similar seasonal cycle with maximum concentrations (greater than 1.5 mg m(exp -3)) during the austral spring, summer, and fall, matching that evident throughout the CCS. Between these regions, off northern and central Chile, the seasonal maximum occurs during July-August (austral winter), contrary to the influence of upwelling favorable winds. Within the CCS, the dominant feature of interannual variability in the 8-year time series is a strong negative concentration anomaly in 1983, an El Nino year. The relative value of this negative anomaly is strongest off central California and is followed by an even stronger negative anomaly is strongest off central California and is followed by an even stronger negative anomaly in 1984 off Baja, California. In the PCS, strong negative anomalies during the 1982-1983 El Nino period are evident only off the Peruvian coast and are evident there only in the

Interannual Variation of Surface Circulation in the Japan/East Sea due to External Forcings and Intrinsic Variability

NASA Astrophysics Data System (ADS)

Choi, Byoung-Ju; Cho, Seong Hun; Jung, Hee Seok; Lee, Sang-Ho; Byun, Do-Seong; Kwon, Kyungman

2018-03-01

The interannual variation of surface ocean currents can be as large as seasonal variation in the Japan/East Sea (JES). To identify the major factors that cause such interannual variability of surface ocean circulation in the JES, surface circulation was simulated from 1998 to 2009 using a three-dimensional model. Contributions of atmospheric forcing (ATM), open boundary data (OBC), and intrinsic variability (ITV) of the surface flow in the JES on the interannual variability of surface ocean circulation were separately examined using numerical simulations. Variability in surface circulation was quantified in terms of variance in sea surface height, 100-m depth water temperature, and surface currents. ITV was found to be the dominant factor that induced interannual variabilities of surface circulation, the main path of the East Korea Warm Current (EKWC), and surface kinetic energy on a time scale of 2-4 years. OBC and ATM were secondary factors contributing to the interannual variation of surface circulation. Interannual variation of ATM changed the separation latitude of EKWC and increased the variability of surface circulation in the Ulleung Basin. Interannual variation of OBC enhanced low-frequency changes in surface circulation and eddies in the Yamato Basin. It also modulated basin-wide uniform oscillations of sea level. This study suggests that precise estimation of initial conditions using data assimilation is essential for long-term prediction of surface circulation in the JES.

Seasonal and inter-annual dynamics of growth, non-structural carbohydrates and C stable isotopes in a Mediterranean beech forest.

PubMed

Scartazza, Andrea; Moscatello, Stefano; Matteucci, Giorgio; Battistelli, Alberto; Brugnoli, Enrico

2013-07-01

Seasonal and inter-annual dynamics of growth, non-structural carbohydrates (NSC) and carbon isotope composition (δ(13)C) of NSC were studied in a beech forest of Central Italy over a 2-year period characterized by different environmental conditions. The net C assimilated by forest trees was mainly used to sustain growth early in the season and to accumulate storage carbohydrates in trunk and root wood in the later part of the season, before leaf shedding. Growth and NSC concentration dynamics were only slightly affected by the reduced soil water content (SWC) during the drier year. Conversely, the carbon isotope analysis on NSC revealed seasonal and inter-annual variations of photosynthetic and post-carboxylation fractionation processes, with a significant increase in δ(13)C of wood and leaf soluble sugars in the drier summer year than in the wetter one. The highly significant correlation between δ(13)C of leaf soluble sugars and SWC suggests a decrease of the canopy C isotope discrimination and, hence, an increased water-use efficiency with decreasing soil water availability. This may be a relevant trait for maintaining an acceptable plant water status and a relatively high C sink capacity during dry seasonal periods. Our results suggest a short- to medium-term homeostatic response of the Collelongo beech stand to variations in water availability and solar radiation, indicating that this Mediterranean forest was able to adjust carbon-water balance in order to prevent C depletion and to sustain plant growth and reserve accumulation during relatively dry seasons.

Optimal selection of MULTI-model downscaled ensembles for interannual and seasonal climate prediction in the eastern seaboard of Thailand

NASA Astrophysics Data System (ADS)

Bejranonda, W.; Koch, M.

2010-12-01

Because of the imminent threat of the water resources of the eastern seaboard of Thailand, a climate impact study has been carried out there. To that avail, a hydrological watershed model is being used to simulate the future water availability in the wake of possible climate change in the region. The hydrological model is forced by predictions from global climate models (GCMs) that are to be downscaled in an appropriate manner. The challenge at that stage of the climate impact analysis lies then the in the choice of the best GCM and the (statistical) downscaling method. In this study the selection of coarse grid resolution output of the GCMs, transferring information to the fine grid of local climate-hydrology is achieved by cross-correlation and multiple linear regression using meteorological data in the eastern seaboard of Thailand observed between 1970-1999. The grids of 20 atmosphere/ocean global climate models (AOGCM), covering latitude 12.5-15.0 N and longitude 100.0-102.5 E were examined using the Climate-Change Scenario Generator (SCENGEN). With that tool the model efficiency of the prediction of daily precipitation and mean temperature was calculated by comparing the 1980-1999 ECMWF reanalysis predictions with the observed data during that time period. The root means square errors of the predictions were considered and ranked to select the top 5 models, namely, BCCR-BCM2.0, GISS-ER, ECHO-G, ECHAM5/MPI-OM and PCM. The daily time-series of 338 predictors in 9 runs of the 5 selected models were gathered from the CMIP3 multi-model database. Monthly time-serial cross-correlations between the climate predictors and the meteorological measurements from 25 rainfall, 4 minimum and maximum temperature, 4 humidity and 2 solar radiation stations in the study area were then computed and ranked. Using the ranked predictors, a multiple-linear regression model (downscaling transfer model) to forecast the local climate was set up. To improve the prediction power of this

Predictability of Zonal Means During Boreal Summer

NASA Technical Reports Server (NTRS)

Schubert, Siegfried; Suarez, Max J.; Pegion, Philip J.; Kistler, Michael A.; Kumar, Arun; Einaudi, Franco (Technical Monitor)

2001-01-01

This study examines the predictability of seasonal means during boreal summer. The results are based on ensembles of June-July-August (JJA) simulations (started in mid May) carried out with the NASA Seasonal-to-Interannual Prediction Project (NSIPP-1) atmospheric general circulation model (AGCM) forced with observed sea surface temperatures (SSTS) and sea ice for the years 1980-1999. We find that the predictability of the JJA extra-tropical height field is primarily in the zonal mean component of the response to the SST anomalies. This contrasts with the cold season (January-February-March) when the predictability of seasonal means in the boreal extratropics is primarily in the wave component of the El Nino/Southern Oscillation (ENSO) response. Two patterns dominate the interannual variability of the ensemble mean JJA zonal mean height field. One has maximum variance in the tropical/subtropical upper troposphere, while the other has substantial variance in middle latitudes of both hemispheres. Both are symmetric with respect to the equator. A regression analysis suggests that the tropical/subtropical pattern is associated with SST anomalies in the far eastern tropical Pacific and the Indian Ocean, while the middle latitude pattern is forced by SST anomalies in the tropical Pacific just east of the dateline. The two leading zonal height patterns are reproduced in model runs forced with the two leading JJA SST patterns of variability. A comparison with observations shows a signature of the middle latitude pattern that is consistent with the occurrence of dry and wet summers over the United States. We hypothesize that both patterns, while imposing only weak constraints on extratropical warm season continental-scale climates, may play a role in the predilection for drought or pluvial conditions.

Seasonal and Interannual Variations of Ice Sheet Surface Elevation at the Summit of Greenland: Observed and Modeled

NASA Technical Reports Server (NTRS)

Zwally, H. Jay; Jun, Li; Koblinsky, Chester J. (Technical Monitor)

2001-01-01

Observed seasonal and interannual variations in the surface elevation over the summit of the Greenland ice sheet are modeled using a new temperature-dependent formulation of firn-densification and observed accumulation variations. The observed elevation variations are derived from ERS (European Remote Sensing)-1 and ERS-2 radar altimeter data for the period between April 1992 and April 1999. A multivariate linear/sine function is fitted to an elevation time series constructed from elevation differences measured by radar altimetry at orbital crossovers. The amplitude of the seasonal elevation cycle is 0.25 m peak-to-peak, with a maximum in winter and a minimum in summer. Inter-annually, the elevation decreases to a minimum in 1995, followed by an increase to 1999, with an overall average increase of 4.2 cm a(exp -1) for 1992 to 1999. Our densification formulation uses an initial field-density profile, the AWS (automatic weather station) surface temperature record, and a temperature-dependent constitutive relation for the densification that is based on laboratory measurements of crystal growth rates. The rate constant and the activation energy commonly used in the Arrhenius-type constitutive relation for firn densification are also temperature dependent, giving a stronger temperature and seasonal amplitudes about 10 times greater than previous densification formulations. Summer temperatures are most important, because of the strong non-linear dependence on temperature. Much of firn densification and consequent surface lowering occurs within about three months of the summer season, followed by a surface build-up from snow accumulation until spring. Modeled interannual changes of the surface elevation, using the AWS measurements of surface temperature and accumulation and results of atmospheric modeling of precipitation variations, are in good agreement with the altimeter observations. In the model, the surface elevation decreases about 20 cm over the seven years due

Measuring the potential utility of seasonal climate predictions

NASA Astrophysics Data System (ADS)

Tippett, Michael K.; Kleeman, Richard; Tang, Youmin

2004-11-01

Variation of sea surface temperature (SST) on seasonal-to-interannual time-scales leads to changes in seasonal weather statistics and seasonal climate anomalies. Relative entropy, an information theory measure of utility, is used to quantify the impact of SST variations on seasonal precipitation compared to natural variability. An ensemble of general circulation model (GCM) simulations is used to estimate this quantity in three regions where tropical SST has a large impact on precipitation: South Florida, the Nordeste of Brazil and Kenya. We find the yearly variation of relative entropy is strongly correlated with shifts in ensemble mean precipitation and weakly correlated with ensemble variance. Relative entropy is also found to be related to measures of the ability of the GCM to reproduce observations.

Seasonal Evolution and Interannual Variability of the Local Solar Energy Absorbed by the Arctic Sea Ice-Ocean System

NASA Technical Reports Server (NTRS)

Perovich, Donald K.; Nghiem, Son V.; Markus, Thorsten; Schwieger, Axel

2007-01-01

The melt season of the Arctic sea ice cover is greatly affected by the partitioning of the incident solar radiation between reflection to the atmosphere and absorption in the ice and ocean. This partitioning exhibits a strong seasonal cycle and significant interannual variability. Data in the period 1998, 2000-2004 were analyzed in this study. Observations made during the 1997-1998 SHEBA (Surface HEat Budget of the Arctic Ocean) field experiment showed a strong seasonal dependence of the partitioning, dominated by a five-phase albedo evolution. QuikSCAT scatterometer data from the SHEBA region in 1999-2004 were used to further investigate solar partitioning in summer. The time series of scatterometer data were used to determine the onset of melt and the beginning of freezeup. This information was combined with SSM/I-derived ice concentration, TOVS-based estimates of incident solar irradiance, and SHEBA results to estimate the amount of solar energy absorbed in the ice-ocean system for these years. The average total solar energy absorbed in the ice-ocean system from April through September was 900 MJ m(sup -2). There was considerable interannual variability, with a range of 826 to 1044 MJ m(sup -2). The total amount of solar energy absorbed by the ice and ocean was strongly related to the date of melt onset, but only weakly related to the total duration of the melt season or the onset of freezeup. The timing of melt onset is significant because the incident solar energy is large and a change at this time propagates through the entire melt season, affecting the albedo every day throughout melt and freezeup.

Intra-seasonal and Inter-annual variability of Bowen Ratio over rain-shadow region of North peninsular India

NASA Astrophysics Data System (ADS)

Morwal, S. B.; Narkhedkar, S. G.; Padmakumari, B.; Maheskumar, R. S.; Deshpande, C. G.; Kulkarni, J. R.

2017-05-01

Intra-seasonal and inter-annual variability of Bowen Ratio (BR) have been studied over the rain-shadow region of north peninsular India during summer monsoon season. Daily grid point data of latent heat flux (LHF), sensible heat flux (SHF) from NCEP/NCAR Reanalysis for the period 1970-2014 have been used to compute daily area-mean BR. Daily grid point rainfall data at a resolution of 0.25° × 0.25° from APHRODITE's Water Resources for the available period 1970-2007 have been used to study the association between rainfall and BR. The study revealed that BR rapidly decreases from 4.1 to 0.29 in the month of June and then remains nearly constant at the same value (≤0.1) in the rest of the season. High values of BR in the first half of June are indicative of intense thermals and convective clouds with higher bases. Low values of BR from July to September period are indicative of weak thermals and convective clouds with lower bases. Intra-seasonal and inter-annual variability of BR is found to be inversely related to precipitation over the region. BR analysis indicates that the land surface characteristics of the study region during July-September are similar to that over oceanic regions as far as intensity of thermals and associated cloud microphysical properties are concerned. Similar variation of BR is found in El Nino and La Nina years. During June, an increasing trend is observed in SHF and BR and decreasing trend in LHF from 1976 to 2014. Increasing trend in the SHF is statistically significant.

Detecting a Terrestrial Biosphere Sink for Carbon Dioxide: Interannual Ecosystem Modeling for the Mid-1980s

NASA Technical Reports Server (NTRS)

Potter, Christopher S.; Klooster, Steven A.; Brooks, Vanessa; Gore, Warren J. (Technical Monitor)

1998-01-01

There is considerable uncertainty as to whether interannual variability in climate and terrestrial ecosystem production is sufficient to explain observed variation in atmospheric carbon content over the past 20-30 years. In this paper, we investigated the response of net CO2 exchange in terrestrial ecosystems to interannual climate variability (1983 to 1988) using global satellite observations as drivers for the NASA-CASA (Carnegie-Ames-Stanford Approach) simulation model. This computer model of net ecosystem production (NEP) is calibrated for interannual simulations driven by monthly satellite vegetation index data (NDVI) from the NOAA Advanced Very High Resolution Radiometer (AVHRR) at 1 degree spatial resolution. Major results from NASA-CASA simulations suggest that from 1985 to 1988, the northern middle-latitude zone (between 30 and 60 degrees N) was the principal region driving progressive annual increases in global net primary production (NPP; i.e., the terrestrial biosphere sink for carbon). The average annual increase in NPP over this predominantly northern forest zone was on the order of +0.4 Pg (10 (exp 15) g) C per year. This increase resulted mainly from notable expansion of the growing season for plant carbon fixation toward the zonal latitude extremes, a pattern uniquely demonstrated in our regional visualization results. A net biosphere source flux of CO2 in 1983-1984, coinciding with an El Nino event, was followed by a major recovery of global NEP in 1985 which lasted through 1987 as a net carbon sink of between 0.4 and 2.6 Avg C per year. Analysis of model controls on NPP and soil heterotrophic CO2 fluxes (Rh) suggests that regional warming in northern forests can enhance ecosystem production significantly. In seasonally dry tropical zones, periodic drought and temperature drying effects may carry over with at least a two-year lag time to adversely impact ecosystem production. These yearly patterns in our model-predicted NEP are consistent in

An assessment of Indian monsoon seasonal forecasts and mechanisms underlying monsoon interannual variability in the Met Office GloSea5-GC2 system

NASA Astrophysics Data System (ADS)

Johnson, Stephanie J.; Turner, Andrew; Woolnough, Steven; Martin, Gill; MacLachlan, Craig

2017-03-01

We assess Indian summer monsoon seasonal forecasts in GloSea5-GC2, the Met Office fully coupled subseasonal to seasonal ensemble forecasting system. Using several metrics, GloSea5-GC2 shows similar skill to other state-of-the-art seasonal forecast systems. The prediction skill of the large-scale South Asian monsoon circulation is higher than that of Indian monsoon rainfall. Using multiple linear regression analysis we evaluate relationships between Indian monsoon rainfall and five possible drivers of monsoon interannual variability. Over the time period studied (1992-2011), the El Niño-Southern Oscillation (ENSO) and the Indian Ocean dipole (IOD) are the most important of these drivers in both observations and GloSea5-GC2. Our analysis indicates that ENSO and its teleconnection with Indian rainfall are well represented in GloSea5-GC2. However, the relationship between the IOD and Indian rainfall anomalies is too weak in GloSea5-GC2, which may be limiting the prediction skill of the local monsoon circulation and Indian rainfall. We show that this weak relationship likely results from a coupled mean state bias that limits the impact of anomalous wind forcing on SST variability, resulting in erroneous IOD SST anomalies. Known difficulties in representing convective precipitation over India may also play a role. Since Indian rainfall responds weakly to the IOD, it responds more consistently to ENSO than in observations. Our assessment identifies specific coupled biases that are likely limiting GloSea5-GC2 Indian summer monsoon seasonal prediction skill, providing targets for model improvement.

Earth System Science at NASA: Teleconnections Between Sea Surface Temperature and Epidemics in Africa

NASA Technical Reports Server (NTRS)

Meeson, Blanche W.

2000-01-01

The research carried out in the Earth Sciences in NASA and at NASA's Goddard Space Flight Center will be the focus of the presentations. In addition, one research project that links sea surface temperature to epidemics in Africa will be highlighted. At GSFC research interests span the full breath of disciplines in Earth Science. Branches and research groups focus on areas as diverse as planetary geomagnetics and atmospheric chemistry. These organizations focus on atmospheric sciences (atmospheric chemistry, climate and radiation, regional processes, atmospheric modeling), hydrological sciences (snow, ice, oceans, and seasonal-to-interannual prediction), terrestrial physics (geology, terrestrial biology, land-atmosphere interactions, geophysics), climate modeling (global warming, greenhouse gases, climate change), on sensor development especially using lidar and microwave technologies, and on information technologies, that enable support of scientific and technical research.

Phytoplankton class-specific primary production in the world's oceans: Seasonal and interannual variability from satellite observations

NASA Astrophysics Data System (ADS)

Uitz, Julia; Claustre, Hervé; Gentili, Bernard; Stramski, Dariusz

2010-09-01

We apply an innovative approach to time series data of surface chlorophyll from satellite observations with SeaWiFS (Sea-viewing Wide Field-of-view Sensor) to estimate the primary production associated with three major phytoplankton classes (micro-, nano-, and picophytoplankton) within the world's oceans. Statistical relationships, determined from an extensive in situ database of phytoplankton pigments, are used to infer class-specific vertical profiles of chlorophyll a concentration from satellite-derived surface chlorophyll a. This information is combined with a primary production model and class-specific photophysiological parameters to compute global seasonal fields of class-specific primary production over a 10-year period from January 1998 through December 2007. Microphytoplankton (mostly diatoms) appear as a major contributor to total primary production in coastal upwelling systems (70%) and temperate and subpolar regions (50%) during the spring-summer season. The contribution of picophytoplankton (e.g., prokaryotes) reaches maximum values (45%) in subtropical oligotrophic gyres. Nanophytoplankton (e.g., prymnesiophytes) provide a ubiquitous, substantial contribution (30-60%). Annual global estimates of class-specific primary production amount to 15 Gt C yr-1 (32% of total), 20 Gt C yr-1 (44%) and 11 Gt C yr-1 (24%) for micro-, nano-, and picophytoplankton, respectively. The analysis of interannual variations revealed large anomalies in class-specific primary production as compared to the 10-year mean cycle in both the productive North Atlantic basin and the more stable equatorial Pacific upwelling. Microphytoplankton show the largest range of variability of the three phytoplankton classes on seasonal and interannual time scales. Our results contribute to an understanding and quantification of carbon cycle in the ocean.

Interannual and seasonal changes in the south seasonal polar cap of Mars: Observations from MY 28-31 using MARCI

NASA Astrophysics Data System (ADS)

Calvin, W. M.; Cantor, B. A.; James, P. B.

2017-08-01

The Mars Color Imager (MARCI) camera on the Mars Reconnaissance Orbiter provides daily synoptic coverage that allows monitoring of seasonal cap retreat and interannual changes that occur between Mars Years (MY) and over the southern summer. We present the first analysis of this data for the southern seasonal cap evolution observed in MY 28, 29, 30 and 31 (2/2007 to 07/2013). Observation over multiple Mars years allows us to compare changes between years as well as longer-term evolution of the high albedo deposits at the poles. Seasonal cap retreat is similar in all years and to retreats observed in other years by both optical and thermal instruments. The cryptic terrain has a fairly consistent boundary in each year, but numerous small-scale variations occur in each MY observed. Additionally, numerous small dark deposits are identified outside the classically identified cyptic region, including Inca City and other locations not previously noted. The large water ice outlier is observed to retain seasonal frost the longest (outside the polar dome) and is also highly variable in each MY. The development of the cryptic/anti-cryptic hemispheres is inferred to occur due to albedo variations that develop after dust venting starts and may be caused by recondensation of CO2 ice on the brightest and coldest regions controlled by topographic winds. Ground ice may play a role in which regions develop cryptic terrain, as there is no elevation control on either cryptic terrain or the late season brightest deposits.

Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos

2016-01-01

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

Evaluating NMME Seasonal Forecast Skill for use in NASA SERVIR Hub Regions

NASA Technical Reports Server (NTRS)

Roberts, J. Brent; Roberts, Franklin R.

2013-01-01

The U.S. National Multi-Model Ensemble seasonal forecasting system is providing hindcast and real-time data streams to be used in assessing and improving seasonal predictive capacity. The coupled forecasts have numerous potential applications, both national and international in scope. The NASA / USAID SERVIR project, which leverages satellite and modeling-based resources for environmental decision making in developing nations, is focusing on the evaluation of NMME forecasts specifically for use in driving applications models in hub regions including East Africa, the Hindu Kush- Himalayan (HKH) region and Mesoamerica. A prerequisite for seasonal forecast use in application modeling (e.g. hydrology, agriculture) is bias correction and skill assessment. Efforts to address systematic biases and multi-model combination in support of NASA SERVIR impact modeling requirements will be highlighted. Specifically, quantilequantile mapping for bias correction has been implemented for all archived NMME hindcasts. Both deterministic and probabilistic skill estimates for raw, bias-corrected, and multi-model ensemble forecasts as a function of forecast lead will be presented for temperature and precipitation. Complementing this statistical assessment will be case studies of significant events, for example, the ability of the NMME forecasts suite to anticipate the 2010/2011 drought in the Horn of Africa and its relationship to evolving SST patterns.

Seasonal and interannual variations of carbon exchange over a rice-wheat rotation system on the North China Plain

NASA Astrophysics Data System (ADS)

Chen, Chen; Li, Dan; Gao, Zhiqiu; Tang, Jianwu; Guo, Xiaofeng; Wang, Linlin; Wan, Bingcheng

2015-10-01

Rice-wheat (R-W) rotation systems are ubiquitous in South and East Asia, and play an important role in modulating the carbon cycle and climate. Long-term, continuous flux measurements help in better understanding the seasonal and interannual variation of the carbon budget over R-W rotation systems. In this study, measurements of CO2 fluxes and meteorological variables over an R-W rotation system on the North China Plain from 2007 to 2010 were analyzed. To analyze the abiotic factors regulating Net Ecosystem Exchange (NEE), NEE was partitioned into gross primary production (GPP) and ecosystem respiration. Nighttime NEE or ecosystem respiration was controlled primarily by soil temperature, while daytime NEE was mainly determined by photosythetically active radiation (PAR). The responses of nighttime NEE to soil temperature and daytime NEE to light were closely associated with crop development and photosynthetic activity, respectively. Moreover, the interannual variation in GPP and NEE mainly depended on precipitation and PAR. Overall, NEE was negative on the annual scale and the rotation system behaved as a carbon sink of 982 g C m-2 per year over the three years. The winter wheat field took up more CO2 than the rice paddy during the longer growing season, while the daily NEE for wheat and rice were -2.35 and -3.96 g C m-2, respectively. After the grain harvest was subtracted from the NEE, the winter wheat field became a moderately strong carbon sink of 251-334 g C m-2 per season, whereas the rice paddy switched to a weak carbon sink of 107-132 per season.

Seasonal and interannual variability of the Mid-Holocene East Asian monsoon in coral δ18O records from the South China Sea

NASA Astrophysics Data System (ADS)

Sun, Donghuai; Gagan, Michael K.; Cheng, Hai; Scott-Gagan, Heather; Dykoski, Carolyn A.; Edwards, R. Lawrence; Su, Ruixia

2005-08-01

Understanding the full range of past monsoon variability, with reference to specific monsoon seasons, is essential to test coupled climate models and improve their predictive capabilities. We present a 54-year long, high-resolution skeletal oxygen isotope (δ18O) record extracted from a well-preserved, massive Porites sp. coral at Hainan Island, South China Sea, to investigate East Asian monsoon variability during summer and winter ∼4400 calendar yr ago. Analysis of modern coral δ18O confirms that Porites from Hainan Island are well positioned to record winter monsoon forcing of sea surface temperature (SST), as well as the influence of summer monsoon rainfall on sea surface salinity (SSS). The coral record for ∼4400 yr ago shows ∼9% amplification of the annual cycle of δ18O, in good agreement with coupled ocean-atmosphere models showing higher summer rainfall (lower coral δ18O) and cooler winter SSTs (higher coral δ18O) in response to greater Northern Hemisphere insolation seasonality during the Middle Holocene. Mean SSTs in the South China Sea during the Mid-Holocene were within 0.5 °C of modern values, yet the mean δ18O for the fossil coral is ∼0.6‰ higher than that for the modern coral, suggesting that the δ18O of surface seawater was higher by at least ∼0.5‰, relative to modern values. The 18O-enrichment is likely to be driven by greater advection of moisture towards the Asian landmass, enhanced monsoon wind-induced evaporation and vertical mixing, and/or invigorated advection of saltier 18O-enriched Pacific water into the relatively fresh South China Sea. The 18O-enrichment of the northern South China Sea ∼4400 yr ago contributes to mounting evidence for recent freshening of the tropical Western Pacific. Today, winter SST and summer SSS variability in the South China Sea reflect the interannual influence of ENSO and the biennial variability inherent to monsoon precipitation. Spectral analysis of winter SSTs ∼4400 yr ago reveals a

The Seasonal and Interannual Variability of the Budgets of N2O and CCl3F

NASA Technical Reports Server (NTRS)

Wong, Sun; Prather, Michael J.; Rind, David H.

1999-01-01

The 6-year wind archives from the Goddard Institute for Space Studies/Global Climate-Middle Atmosphere Model (GISS/GCMAM) were in- put to the GISS/Harvard/Irvine Chemical Transport Model (G/H/I CTM) to study the seasonal and interannual variability of the budgets and distributions of nitrous oxide (N2O) and trichlorofluoromethane (CCl3F), with the corresponding chemical loss frequencies recycled and boundary conditions kept unchanged from year to year. The effects of ozone feedback and quasi-biennial oscillation (QBO) were not included. However, the role of circulation variation in driving the lifetime variability is investigated. It was found that the global loss rates of these tracers are related to the extratropical planetary wave activity, which drives the tropical upward mass flux. For N2O, a semiannual signal in the loss rate variation is associated with the interhemispheric asymmetry in the upper stratospheric wave activity. For CCl3F, the semiannual signal is weaker, associated with the comparatively uniform wave episodes in the lower stratosphere. The loss rates lag behind the wave activity by about 1-2 months. The interannual variation of the GCM generated winds drives the interannual variation of the annually averaged lifetime. The year-to-year variations of the annually averaged lifetimes can be about 3% for N2O and 4% for CCl3F.

Seasonal and Inter-annual Variability in Modeled Larval Dispersal and Population Connectivity of Blue Crabs (Callinectes sapidus) in the Northern Gulf of Mexico

NASA Astrophysics Data System (ADS)

Gyory, J.; Jones, B.; Ko, D. S.; Taylor, C.

2016-02-01

Larval dispersal trajectories and their resulting population connectivity patterns are known to be key drivers of population dynamics for many marine organisms. However, few studies to date have examined the temporal variability in population connectivity. Here, we model the larval dispersal and population connectivity of blue crabs in the northern Gulf of Mexico from 2003-2012 and use network analyses to understand how they vary over seasonal and inter-annual scales. We found that in all years, the Mississippi River Delta is a barrier to dispersal. Few larvae cross it and settle successfully. In some years (2004, 2007, 2008, and 2009), 1-2 locations (Adams Bay and Chandeleur Sound) had high (> 0.3) betweenness centrality. These locations are likely to be important for maintaining population connectivity in the region, since more than 30% of larval pathways are predicted to pass through them. Connectivity matrices suggest that some estuaries have consistently high larval retention rates. These include West Cote Blanche Bay, Chandeleur Sound, and, in some years, Pensacola Bay and Atchafalaya Bay. Within the spawning season, we observe a decline in average vertex degree and average source strength in every year. This suggests that seasonal declines in the strength of along-shore currents produce consistent reductions in population connectivity through the spawning season.

Seasonal prediction and predictability of Eurasian spring snow water equivalent in NCEP Climate Forecast System version 2 reforecasts

NASA Astrophysics Data System (ADS)

He, Qiong; Zuo, Zhiyan; Zhang, Renhe; Zhang, Ruonan

2018-01-01

The spring snow water equivalent (SWE) over Eurasia plays an important role in East Asian and Indian monsoon rainfall. This study evaluates the seasonal prediction capability of NCEP Climate Forecast System version 2 (CFSv2) retrospective forecasts (1983-2010) for the Eurasian spring SWE. The results demonstrate that CFSv2 is able to represent the climatological distribution of the observed Eurasian spring SWE with a lead time of 1-3 months, with the maximum SWE occurring over western Siberia and Northeastern Europe. For a longer lead time, the SWE is exaggerated in CFSv2 because the start of snow ablation in CFSv2 lags behind that of the observation, and the simulated snowmelt rate is less than that in the observation. Generally, CFSv2 can simulate the interannual variations of the Eurasian spring SWE 1-5 months ahead of time but with an exaggerated magnitude. Additionally, the downtrend in CFSv2 is also overestimated. Because the initial conditions (ICs) are related to the corresponding simulation results significantly, the robust interannual variability and the downtrend in the ICs are most likely the causes for these biases. Moreover, CFSv2 exhibits a high potential predictability for the Eurasian spring SWE, especially the spring SWE over Siberia, with a lead time of 1-5 months. For forecasts with lead times longer than 5 months, the model predictability gradually decreases mainly due to the rapid decrease in the model signal.

Seasonal and interannual variability of chlorophyll-a and associated physical synchronous variability in the western tropical Pacific

NASA Astrophysics Data System (ADS)

Hou, Xueyan; Dong, Qing; Xue, Cunjin; Wu, Shuchao

2016-06-01

Based on long-term satellite-derived ocean data sets and methods of empirical orthogonal function and singular value decomposition, we investigated the spatiotemporal variability of the chlorophyll-a concentration (CHL) on seasonal and interannual timescales in the western tropical Pacific associated with physical ocean variables of sea surface temperature (SST), sea level anomaly (SLA) and sea surface wind (SSW), and the El Niño Southern Oscillation (ENSO) index. The bio-physical synchronous variation on interannual timescale was also confirmed in terms of the scales of variability and oscillation periods in the time-frequency space using the methods of Fourier transform, Morlet wavelet transform, and wavelet coherence analysis. On a seasonal timescale, the first two modes of the monthly mean CHL fields described the consecutive spatiotemporal variation in CHL in the western tropical Pacific. CHL reached the maximum during late winter-early spring and minimum during summer-early autumn with the exception of the northeast of Papua New Guinea and the Solomon Islands. The CHL bloom in boreal winter-spring was closely associated with cold SST, high sea level along the North Equatorial Countercurrent meanders, and strong wind. On an interannual timescale, the variability of CHL exhibited a close correlation with SST, SLA, SSW, and ENSO. During El Niño, CHL increased in the oligotrophic western basin of the warm pool associated with cold SST, low SLA, and strong westerly winds but decreased in the mesotrophic eastern basin of the warm pool in association with warm SST, high SLA, and weak easterly trade winds. There may exist time-lag for the bio-physical covariation, i.e., CHL and SST varied simultaneously within 1 month, and CHL variations led SLA by approximately 0-3 months but lagged wind speed by about 1 month. In the time-frequency domain, the interannual variability in CHL and physical ocean variables had high common power, indicating that the variability scales

Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 2 (JMA/MRI-CPS2): atmosphere-land-ocean-sea ice coupled prediction system for operational seasonal forecasting

NASA Astrophysics Data System (ADS)

Takaya, Yuhei; Hirahara, Shoji; Yasuda, Tamaki; Matsueda, Satoko; Toyoda, Takahiro; Fujii, Yosuke; Sugimoto, Hiroyuki; Matsukawa, Chihiro; Ishikawa, Ichiro; Mori, Hirotoshi; Nagasawa, Ryoji; Kubo, Yutaro; Adachi, Noriyuki; Yamanaka, Goro; Kuragano, Tsurane; Shimpo, Akihiko; Maeda, Shuhei; Ose, Tomoaki

2018-02-01

This paper describes the Japan Meteorological Agency/Meteorological Research Institute-Coupled Prediction System version 2 (JMA/MRI-CPS2), which was put into operation in June 2015 for the purpose of performing seasonal predictions. JMA/MRI-CPS2 has various upgrades from its predecessor, JMA/MRI-CPS1, including improved resolution and physics in its atmospheric and oceanic components, introduction of an interactive sea-ice model and realistic initialization of its land component. Verification of extensive re-forecasts covering a 30-year period (1981-2010) demonstrates that JMA/MRI-CPS2 possesses improved seasonal predictive skills for both atmospheric and oceanic interannual variability as well as key coupled variability such as the El Niño-Southern Oscillation (ENSO). For ENSO prediction, the new system better represents the forecast uncertainty and transition/duration of ENSO phases. Our analysis suggests that the enhanced predictive skills are attributable to incremental improvements resulting from all of the changes, as is apparent in the beneficial effects of sea-ice coupling and land initialization on 2-m temperature predictions. JMA/MRI-CPS2 is capable of reasonably representing the seasonal cycle and secular trends of sea ice. The sea-ice coupling remarkably enhances the predictive capability for the Arctic 2-m temperature, indicating the importance of this factor, particularly for seasonal predictions in the Arctic region.

Time series pCO2 at a coastal mooring: Internal consistency, seasonal cycles, and interannual variability

NASA Astrophysics Data System (ADS)

Reimer, Janet J.; Cai, Wei-Jun; Xue, Liang; Vargas, Rodrigo; Noakes, Scott; Hu, Xinping; Signorini, Sergio R.; Mathis, Jeremy T.; Feely, Richard A.; Sutton, Adrienne J.; Sabine, Christopher; Musielewicz, Sylvia; Chen, Baoshan; Wanninkhof, Rik

2017-08-01

Marine carbonate system monitoring programs often consist of multiple observational methods that include underway cruise data, moored autonomous time series, and discrete water bottle samples. Monitored parameters include all, or some of the following: partial pressure of CO2 of the water (pCO2w) and air, dissolved inorganic carbon (DIC), total alkalinity (TA), and pH. Any combination of at least two of the aforementioned parameters can be used to calculate the others. In this study at the Gray's Reef (GR) mooring in the South Atlantic Bight (SAB) we: examine the internal consistency of pCO2w from underway cruise, moored autonomous time series, and calculated from bottle samples (DIC-TA pairing); describe the seasonal to interannual pCO2w time series variability and air-sea flux (FCO2), as well as describe the potential sources of pCO2w variability; and determine the source/sink for atmospheric pCO2. Over the 8.5 years of GR mooring time series, mooring-underway and mooring-bottle calculated-pCO2w strongly correlate with r-values > 0.90. pCO2w and FCO2 time series follow seasonal thermal patterns; however, seasonal non-thermal processes, such as terrestrial export, net biological production, and air-sea exchange also influence variability. The linear slope of time series pCO2w increases by 5.2 ± 1.4 μatm y-1 with FCO2 increasing 51-70 mmol m-2 y-1. The net FCO2 sign can switch interannually with the magnitude varying greatly. Non-thermal pCO2w is also increasing over the time series, likely indicating that terrestrial export and net biological processes drive the long term pCO2w increase.

Interannual Variability in the Position and Strength of the East Asian Jet Stream and Its Relation to Large - scale Circulation

NASA Astrophysics Data System (ADS)

Chan, Duo; Zhang, Yang; Wu, Qigang

2013-04-01

East Asian Jet Stream (EASJ) is charactered by obvious interannual variability in strength and position (latitude), with wide impacts on East Asian climate in all seasons. In this study, two indices are established to measure the interannual variability in intensity and position of EAJS. Possible causing factors, including both local signals and non-local large-scale circulation, are examined using NCAP-NCAR reanalysis data to investigate their relations with jet variation. Our analysis shows that the relationship between the interannual variations of EASJ and these factors depends on seasons. In the summer, both the intensity and position of EASJ are closely related to the meridional gradient of local surface temperature, but display no apparent relationship with the larg-scale circulation. In cold seasons (autumn, winter and spring), both the local factor and the large-scale circulation, i.e. the Pacific/North American teleconnection pattern (PNA), play important roles in the interannual variability of the jet intensity. The variability in the jet position, however, is more correlated to the Arctic Oscillation (AO), especially in winter. Diagnostic analysis indicates that transient eddy activity plays an important role in connecting the interannual variability of EASJ position with AO.

Quantifying the Seasonal and Interannual Variability of North American Isoprene Emissions Using Satellite Observations of the Formaldehyde Column

NASA Technical Reports Server (NTRS)

Palmer, Paul I.; Abbot, Dorian S.; Fu, Tzung-May; Jacob, Daniel J.; Chance, Kelly; Kurosu, Thomas P.; Guenther, Alex; Wiedinmyer, Christine; Stanton, Jenny C.; Pilling, Michael J.;

Sensitivity of the interannual variability of mineral aerosol simulations to meteorological forcing dataset

DOE PAGES

Smith, Molly B.; Mahowald, Natalie M.; Albani, Samuel; ...

2017-03-07

Interannual variability in desert dust is widely observed and simulated, yet the sensitivity of these desert dust simulations to a particular meteorological dataset, as well as a particular model construction, is not well known. Here we use version 4 of the Community Atmospheric Model (CAM4) with the Community Earth System Model (CESM) to simulate dust forced by three different reanalysis meteorological datasets for the period 1990–2005. We then contrast the results of these simulations with dust simulated using online winds dynamically generated from sea surface temperatures, as well as with simulations conducted using other modeling frameworks but the same meteorological forcings, in order tomore » determine the sensitivity of climate model output to the specific reanalysis dataset used. For the seven cases considered in our study, the different model configurations are able to simulate the annual mean of the global dust cycle, seasonality and interannual variability approximately equally well (or poorly) at the limited observational sites available. Altogether, aerosol dust-source strength has remained fairly constant during the time period from 1990 to 2005, although there is strong seasonal and some interannual variability simulated in the models and seen in the observations over this time period. Model interannual variability comparisons to observations, as well as comparisons between models, suggest that interannual variability in dust is still difficult to simulate accurately, with averaged correlation coefficients of 0.1 to 0.6. Because of the large variability, at least 1 year of observations at most sites are needed to correctly observe the mean, but in some regions, particularly the remote oceans of the Southern Hemisphere, where interannual variability may be larger than in the Northern Hemisphere, 2–3 years of data are likely to be needed.« less

Inter-annual cascade effect on marine food web: A benthic pathway lagging nutrient supply to pelagic fish stock

PubMed Central

Fernandes, Lohengrin Dias de Almeida; Fagundes Netto, Eduardo Barros; Coutinho, Ricardo

2017-01-01

Currently, spatial and temporal changes in nutrients availability, marine planktonic, and fish communities are best described on a shorter than inter-annual (seasonal) scale, primarily because the simultaneous year-to-year variations in physical, chemical, and biological parameters are very complex. The limited availability of time series datasets furnishing simultaneous evaluations of temperature, nutrients, plankton, and fish have limited our ability to describe and to predict variability related to short-term process, as species-specific phenology and environmental seasonality. In the present study, we combine a computational time series analysis on a 15-year (1995–2009) weekly-sampled time series (high-resolution long-term time series, 780 weeks) with an Autoregressive Distributed Lag Model to track non-seasonal changes in 10 potentially related parameters: sea surface temperature, nutrient concentrations (NO2, NO3, NH4 and PO4), phytoplankton biomass (as in situ chlorophyll a biomass), meroplankton (barnacle and mussel larvae), and fish abundance (Mugil liza and Caranx latus). Our data demonstrate for the first time that highly intense and frequent upwelling years initiate a huge energy flux that is not fully transmitted through classical size-structured food web by bottom-up stimulus but through additional ontogenetic steps. A delayed inter-annual sequential effect from phytoplankton up to top predators as carnivorous fishes is expected if most of energy is trapped into benthic filter feeding organisms and their larval forms. These sequential events can explain major changes in ecosystem food web that were not predicted in previous short-term models. PMID:28886162

Precipitation interannual variability and predictions for West Africa from the National Multi-Model Ensemble dataset

NASA Astrophysics Data System (ADS)

Thiaw, W. M.

2013-12-01

The ability of coupled climate models from the national multi-model ensemble (NMME) dataset to reproduce the basic state and interannual variability of precipitation in West Africa and associated teleconnections is examined. The analysis is for the period 1982-2010 for most of the models, which corresponds to the NMME hindcast period, except for the CFS version 1 (CFSv1) which covers the period 1981-2009. The satellite based CPC African Rainfall Climatology (ARC2) data is used as proxy for observed rainfall and to validate the models. We examine rainfall patterns throughout the year. Models are able to reproduce the north-south migration of precipitation from winter and spring when the area of maximum precipitation is located in Central Africa and the Gulf of Guinea region to the summer when it is in northern Sub-Saharan Africa, and the later return to the south. Models also appropriately place precipitation over the Gulf of Guinea region during the equinoxes in MAM and OND. However, there are considerable differences in the representation of the intensities and locations of the rainfall. Three of the models including the two versions of the NCEP CFS and the NASA models also have a systematic dry (wet) bias over the Sahel (Gulf of Guinea region) during the summer rainfall season, while the others show alternating wet and dry biases across West Africa. All models have spatially averaged values of standard deviation lower than that observed. Models are also able to reproduce to some extent the main features of the precipitation variability maximum, but again with deficiencies in the amplitudes and locations. The areas of highest variability are generally depicted, but there are significant differences among the models, and even between the two versions of the CFS. Teleconnections in the models are investigated by first conducting an EOF in the precipitation anomaly fields and then perform a regression of the first or second EOF time series onto the global SST

Seasonality, interannual variability, and linear tendency of wind speeds in the northeast Brazil from 1986 to 2011.

PubMed

Torres Silva dos Santos, Alexandre; Moisés Santos e Silva, Cláudio

2013-01-01

Wind speed analyses are currently being employed in several fields, especially in wind power generation. In this study, we used wind speed data from records of Universal Fuess anemographs at an altitude of 10 m from 47 weather stations of the National Institute of Meteorology (Instituto Nacional de Meteorologia-INMET) from January 1986 to December 2011. The objective of the study was to investigate climatological aspects and wind speed trends. To this end, the following methods were used: filling of missing data, descriptive statistical calculations, boxplots, cluster analysis, and trend analysis using the Mann-Kendall statistical method. The seasonal variability of the average wind speeds of each group presented higher values for winter and spring and lower values in the summer and fall. The groups G1, G2, and G5 showed higher annual averages in the interannual variability of wind speeds. These observed peaks were attributed to the El Niño and La Niña events, which change the behavior of global wind circulation and influence wind speeds over the region. Trend analysis showed more significant negative values for the G3, G4, and G5 groups for all seasons of the year and in the annual average for the period under study.

Seasonality, Interannual Variability, and Linear Tendency of Wind Speeds in the Northeast Brazil from 1986 to 2011

PubMed Central

Santos e Silva, Cláudio Moisés

2013-01-01

Wind speed analyses are currently being employed in several fields, especially in wind power generation. In this study, we used wind speed data from records of Universal Fuess anemographs at an altitude of 10 m from 47 weather stations of the National Institute of Meteorology (Instituto Nacional de Meteorologia-INMET) from January 1986 to December 2011. The objective of the study was to investigate climatological aspects and wind speed trends. To this end, the following methods were used: filling of missing data, descriptive statistical calculations, boxplots, cluster analysis, and trend analysis using the Mann-Kendall statistical method. The seasonal variability of the average wind speeds of each group presented higher values for winter and spring and lower values in the summer and fall. The groups G1, G2, and G5 showed higher annual averages in the interannual variability of wind speeds. These observed peaks were attributed to the El Niño and La Niña events, which change the behavior of global wind circulation and influence wind speeds over the region. Trend analysis showed more significant negative values for the G3, G4, and G5 groups for all seasons of the year and in the annual average for the period under study. PMID:24250267

Simulation and Prediction of Warm Season Drought in North America

NASA Technical Reports Server (NTRS)

Wang, Hailan; Chang, Yehui; Schubert, Siegfried D.; Koster, Randal D.

2018-01-01

This presentation presents our recent work on model simulation and prediction of warm season drought in North America. The emphasis will be on the contribution from the leading modes of subseasonal atmospheric circulation variability, which are often present in the form of stationary Rossby waves. Here we take advantage of the results from observations, reanalyses, and simulations and reforecasts performed using the NASA Goddard Earth Observing System (GEOS-5) atmospheric and coupled General Circulation Model (GCM). Our results show that stationary Rossby waves play a key role in Northern Hemisphere (NH) atmospheric circulation and surface meteorology variability on subseasonal timescales. In particular, such waves have been crucial to the development of recent short-term warm season heat waves and droughts over North America (e.g. the 1988, 1998, and 2012 summer droughts) and northern Eurasia (e.g., the 2003 summer heat wave over Europe and the 2010 summer drought and heat wave over Russia). Through an investigation of the physical processes by which these waves lead to the development of warm season drought in North America, it is further found that these waves can serve as a potential source of drought predictability. In order to properly represent their effect and exploit this source of predictability, a model needs to correctly simulate the Northern Hemisphere (NH) mean jet streams and be able to predict the sources of these waves. Given the NASA GEOS-5 AGCM deficiency in simulating the NH jet streams and tropical convection during boreal summer, an approach has been developed to artificially remove much of model mean biases, which leads to considerable improvement in model simulation and prediction of stationary Rossby waves and drought development in North America. Our study points to the need to identify key model biases that limit model simulation and prediction of regional climate extremes, and diagnose the origin of these biases so as to inform modeling

Temporal variability of the NPP-GPP ratio at seasonal and interannual time scales in a temperate beech forest

NASA Astrophysics Data System (ADS)

Campioli, M.; Gielen, B.; Göckede, M.; Papale, D.; Bouriaud, O.; Granier, A.

2011-09-01

The allocation of carbon (C) taken up by the tree canopy for respiration and production of tree organs with different construction and maintenance costs, life span and decomposition rate, crucially affects the residence time of C in forests and their C cycling rate. The carbon-use efficiency, or ratio between net primary production (NPP) and gross primary production (GPP), represents a convenient way to analyse the C allocation at the stand level. In this study, we extend the current knowledge on the NPP-GPP ratio in forests by assessing the temporal variability of the NPP-GPP ratio at interannual (for 8 years) and seasonal (for 1 year) scales for a young temperate beech stand, reporting dynamics for both leaves and woody organs, in particular stems. NPP was determined with biometric methods/litter traps, whereas the GPP was estimated via the eddy covariance micrometeorological technique. The interannual variability of the proportion of C allocated to leaf NPP, wood NPP and leaf plus wood NPP (on average 11% yr-1, 29% yr-1 and 39% yr-1, respectively) was significant among years with up to 12% yr-1 variation in NPP-GPP ratio. Studies focusing on the comparison of NPP-GPP ratio among forests and models using fixed allocation schemes should take into account the possibility of such relevant interannual variability. Multiple linear regressions indicated that the NPP-GPP ratio of leaves and wood significantly correlated with environmental conditions. Previous year drought and air temperature explained about half of the NPP-GPP variability of leaves and wood, respectively, whereas the NPP-GPP ratio was not decreased by severe drought, with large NPP-GPP ratio on 2003 due mainly to low GPP. During the period between early May and mid June, the majority of GPP was allocated to leaf and stem NPP, whereas these sinks were of little importance later on. Improved estimation of seasonal GPP and of the contribution of previous-year reserves to stem growth, as well as reduction

Seasonal and interannual variability of climate and vegetation indices across the Amazon

PubMed Central

Brando, Paulo M.; Goetz, Scott J.; Baccini, Alessandro; Nepstad, Daniel C.; Beck, Pieter S. A.; Christman, Mary C.

2010-01-01

Drought exerts a strong influence on tropical forest metabolism, carbon stocks, and ultimately the flux of carbon to the atmosphere. Satellite-based studies have suggested that Amazon forests green up during droughts because of increased sunlight, whereas field studies have reported increased tree mortality during severe droughts. In an effort to reconcile these apparently conflicting findings, we conducted an analysis of climate data, field measurements, and improved satellite-based measures of forest photosynthetic activity. Wet-season precipitation and plant-available water (PAW) decreased over the Amazon Basin from 1996−2005, and photosynthetically active radiation (PAR) and air dryness (expressed as vapor pressure deficit, VPD) increased from 2002–2005. Using improved enhanced vegetation index (EVI) measurements (2000–2008), we show that gross primary productivity (expressed as EVI) declined with VPD and PAW in regions of sparse canopy cover across a wide range of environments for each year of the study. In densely forested areas, no climatic variable adequately explained the Basin-wide interannual variability of EVI. Based on a site-specific study, we show that monthly EVI was relatively insensitive to leaf area index (LAI) but correlated positively with leaf flushing and PAR measured in the field. These findings suggest that production of new leaves, even when unaccompanied by associated changes in LAI, could play an important role in Basin-wide interannual EVI variability. Because EVI variability was greatest in regions of lower PAW, we hypothesize that drought could increase EVI by synchronizing leaf flushing via its effects on leaf bud development. PMID:20679201

Seasonal and interannual variability of climate and vegetation indices across the Amazon.

PubMed

Brando, Paulo M; Goetz, Scott J; Baccini, Alessandro; Nepstad, Daniel C; Beck, Pieter S A; Christman, Mary C

2010-08-17

Drought exerts a strong influence on tropical forest metabolism, carbon stocks, and ultimately the flux of carbon to the atmosphere. Satellite-based studies have suggested that Amazon forests green up during droughts because of increased sunlight, whereas field studies have reported increased tree mortality during severe droughts. In an effort to reconcile these apparently conflicting findings, we conducted an analysis of climate data, field measurements, and improved satellite-based measures of forest photosynthetic activity. Wet-season precipitation and plant-available water (PAW) decreased over the Amazon Basin from 1996-2005, and photosynthetically active radiation (PAR) and air dryness (expressed as vapor pressure deficit, VPD) increased from 2002-2005. Using improved enhanced vegetation index (EVI) measurements (2000-2008), we show that gross primary productivity (expressed as EVI) declined with VPD and PAW in regions of sparse canopy cover across a wide range of environments for each year of the study. In densely forested areas, no climatic variable adequately explained the Basin-wide interannual variability of EVI. Based on a site-specific study, we show that monthly EVI was relatively insensitive to leaf area index (LAI) but correlated positively with leaf flushing and PAR measured in the field. These findings suggest that production of new leaves, even when unaccompanied by associated changes in LAI, could play an important role in Basin-wide interannual EVI variability. Because EVI variability was greatest in regions of lower PAW, we hypothesize that drought could increase EVI by synchronizing leaf flushing via its effects on leaf bud development.

Modeled Carbon Cycle Responses to Altered Precipitation Amount and Interannual Variation in Desert Grassland

NASA Astrophysics Data System (ADS)

Liang, G.; Wilcox, K.; Rudgers, J.; Litvak, M. E.; Newsome, S. D.; Collins, S. L.; Pockman, W.; Luo, Y.

2017-12-01

Altered amounts and increased interannual variation of precipitation are likely to occur on a regional to global scale in the late 21st Century, yet understanding the interactive effects of these changes on ecosystem processes is limited. Here, we modeled the responses of the carbon cycle in a desert grassland at the Sevilleta National Wildlife Refuge (SEV) to changes in precipitation amount and interannual variation using the Terrestrial Ecosystem model. After model calibration, we generated 100-year hourly weather data by randomly repeated sampling of observed hourly weather data at SEV from 2000 to 2012. We then modified this 100-year time series to create six climate scenarios: (1) ambient (AMB); (2) increased air temperature by 4.3 °C in summer and 3.3 °C in other seasons for each year (IT); (3) 20% decreased precipitation amount for every event (DP); (4) combined IT and DP (DPT); (5) 100% increased precipitation interannual variance without changing the mean (IV); (6) combined IT and IV (IVT). Our results showed IV significantly increased the sensitivity of NPP to continuous extreme drought. In addition, the increased number of extreme drought years caused by IV exacerbated the negative influence of individual extreme drought events on soil organic carbon (SOC). The IV climate scenario showed the highest interannual variance of carbon fluxes and SOC, but increased temperature reduced this variance. DP and IV decreased NPP by 10.7% and 18.3% compared with AMB, respectively, and the negative impact of IV on NPP was more severe than that of DP. Our results indicate that the increased interannual variation in precipitation could have more severe impacts on terrestrial ecosystems that exceed the decrease in predicted average annual precipitation.

Effects of ocean initial perturbation on developing phase of ENSO in a coupled seasonal prediction model

NASA Astrophysics Data System (ADS)

Lee, Hyun-Chul; Kumar, Arun; Wang, Wanqiu

2018-03-01

Coupled prediction systems for seasonal and inter-annual variability in the tropical Pacific are initialized from ocean analyses. In ocean initial states, small scale perturbations are inevitably smoothed or distorted by the observational limits and data assimilation procedures, which tends to induce potential ocean initial errors for the El Nino-Southern Oscillation (ENSO) prediction. Here, the evolution and effects of ocean initial errors from the small scale perturbation on the developing phase of ENSO are investigated by an ensemble of coupled model predictions. Results show that the ocean initial errors at the thermocline in the western tropical Pacific grow rapidly to project on the first mode of equatorial Kelvin wave and propagate to the east along the thermocline. In boreal spring when the surface buoyancy flux weakens in the eastern tropical Pacific, the subsurface errors influence sea surface temperature variability and would account for the seasonal dependence of prediction skill in the NINO3 region. It is concluded that the ENSO prediction in the eastern tropical Pacific after boreal spring can be improved by increasing the observational accuracy of subsurface ocean initial states in the western tropical Pacific.

Seasonal hypoxia in eutrophic stratified coastal shelves: mechanisms, sensibilities and interannual variability from the North-Western Black Sea case

NASA Astrophysics Data System (ADS)

Capet, A.; Beckers, J.-M.; Grégoire, M.

2012-12-01

The Black Sea north-western shelf (NWS) is a~shallow eutrophic area in which seasonal stratification of the water column isolates bottom waters from the atmosphere and prevents ventilation to compensate for the large consumption of oxygen, due to respiration in the bottom waters and in the sediments. A 3-D coupled physical biogeochemical model is used to investigate the dynamics of bottom hypoxia in the Black Sea NWS at different temporal scales from seasonal to interannual (1981-2009) and to differentiate the driving factors (climatic versus eutrophication) of hypoxic conditions in bottom waters. Model skills are evaluated by comparison with 14 500 in-situ oxygen measurements available in the NOAA World Ocean Database and the Black Sea Commission data. The choice of skill metrics and data subselections orientate the validation procedure towards specific aspects of the oxygen dynamics, and prove the model's ability to resolve the seasonal cycle and interannual variability of oxygen concentration as well as the spatial location of the oxygen depleted waters and the specific threshold of hypoxia. During the period 1981-2009, each year exhibits seasonal bottom hypoxia at the end of summer. This phenomenon essentially covers the northern part of the NWS, receiving large inputs of nutrients from the Danube, Dniestr and Dniepr rivers, and extends, during the years of severe hypoxia, towards the Romanian Bay of Constanta. In order to explain the interannual variability of bottom hypoxia and to disentangle its drivers, a statistical model (multiple linear regression) is proposed using the long time series of model results as input variables. This statistical model gives a general relationship that links the intensity of hypoxia to eutrophication and climate related variables. The use of four predictors allows to reproduce 78% of hypoxia interannual variability: the annual nitrate discharge (N), the sea surface temperature in the month preceding stratification (T), the

[Seasonal and interannual variations of sockeye salmon (Oncorhynchus nerka) microsatellite DNA in two Kamchatka lake-river systems].

PubMed

Khrustaleva, A M; Zelenina, D A

2008-07-01

Seasonal and interannual variations in the sockeye salmon populations from two lake-river systems of the East and West Kamchatka were studied. Stability of allele and genotypic frequencies of six microsatellite DNA loci in the adjacent generations and spawning populations of the sockeye salmon of the Bol'shaya River was confirmed experimentally. The pairwise intersample differentiation (F(st)) of the local sockeye salmon populations from the southwestern Kamchatka coast (Ozernaya and Bol'shaya Rivers)was almost 7 times higher than the corresponding values for the spawning populations of the Bol'shaya River sockeye salmon of the adjacent years; 15 times, for the adjacent Bol'shaya River sockeye salmon generations; and four times, for the seasonal races within the Kamchatka River.

How does the terrestrial carbon exchange respond to inter-annual climatic variations? A quantification based on atmospheric CO2 data

NASA Astrophysics Data System (ADS)

Rödenbeck, Christian; Zaehle, Sönke; Keeling, Ralph; Heimann, Martin

2018-04-01

The response of the terrestrial net ecosystem exchange (NEE) of CO2 to climate variations and trends may crucially determine the future climate trajectory. Here we directly quantify this response on inter-annual timescales by building a linear regression of inter-annual NEE anomalies against observed air temperature anomalies into an atmospheric inverse calculation based on long-term atmospheric CO2 observations. This allows us to estimate the sensitivity of NEE to inter-annual variations in temperature (seen as a climate proxy) resolved in space and with season. As this sensitivity comprises both direct temperature effects and the effects of other climate variables co-varying with temperature, we interpret it as inter-annual climate sensitivity. We find distinct seasonal patterns of this sensitivity in the northern extratropics that are consistent with the expected seasonal responses of photosynthesis, respiration, and fire. Within uncertainties, these sensitivity patterns are consistent with independent inferences from eddy covariance data. On large spatial scales, northern extratropical and tropical inter-annual NEE variations inferred from the NEE-T regression are very similar to the estimates of an atmospheric inversion with explicit inter-annual degrees of freedom. The results of this study offer a way to benchmark ecosystem process models in more detail than existing effective global climate sensitivities. The results can also be used to gap-fill or extrapolate observational records or to separate inter-annual variations from longer-term trends.

Regional simulation of interannual variability over South America

NASA Astrophysics Data System (ADS)

Misra, V.; Dirmeyer, P. A.; Kirtman, B. P.; Juang, H.-M. Henry; Kanamitsu, M.

2002-08-01

Three regional climate simulations covering the austral summer season during three contrasting phases of the El Niño-Southern Oscillation cycle were conducted with the Regional Spectral Model (RSM) developed at the National Centers for Environmental Prediction (NCEP). The simulated interannual variability of precipitation over the Amazon River Basin, the Intertropical Convergence Zone, the Pacific and Atlantic Ocean basins, and extratropical South America compare reasonably well with observations. The RSM optimally filters the peturbations about a time-varying base field, thereby enhancing the information content of the global NCEP reanalysis. The model is better than the reanalysis in reproducing the observed interannual variability of outgoing longwave radiation at both high frequencies (3-30 days) and intraseasonal (30-60 days) scales. The low-level jet shows a peak in its speed in 1998 and a minimum in the 1999 simulations. The lag correlation of the jet index with convection over various areas in continental South America indicates that the jet induces precipitation over the Pampas region downstream. A detailed moisture budget was conducted over various subregions. This budget reveals that moisture flux convergence determines most of the interannual variability of precipitation over the Amazon Basin, the Atlantic Intertropical Convergence Zone, and the Nordeste region of Brazil. However, both surface evaporation and surface moisture flux convergence were found to be critical in determining the interannual variability of precipitation over the southern Pampas, Gran Chaco area, and the South Atlantic Convergence Zone.

Causes of skill in seasonal predictions of the Arctic Oscillation

NASA Astrophysics Data System (ADS)

Kumar, Arun; Chen, Mingyue

2017-11-01

Based on an analysis of hindcasts from a seasonal forecast system, complemented by the analysis of a large ensemble of AMIP simulations, possible causes for skillful prediction of the winter Arctic Oscillation (AO) on a seasonal time-scale are analyzed. The possibility that the recent increase in AO skill could be due to model improvements, or due to changes in the persistence characteristics of the AO, is first discounted. The analysis then focuses on exploring the possibility that the recent increase in prediction skill in AO may be due to sampling variations or could have physical causes. Temporal variations in AO skill due entirely to sampling alone cannot be discounted as this is a fundamental constraint on verifications over a short time-series. This notion is supported from theoretical considerations, and from the analysis of the temporal variations in the perfect model skill where substantial variations in skill due to sampling alone are documented. As for the physical causes, the analysis indicates possible links in the prediction skill of AO with the SST forcing from the tropics, particularly related to the SST variations associated with the Trans-Niño Index (TNI). Interannual and low frequency variations in the TNI could have contributed to similar temporal variations in AO skill. For example, a dominance of central Pacific El Niño events after 2000 (a reflection of low-frequency variations in TNI) coincided with an increase in the prediction skill of AO. The analysis approach and results provide an avenue for further investigations; for example, model simulations forced with the SST pattern associated with the TNI, to establish or reaffirm causes for AO skill.

Predictability and prediction of tropical cyclones on daily to interannual time scales

NASA Astrophysics Data System (ADS)

Belanger, James Ian

regions. Following the TC predictability studies, a proof-of-concept operational forecast system for North Atlantic TCs is presented for daily to intraseasonal time scales. Findings from the predictability studies are used in conjunction with recently developed forecast calibration techniques to render the VarEPS and ECMFS forecasts more useful in an operational setting. The proposed combination of bias-calibrated regional probabilistic forecast guidance along with objectively-defined measures of confidence is a new way of providing TC forecasts on intraseasonal time scales. On interannual time scales, the predictability of TCs is examined by considering their relationship with tropical Atlantic easterly waves. First, a set of easterly wave climatologies for the Climate Forecast System-Reanalysis, ERA-Interim, ERA-40, and NCEP/NCAR Reanalysis are developed using a new easterly wave tracking algorithm based on 700 hPa curvature relative vorticity anomalies. From the reanalysis-derived easterly wave climatologies, a moderately positive and statistically significant relationship is seen with tropical Atlantic TCs, suggesting that approximately 20-30% of the total variance in the number of TCs on interannual time scales may be explained by the frequency of easterly waves. In relation to large-scale climate modes, the Atlantic Multidecadal Oscillation (AMO) and Atlantic Meridional Mode (AMM) exhibit the strongest positive covariability with Atlantic easterly wave frequency. Besides changes in the number of easterly waves, the intensification efficiency of easterly waves, which is the percentage of waves that induce North Atlantic TC formation, has also been evaluated. These findings offer a plausible physical explanation for the recent increase in the number of NATL TCs, as it has been concomitant with an increasing trend in both the number of tropical Atlantic easterly waves and intensification efficiency. In addition, the easterly wave-tropical cyclone pathway is likely an

Transport Pathways for Asian Pollution Outflow Over the Pacific: Interannual and Seasonal Variations

NASA Technical Reports Server (NTRS)

Liu, Hong-Yu; Jacob, Daniel J.; Bey, Isabelle; Yantosca, Robert M.; Duncan, Bryan N.; Sachse, Glen W.

2003-01-01

The meteorological pathways contributing to Asian pollution outflow over the Pacific are examined with a global three-dimensional model analysis of CO observations from the Transport and Chemical Evolution over the Pacific (TRACE-P) aircraft mission (February-April 2001). The model is used also to place the TRACE-P observations in an interannual (1994-2001) and seasonal context. The major process driving Asian pollution outflow in spring is frontal lifting ahead of southeastward-moving cold fronts (the leading edge of cold surges) and transport in the boundary layer behind the cold fronts. Orographic lifting over central and eastern China combines with the cold fronts to promote the transport of Chinese pollution to the free troposphere. Outflow of seasonal biomass burning in Southeast Asia during spring takes place mostly by deep convection but also by northeastward transport and frontal lifting, mixing with the anthropogenic outflow. Boundary layer outflow over the western Pacific is largely devoid of biomass burning influence. European and African (biomass burning) plumes in Asian outflow during TRACE-P were weak (less than 60 ppbv and 20 ppbv CO, respectively) and were not detectable in the observations because of superposition of the much larger Asian pollution signal. Spring 2001 (La Nina) was characterized by unusually frequent cold surge events in the Asian Pacific rim and strong convection in Southeast Asia, leading to unusually strong boundary layer outflow of anthropogenic emissions and convective outflow of biomass burning emissions in the upper troposphere. The Asian outflow flux of CO to the Pacific is found to vary seasonally by a factor of 3-4 (maximum in March and minimum in summer). The March maximum results from frequent cold surge events and seasonal biomass burning emissions.

Transport pathways for Asian pollution outflow over the Pacific: Interannual and seasonal variations

NASA Astrophysics Data System (ADS)

Liu, Hongyu; Jacob, Daniel J.; Bey, Isabelle; Yantosca, Robert M.; Duncan, Bryan N.; Sachse, Glen W.

2003-10-01

The meteorological pathways contributing to Asian pollution outflow over the Pacific are examined with a global three-dimensional model analysis of CO observations from the Transport and Chemical Evolution over the Pacific (TRACE-P) aircraft mission (February-April 2001). The model is used also to place the TRACE-P observations in an interannual (1994-2001) and seasonal context. The major process driving Asian pollution outflow in spring is frontal lifting ahead of southeastward-moving cold fronts (the leading edge of cold surges) and transport in the boundary layer behind the cold fronts. Orographic lifting over central and eastern China combines with the cold fronts to promote the transport of Chinese pollution to the free troposphere. Outflow of seasonal biomass burning in Southeast Asia during spring takes place mostly by deep convection but also by northeastward transport and frontal lifting, mixing with the anthropogenic outflow. Boundary layer outflow over the western Pacific is largely devoid of biomass burning influence. European and African (biomass burning) plumes in Asian outflow during TRACE-P were weak (<60 ppbv and 20 ppbv CO, respectively) and were not detectable in the observations because of superposition of the much larger Asian pollution signal. Spring 2001 (La Niña) was characterized by unusually frequent cold surge events in the Asian Pacific rim and strong convection in Southeast Asia, leading to unusually strong boundary layer outflow of anthropogenic emissions and convective outflow of biomass burning emissions in the upper troposphere. The Asian outflow flux of CO to the Pacific is found to vary seasonally by a factor of 3-4 (maximum in March and minimum in summer). The March maximum results from frequent cold surge events and seasonal biomass burning emissions.

Observed seasonal and interannual variability of the near-surface thermal structure of the Arabian Sea Warm Pool

NASA Astrophysics Data System (ADS)

Rao, R. R.; Ramakrishna, S. S. V. S.

2017-06-01

The observed seasonal and interannual variability of near-surface thermal structure of the Arabian Sea Warm Pool (ASWP) is examined utilizing a reanalysis data set for the period 1990-2008. During a year, the ASWP progressively builds from February, reaches its peak by May only in the topmost 60 m water column. The ASWP Index showed a strong seasonal cycle with distinct interannual signatures. The years with higher (lower) sea surface temperature (SST) and larger (smaller) spatial extent are termed as strong (weak) ASWP years. The differences in the magnitude and spatial extent of thermal structure between the strong and weak ASWP regimes are seen more prominently in the topmost 40 m water column. The heat content values with respect to 28 °C isotherm (HC28) are relatively higher (lower) during strong (weak) ASWP years. Even the secondary peak in HC28 seen during the preceding November-December showed higher (lower) magnitude during the strong ASWP (weak) years. The influence of the observed variability in the surface wind field, surface net air-sea heat flux, near-surface mixed layer thickness, sea surface height (SSH) anomaly, depth of 20 °C isotherm and barrier layer thickness is examined to explain the observed differences in the near-surface thermal structure of the ASWP between strong and weak regimes. The surface wind speed is much weaker in particular during the preceding October and February-March corresponding to the strong ASWP years when compared to those of the weak ASWP years implying its important role. Both stronger winter cooling during weak ASWP years and stronger pre-monsoon heating during strong ASWP years through the surface air-sea heat fluxes contribute to the observed sharp contrast in the magnitudes of both the regimes of the ASWP. The upwelling Rossby wave during the preceding summer monsoon, post-monsoon and winter seasons is stronger corresponding to the weak ASWP regime when compared to the strong ASWP regime resulting in greater

Seasonal Prediction of Hydro-Climatic Extremes in the Greater Horn of Africa Under Evolving Climate Conditions to Support Adaptation Strategies

NASA Astrophysics Data System (ADS)

Tadesse, T.; Zaitchik, B. F.; Habib, S.; Funk, C. C.; Senay, G. B.; Dinku, T.; Policelli, F. S.; Block, P.; Baigorria, G. A.; Beyene, S.; Wardlow, B.; Hayes, M. J.

2014-12-01

The development of effective strategies to adapt to changes in the character of droughts and floods in Africa will rely on improved seasonal prediction systems that are robust to an evolving climate baseline and can be integrated into disaster preparedness and response. Many efforts have been made to build models to improve seasonal forecasts in the Greater Horn of Africa region (GHA) using satellite and climate data, but these efforts and models must be improved and translated into future conditions under evolving climate conditions. This has considerable social significance, but is challenged by the nature of climate predictability and the adaptability of coupled natural and human systems facing exposure to climate extremes. To address these issues, work is in progress under a project funded by NASA. The objectives of the project include: 1) Characterize and explain large-scale drivers in the ocean-atmosphere-land system associated with years of extreme flood or drought in the GHA. 2) Evaluate the performance of state-of-the-art seasonal forecast methods for prediction of decision-relevant metrics of hydrologic extremes. 3) Apply seasonal forecast systems to prediction of socially relevant impacts on crops, flood risk, and economic outcomes, and assess the value of these predictions to decision makers. 4) Evaluate the robustness of seasonal prediction systems to evolving climate conditions. The National Drought Mitigation Center (University of Nebraska-Lincoln, USA) is leading this project in collaboration with the USGS, Johns Hopkins University, University of Wisconsin-Madison, the International Research Institute for Climate and Society, NASA, and GHA local experts. The project is also designed to have active engagement of end users in various sectors, university researchers, and extension agents in GHA through workshops and/or webinars. This project is expected improve and implement new and existing climate- and remote sensing-based agricultural

Intraseasonal and Interannual Variability of Mars Present Climate

NASA Astrophysics Data System (ADS)

Hollingsworth, Jeffery L.; Bridger, Alison F. C.; Haberle, Robert M.

1996-01-01

This is a Final Report for a Joint Research Interchange (JRI) between NASA Ames Research Center and San Jose State University, Department of Meteorology. The focus of this JRI has been to investigate the nature of intraseasonal and interannual variability of Mars'present climate. We have applied a three-dimensional climate model based on the full hydrostatic primitive equations to determine the spatial, but primarily, the temporal structures of the planet's large-scale circulation as it evolves during a given seasonal advance, and, over multi-annual cycles. The particular climate model applies simplified physical parameterizations and is computationally efficient. It could thus easily be integrated in a perpetual season or advancing season configuration, as well as over many Mars years. We have assessed both high and low-frequency components of the circulation (i.e., motions having periods of Omicron(2-10 days) or greater than Omicron(10 days), respectively). Results from this investigation have explored the basic issue whether Mars' climate system is naturally 'chaotic' associated with nonlinear interactions of the large-scale circulation-regardless of any allowance for year-to-year variations in external forcing mechanisms. Titles of papers presented at scientific conferences and a manuscript to be submitted to the scientific literature are provided. An overview of a areas for further investigation is also presented.

Comparison of phenology models for predicting the onset of growing season over the Northern Hemisphere.

PubMed

Fu, Yang; Zhang, Haicheng; Dong, Wenjie; Yuan, Wenping

2014-01-01

Vegetation phenology models are important for examining the impact of climate change on the length of the growing season and carbon cycles in terrestrial ecosystems. However, large uncertainties in present phenology models make accurate assessment of the beginning of the growing season (BGS) a challenge. In this study, based on the satellite-based phenology product (i.e. the V005 MODIS Land Cover Dynamics (MCD12Q2) product), we calibrated four phenology models, compared their relative strength to predict vegetation phenology; and assessed the spatial pattern and interannual variability of BGS in the Northern Hemisphere. The results indicated that parameter calibration significantly influences the models' accuracy. All models showed good performance in cool regions but poor performance in warm regions. On average, they explained about 67% (the Growing Degree Day model), 79% (the Biome-BGC phenology model), 73% (the Number of Growing Days model) and 68% (the Number of Chilling Days-Growing Degree Day model) of the BGS variations over the Northern Hemisphere. There were substantial differences in BGS simulations among the four phenology models. Overall, the Biome-BGC phenology model performed best in predicting the BGS, and showed low biases in most boreal and cool regions. Compared with the other three models, the two-phase phenology model (NCD-GDD) showed the lowest correlation and largest biases with the MODIS phenology product, although it could catch the interannual variations well for some vegetation types. Our study highlights the need for further improvements by integrating the effects of water availability, especially for plants growing in low latitudes, and the physiological adaptation of plants into phenology models.

Comparison of Phenology Models for Predicting the Onset of Growing Season over the Northern Hemisphere

PubMed Central

Fu, Yang; Zhang, Haicheng; Dong, Wenjie; Yuan, Wenping

2014-01-01

Vegetation phenology models are important for examining the impact of climate change on the length of the growing season and carbon cycles in terrestrial ecosystems. However, large uncertainties in present phenology models make accurate assessment of the beginning of the growing season (BGS) a challenge. In this study, based on the satellite-based phenology product (i.e. the V005 MODIS Land Cover Dynamics (MCD12Q2) product), we calibrated four phenology models, compared their relative strength to predict vegetation phenology; and assessed the spatial pattern and interannual variability of BGS in the Northern Hemisphere. The results indicated that parameter calibration significantly influences the models' accuracy. All models showed good performance in cool regions but poor performance in warm regions. On average, they explained about 67% (the Growing Degree Day model), 79% (the Biome-BGC phenology model), 73% (the Number of Growing Days model) and 68% (the Number of Chilling Days-Growing Degree Day model) of the BGS variations over the Northern Hemisphere. There were substantial differences in BGS simulations among the four phenology models. Overall, the Biome-BGC phenology model performed best in predicting the BGS, and showed low biases in most boreal and cool regions. Compared with the other three models, the two-phase phenology model (NCD-GDD) showed the lowest correlation and largest biases with the MODIS phenology product, although it could catch the interannual variations well for some vegetation types. Our study highlights the need for further improvements by integrating the effects of water availability, especially for plants growing in low latitudes, and the physiological adaptation of plants into phenology models. PMID:25279567

COLT: seasonal prediction of crop irrigation needs

NASA Astrophysics Data System (ADS)

Villani, Giulia; Spisni, Andrea; Mariani, Maria Cristina; Pratizzoli, William; Pavan, Valentina; Tomei, Fausto; Botarelli, Lucio; Marletto, Vittorio

2013-04-01

from the ARPA-SIMC web site. Since 2010 forecasts of the crops water irrigation requirements have been computed and compared with the simulated data at the end of the summer with good results. The COLT scheme is able to predict the very large interannual variability of the seasonal crop water needs: in 2010 the summer was rather wet and COLT predicted about 500 Mm3, while in 2011 the median forecast was 850 Mm3, a value considered as normal. The summer of 2012 was exceptionally dry, thus the median COLT forecast was 1077 Mm3, while the value computed with observed summer data reached 1340 Mm3 (+24%). The COLT scheme was also tested in a study area located near Ravenna (570 ha), where actual crop irrigation volumes are measured. The median forecasted irrigation (0.50 Mm3) resulted 14% higher than the observed value for 2011 (0.44 Mm3), mainly due to errors in classification of non irrigated crops as irrigated, and possibly to the water table not being accounted for in the model. COLT looks like a promising approach for assessing, planning and managing water resources in agriculture, and for mitigating the impacts of intense climate anomalies in the agricultural sector.

Seasonal and interannual variability of surface CDOM in the South China Sea associated with El Niño

NASA Astrophysics Data System (ADS)

Ma, Jinfeng; Zhan, Haigang; Du, Yan

2011-04-01

Satellite imagery of SeaWiFS from October 1997 to November 2007 is used to investigate the dominant seasonal and interannual variations of the surface light absorption due to Colored Dissolved Organic Materials (CDOM) in the South China Sea (SCS). Results show that the spatial distribution of CDOM mimics the major features of the SCS basin-scale circulation. High values of CDOM are found in upwelling regions like southeast of Vietnam in summer and northwest of Luzon in winter. At a basin scale, CDOM is high in winter when upwelling is strong, solar shortwave radiation and stratification weak, and vertical mixing intense. Opposite conditions exist in spring and summer. Interannual variability of the basin-wide CDOM is characterized by abnormal troughs during the El Niño events. A strong relationship exists between the time series of the first EOF mode (for both winter and summer) and Niño 3.4 Index. Associations of these events with climatic and hydrographic properties (i.e. wind forcing, solar shortwave radiation, Ekman pumping, vertical mixing, sea surface height and temperature) are discussed.

Evaluation of Multi-Model Ensemble System for Seasonal and Monthly Prediction

NASA Astrophysics Data System (ADS)

Zhang, Q.; Van den Dool, H. M.

2013-12-01

Since August 2011, the realtime seasonal forecasts of U.S. National Multi-Model Ensemble (NMME) have been made on 8th of each month by NCEP Climate Prediction Center (CPC). During the first year, the participating models were NCEP/CFSv1&2, GFDL/CM2.2, NCAR/U.Miami/COLA/CCSM3, NASA/GEOS5, IRI/ ECHAM-a & ECHAM-f for the realtime NMME forecast. The Canadian Meteorological Center CanCM3 and CM4 replaced the CFSv1 and IRI's models in the second year. The NMME team at CPC collects three variables, including precipitation, 2-meter temperature and sea surface temperature from each modeling center on a 1x1 global grid, removes systematic errors, makes the grand ensemble mean with equal weight for each model and constructs a probability forecast with equal weight for each member. The team then provides the NMME forecast to the operational CPC forecaster responsible for the seasonal and monthly outlook each month. Verification of the seasonal and monthly prediction from NMME is conducted by calculating the anomaly correlation (AC) from the 30-year hindcasts (1982-2011) of individual model and NMME ensemble. The motivation of this study is to provide skill benchmarks for future improvements of the NMME seasonal and monthly prediction system. The experimental (Phase I) stage of the project already supplies routine guidance to users of the NMME forecasts.

A Comparison of Seasonal and Interannual Variability of Soil Dust Aerosols Over the Atlantic Ocean as Inferred by the Toms AI and AVHRR AOT Retrievals

NASA Technical Reports Server (NTRS)

Cakmur, R. V.; Miller, R. L.; Tegen, Ina; Hansen, James E. (Technical Monitor)

2001-01-01

The seasonal cycle and interannual variability of two estimates of soil (or 'mineral') dust aerosols are compared: Advanced Very High Resolution Radiometer (AVHRR) aerosol optical thickness (AOT) and Total Ozone Mapping Spectrometer (TOMS) aerosol index (AI), Both data sets, comprising more than a decade of global, daily images, are commonly used to evaluate aerosol transport models. The present comparison is based upon monthly averages, constructed from daily images of each data set for the period between 1984 and 1990, a period that excludes contamination from volcanic eruptions. The comparison focuses upon the Northern Hemisphere subtropical Atlantic Ocean, where soil dust aerosols make the largest contribution to the aerosol load, and are assumed to dominate the variability of each data set. While each retrieval is sensitive to a different aerosol radiative property - absorption for the TOMS AI versus reflectance for the AVHRR AOT - the seasonal cycles of dust loading implied by each retrieval are consistent, if seasonal variations in the height of the aerosol layer are taken into account when interpreting the TOMS AI. On interannual time scales, the correlation is low at most locations. It is suggested that the poor interannual correlation is at least partly a consequence of data availability. When the monthly averages are constructed using only days common to both data sets, the correlation is substantially increased: this consistency suggests that both TOMS and AVHRR accurately measure the aerosol load in any given scene. However, the two retrievals have only a few days in common per month so that these restricted monthly averages have a large uncertainty. Calculations suggest that at least 7 to 10 daily images are needed to estimate reliably the average dust load during any particular month, a threshold that is rarely satisfied by the AVHRR AOT due to the presence of clouds in the domain. By rebinning each data set onto a coarser grid, the availability of

Interannual growth dynamics of vegetation in the Kuparuk River watershed, Alaska based on the Normalized Difference Vegetation Index

USGS Publications Warehouse

Hope, A.S.; Boynton, W.L.; Stow, D.A.; Douglas, David C.

2003-01-01

Interannual above-ground production patterns are characterized for three tundra ecosystems in the Kuparuk River watershed of Alaska using NOAA-AVHRR Normalized Difference Vegetation Index (NDVI) data. NDVI values integrated over each growing season (SINDVI) were used to represent seasonal production patterns between 1989 and 1996. Spatial differences in ecosystem production were expected to follow north-south climatic and soil gradients, while interannual differences in production were expected to vary with variations in seasonal precipitation and temperature. It was hypothesized that the increased vegetation growth in high latitudes between 1981 and 1991 previously reported would continue through the period of investigation for the study watershed. Zonal differences in vegetation production were confirmed but interannual variations did not covary with seasonal precipitation or temperature totals. A sharp reduction in the SINDVI in 1992 followed by a consistent increase up to 1996 led to a further hypothesis that the interannual variations in SINDVI were associated with variations in stratospheric optical depth. Using published stratospheric optical depth values derived from the SAGE and SAGE-II satellites, it is demonstrated that variations in these depths are likely the primary cause of SINDVI interannual variability.

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

NASA Astrophysics Data System (ADS)

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

2005-11-01

This work examines the feasibility of weather and seasonal climate predictions for South America using the multi-model synthetic superensemble approach for climate, and the multi-model conventional superensemble approach for numerical weather prediction, both developed at Florida State University (FSU). The effect on seasonal climate forecasts of the number of models used in the synthetic superensemble is investigated. It is shown that the synthetic superensemble approach for climate and the conventional superensemble approach for numerical weather prediction can reduce the errors over South America in seasonal climate prediction and numerical weather prediction.For climate prediction, a suite of 13 models is used. The forecast lead-time is 1 month for the climate forecasts, which consist of precipitation and surface temperature forecasts. The multi-model ensemble is comprised of four versions of the FSU-Coupled Ocean-Atmosphere Model, seven models from the Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction (DEMETER), a version of the Community Climate Model (CCM3), and a version of the predictive Ocean Atmosphere Model for Australia (POAMA). The results show that conditions over South America are appropriately simulated by the Florida State University Synthetic Superensemble (FSUSSE) in comparison to observations and that the skill of this approach increases with the use of additional models in the ensemble. When compared to observations, the forecasts are generally better than those from both a single climate model and the multi-model ensemble mean, for the variables tested in this study.For numerical weather prediction, the conventional Florida State University Superensemble (FSUSE) is used to predict the mass and motion fields over South America. Predictions of mean sea level pressure, 500 hPa geopotential height, and 850 hPa wind are made with a multi-model superensemble comprised of six global models for the period

Precipitation event tracking reveals that precipitation characteristics respond differently under seasonal, interannual, and anthropogenic forcing

NASA Astrophysics Data System (ADS)

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

2017-12-01

Change in precipitation characteristics is an especially concerning potential impact of climate change, and both model and observational studies suggest that increases in precipitation intensity are likely. However, studies to date have focused on mean accumulated precipitation rather than on the characteristics of individual events. We report here on a study using a novel rainstorm identification tracking algorithm (Chang et al. 2016) that allows evaluating changes in spatio-temporal characteristics of events. We analyze high-resolution precipitation from dynamically downscaled regional climate simulations over the continental U.S. (WRF driven by CCSM4) of present and future climate conditions. We show that precipitation events show distinct characteristic changes for natural seasonal and interannual variations and for anthropogenic greenhouse-gas forcing. In all cases, wetter seasons/years/future climate states are associated with increased precipitation intensity, but other precipitation characteristics respond differently to the different drivers. For example, under anthropogenic forcing, future wetter climate states involve smaller individual event sizes (partially offsetting their increased intensity). Under natural variability, however, wetter years involve larger mean event sizes. Event identification and tracking algorithms thus allow distinguishing drivers of different types of precipitation changes, and in relating those changes to large-scale processes.

Accessing, Utilizing and Visualizing NASA Remote Sensing Data for Malaria Modeling and Surveillance

NASA Technical Reports Server (NTRS)

Kiang, Richard K.; Adimi, Farida; Kempler, Steven

2007-01-01

This poster presentation reviews the use of NASA remote sensing data that can be used to extract environmental information for modeling malaria transmission. The authors discuss the remote sensing data from Landsat, Advanced Very High Resolution Radiometer (AVHRR), Moderate Resolution Imaging Spectroradiometer (MODIS), Tropical Rainfall Measuring Mission (TRMM), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Earth Observing One (EO-1), Advanced Land Imager (ALI) and Seasonal to Interannual Earth Science Information Partner (SIESIP) dataset.

Predicting Fire Season Severity in South America Using Sea Surface Temperature Anomalies

NASA Technical Reports Server (NTRS)

Chen, Yang; Randerson, James T.; Morton, Douglas C.; Jin, Yufang; DeFries, Ruth S.; Collatz, George J.; Kasibhatla, Prasad S.; Giglio, Louis; Jin, Yufang; Marlier, Miriam

2011-01-01

Fires in South America cause forest degradation and contribute to carbon emissions associated with land use change. Here we investigated the relationship between year-to-year changes in satellite-derived estimates of fire activity in South America and sea surface temperature (SST) anomalies. We found that the Oceanic Ni o Index (ONI) was correlated with interannual fire activity in the eastern Amazon whereas the Atlantic Multidecadal Oscillation (AMO) index was more closely linked with fires in the southern and southwestern Amazon. Combining these two climate indices, we developed an empirical model that predicted regional annual fire season severity (FSS) with 3-5 month lead times. Our approach provides the foundation for an early warning system for forecasting the vulnerability of Amazon forests to fires, thus enabling more effective management with benefits for mitigation of greenhouse gas and air pollutant emissions.

Short-term favorable weather conditions are an important control of interannual variability in carbon and water fluxes

Treesearch

Jakob Zscheischler; Simone Fatichi; Sebastian Wolf; Peter D. Blanken; Gil Bohrer; Ken Clark; Ankur R. Desai; David Hollinger; Trevor Keenan; Kimberly A. Novick; Sonia I. Seneviratne

2016-01-01

Ecosystem models often perform poorly in reproducing interannual variability in carbon and water fluxes, resulting in considerable uncertainty when estimating the land-carbon sink. While many aggregated variables (growing season length, seasonal precipitation, or temperature) have been suggested as predictors for interannual variability in carbon fluxes, their...

Seasonal and Interannual Variation of Currents and Water Properties off the Mid-East Coast of Korea

NASA Astrophysics Data System (ADS)

Park, J. H.; Chang, K. I.; Nam, S.

2016-02-01

Since 1999, physical parameters such as current, temperature, and salinity off the mid-east coast of Korea have been continuously observed from the long-term buoy station called `East-Sea Real-time Ocean monitoring Buoy (ESROB)'. Applying harmonic analysis to 6-year-long (2007-2012) depth-averaged current data from the ESROB, a mean seasonal cycle of alongshore currents, characterized by poleward current in average and equatorward current in summer, is extracted which accounts for 5.8% of the variance of 40 hours low-pass filtered currents. In spite of the small variance explained, a robust seasonality of summertime equatorward reversal typifies the low-passed alongshore currents along with low-density water. To reveal the dynamics underlying the seasonal variation, each term of linearized, depth-averaged momentum equations is estimated using the data from ESROB, adjacent tide gauge stations, and serial hydrographic stations. The result indicates that the reversal of alongshore pressure gradient is a major driver of the equatorward reversals in summer. The reanalysis wind product (MERRA) and satellite altimeter-derived sea surface height (AVISO) data show correlated features between positive (negative) wind stress curl and sea surface depression (uplift). Quantitative estimates reveal that the wind-stress curl accounts for 42% of alongshore sea level variation. Summertime low-density water originating from the northern coastal region is a footprint of the buoyancy-driven equatorward current. An interannual variation (anomalies from the mean seasonal cycle) of alongshore currents and its possible driving mechanisms will be discussed.

Understanding the predictability of seasonal precipitation over northeast Brazil

NASA Astrophysics Data System (ADS)

Misra, Vasubandhu

2006-05-01

Using multiple long-term simulations of the Center for Ocean-Land-Atmosphere Studies (COLA) atmospheric general circulation model (AGCM) forced with observed sea surface temperature (SST), it is shown that the model has high skill in simulating the February-March-April (FMA) rainy season over northeast Brazil (Nordeste). Separate sensitivity experiments conducted with the same model that entails suppression of all variability except for the climatological annual cycle in SST over the Pacific and Atlantic Oceans reveal that this skill over Nordeste is sensitive to SST anomalies in the tropical Atlantic Ocean. However, the spatial pattern of SST anomalies in the tropical Atlantic Ocean that correlate with FMA Nordeste rainfall are in fact a manifestation of El Niño Southern Oscillation (ENSO) phenomenon in the Pacific Ocean. This study also analyzes the failure of the COLA AGCM in capturing the correct FMA precipitation anomalies over Nordeste in several years of the simulation. It is found that this failure occurs when the SST anomalies over the northern tropical Atlantic Ocean are large and not significantly correlated with contemporaneous SST anomalies over the eastern Pacific Ocean. In two of the relatively large ENSO years when the model failed to capture the correct signal of the interannual variability of precipitation over Nordeste, it was found that the meridional gradient of SST anomalies over the tropical Atlantic Ocean was inconsistent with the canonical development of ENSO. The analysis of the probabilistic skill of the model revealed that it has more skill in predicting flood years than drought. Furthermore, the model has no skill in predicting normal seasons. These model features are consistent with the model systematic errors.

Evaluating Antarctic sea ice predictability at seasonal to interannual timescales in global climate models

NASA Astrophysics Data System (ADS)

Marchi, Sylvain; Fichefet, Thierry; Goosse, Hugues; Zunz, Violette; Tietsche, Steffen; Day, Jonny; Hawkins, Ed

2016-04-01

Unlike the rapid sea ice losses reported in the Arctic, satellite observations show an overall increase in Antarctic sea ice extent over recent decades. Although many processes have already been suggested to explain this positive trend, it remains the subject of current investigations. Understanding the evolution of the Antarctic sea ice turns out to be more complicated than for the Arctic for two reasons: the lack of observations and the well-known biases of climate models in the Southern Ocean. Irrespective of those issues, another one is to determine whether the positive trend in sea ice extent would have been predictable if adequate observations and models were available some decades ago. This study of Antarctic sea ice predictability is carried out using 6 global climate models (HadGEM1.2, MPI-ESM-LR, GFDL CM3, EC-Earth V2, MIROC 5.2 and ECHAM 6-FESOM) which are all part of the APPOSITE project. These models are used to perform hindcast simulations in a perfect model approach. The predictive skill is estimated thanks to the PPP (Potential Prognostic Predictability) and the ACC (Anomaly Correlation Coefficient). The former is a measure of the uncertainty of the ensemble while the latter assesses the accuracy of the prediction. These two indicators are applied to different variables related to sea ice, in particular the total sea ice extent and the ice edge location. This first model intercomparison study about sea ice predictability in the Southern Ocean aims at giving a general overview of Antarctic sea ice predictability in current global climate models.

Seasonal and interannual changes in zooplankton community in the coastal zone of the North-Eastern Black Sea.

NASA Astrophysics Data System (ADS)

Nikishina, A. B.; Arashkevich, E. G.; Louppova, N. E.; Soloviev, K. A.

2009-04-01

The phenological response of zooplankton community is a result of simultaneous effect of several factors: feeding conditions, predation abundance, periods of reproduction of common species and hydrodynamic regime. The Black sea ecosystem is one of the best studied in the world, otherwise there is still some illegibility about ecosystem functioning and especially about environmental factors influence on zooplankton dynamics. For the last twenty years pelagic system of the Black Sea has changed dramatically. The invasion of ctenophore Mnemiopsis leidyi in the middle of eighties caused significant decrease in zooplankton biomass. It also altered plankton structure and shifted periods of mass reproduction of the abundant species and biomass maximums. For instance, before the invasion of Mnemiopsis the maximum of zooplankton biomass was observed in autumn (data by A. Pasternak, 1983), and after that the maximum moved to the spring (data by V.S. Khoroshilov, 1999). The incursion of ctenophore Beroe ovata feeding on Mnemiopsis in the nineties has led to the enhancement of zooplankton community. Although the detailed analysis of seasonal zooplankton dynamics wasn't performed in the recent years. The object of our research was to study seasonal and interannual changes in zooplankton community in the coastal area of the North-Eastern Black Sea. Analysis of interannual, seasonal and spatial changes in zooplankton distribution, abundance and species composition along with age structure of dominant populations were performed based on investigations during 2005-2008 years in the North-Eastern Black Sea. Plankton samples were obtained monthly since June 2005 till December 2008. Plankton was collected at three stations at depths 25m, 50m and 500-1000m along the transect from the Blue Bay to the open sea. Sampling of gelatinous animals was conducted in parallel to the zooplankton sampling. Simultaneously with plankton sampling CTD data were obtained. The feeding conditions were

Wood phenology, not carbon input, controls the interannual variability of wood growth in a temperate oak forest.

PubMed

Delpierre, Nicolas; Berveiller, Daniel; Granda, Elena; Dufrêne, Eric

2016-04-01

Although the analysis of flux data has increased our understanding of the interannual variability of carbon inputs into forest ecosystems, we still know little about the determinants of wood growth. Here, we aimed to identify which drivers control the interannual variability of wood growth in a mesic temperate deciduous forest. We analysed a 9-yr time series of carbon fluxes and aboveground wood growth (AWG), reconstructed at a weekly time-scale through the combination of dendrometer and wood density data. Carbon inputs and AWG anomalies appeared to be uncorrelated from the seasonal to interannual scales. More than 90% of the interannual variability of AWG was explained by a combination of the growth intensity during a first 'critical period' of the wood growing season, occurring close to the seasonal maximum, and the timing of the first summer growth halt. Both atmospheric and soil water stress exerted a strong control on the interannual variability of AWG at the study site, despite its mesic conditions, whilst not affecting carbon inputs. Carbon sink activity, not carbon inputs, determined the interannual variations in wood growth at the study site. Our results provide a functional understanding of the dependence of radial growth on precipitation observed in dendrological studies. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Climate services for health: predicting the evolution of the 2016 dengue season in Machala, Ecuador.

PubMed

Lowe, Rachel; Stewart-Ibarra, Anna M; Petrova, Desislava; García-Díez, Markel; Borbor-Cordova, Mercy J; Mejía, Raúl; Regato, Mary; Rodó, Xavier

2017-07-01

El Niño and its effect on local meteorological conditions potentially influences interannual variability in dengue transmission in southern coastal Ecuador. El Oro province is a key dengue surveillance site, due to the high burden of dengue, seasonal transmission, co-circulation of all four dengue serotypes, and the recent introduction of chikungunya and Zika. In this study, we used climate forecasts to predict the evolution of the 2016 dengue season in the city of Machala, following one of the strongest El Niño events on record. We incorporated precipitation, minimum temperature, and Niño3·4 index forecasts in a Bayesian hierarchical mixed model to predict dengue incidence. The model was initiated on Jan 1, 2016, producing monthly dengue forecasts until November, 2016. We accounted for misreporting of dengue due to the introduction of chikungunya in 2015, by using active surveillance data to correct reported dengue case data from passive surveillance records. We then evaluated the forecast retrospectively with available epidemiological information. The predictions correctly forecast an early peak in dengue incidence in March, 2016, with a 90% chance of exceeding the mean dengue incidence for the previous 5 years. Accounting for the proportion of chikungunya cases that had been incorrectly recorded as dengue in 2015 improved the prediction of the magnitude of dengue incidence in 2016. This dengue prediction framework, which uses seasonal climate and El Niño forecasts, allows a prediction to be made at the start of the year for the entire dengue season. Combining active surveillance data with routine dengue reports improved not only model fit and performance, but also the accuracy of benchmark estimates based on historical seasonal averages. This study advances the state-of-the-art of climate services for the health sector, by showing the potential value of incorporating climate information in the public health decision-making process in Ecuador. European Union

Asian summer monsoon seasonal prediction skill in the Met Office GloSea5 model and its dependence on mean state biases

NASA Astrophysics Data System (ADS)

Bush, Stephanie; Turner, Andrew; Martin, Gill; Woolnough, Steve

2015-04-01

Predicting the circulation and precipitation features of the Asian monsoon on time scales of weeks to the season ahead remains a challenge for prediction centres. Current state-of-the-art models retain large biases, particularly dryness over India, which evolve rapidly from initialization and persist into centennial length climate integrations, illustrating the seamless nature of the monsoon problem. We present initial results from our Ministry of Earth Sciences Indian Monsoon Mission collaboration project to assess and improve weekly-to-seasonal forecasts in the Met Office Unified Model (MetUM) coupled initialized Global Seasonal Prediction System (GloSea5). Using a 14-year hindcast ensemble of integrations in which atmosphere, ocean and sea-ice components are initialized from May start dates, we assess the monsoon seasonal prediction skill and global mean state biases of GloSea5. Initial May and June biases include a lack of precipitation over the Indian peninsula, and a weakened monsoon flow, and these give way to a more robust pattern of excess precipitation in the western north Pacific, lack of precipitation over the Maritime Continent, excess westerlies across the Indian peninsula and Indochina, and cool SSTs in the eastern equatorial Indian Ocean and western north Pacific in July and August. Despite these mean state biases, the interannual correlation of predicted JJA all India rainfall from 1998 to 2009 with TRMM is fairly high at 0.68. Future work will focus on the prospects for further improving this skill with bias correction techniques.

Analysis of smoke and cloud impact on seasonal and interannual variations in normalized difference vegetation index in Amazon

NASA Astrophysics Data System (ADS)

Kobayashi, H.; Dye, D. G.

2004-12-01

Normalized difference vegetation index (NDVI) derived from National Oceanic and Atmospheric Administration (NOAA)/Advanced Very High Resolution Radiometer (AVHRR) is a unique measurement of long-term variations in global vegetation dynamics. The NDVI data have been used for the detection of the seasonal and interannual variations in vegetation. However, as reported in several studies, NDVI decreases with the increase in clouds and/or smoke aerosol contaminated in the pixels. This study assesses the smoke and clouds effect on long-term Global Inventory Modeling and Mapping Studies (GIMMS) and Pathfinder AVHRR Land (PAL) NDVI data in Amazon. This knowledge will help developing the correction method in the tropics in the future. To assess the smoke and cloud effects on GIMMS and PAL, we used another satellite-derived data sets; NDVI derived from SPOT/VEGETATION (VGT) data and Aerosol Index (AI) derived from Total Ozone Mapping Spectrometer (TOMS). Since April 1998, VGT has measured the earth surface globally including in Amazon. The advantage of the VGT is that it has blue channel where the smoke and cloud can be easily detected. By analyzing the VGT NDVI and comparing with the AVHRR-based NDVI, we inferred smoke and cloud effect on the AVHRR-based NDVI. From the results of the VGT analysis, we found the large NDVI seasonality in South and Southeastern Amazon. In these areas, the NDVI gradually increased from April to July and decreased from August to October. However the sufficient NDVI data were not existed from August to November when the smoke and cloud pixels were masked using blue reflectance. Thus it is said that the smoke and clouds mainly cause the large decreases in NDVI between August and November and NDVI has little vegetation signature in these months. Also we examined the interannual variations in NDVI and smoke aerosol. Then the decrease in NDVI is well consistent with the increase in the increase in AI. Our results suggest that the months between April

Seasonal Phosphorus Sources and Loads to Upper Klamath Lake, Oregon, as Determined by a Dynamic SPARROW Model

NASA Astrophysics Data System (ADS)

Saleh, D.; Domagalski, J. L.; Smith, R. A.

2016-12-01

The SPARROW (SPAtially-Referenced Regression On Watershed Attributes) model, developed by the U.S. Geological Survey, has been used to identify and quantify the sources of nitrogen and phosphorus in watersheds and to predict their fluxes and concentration at specified locations downstream. Existing SPARROW models use a hybrid statistical approach to describe an annual average ("steady-state") relationship between sources and stream conditions based on long-term water quality monitoring data and spatially-referenced explanatory information. Although these annual models are useful for some management purposes, many water quality issues stem from intra- and inter-annual changes in constituent sources, hydrologic forcing, or other environmental conditions, which cause a lag between watershed inputs and stream water quality. We are developing a seasonal dynamic SPARROW model of sources, fluxes, and yields of phosphorus for the watershed (approximately 9,700 square kilometers) draining to Upper Klamath Lake, Oregon. The lake is hyper-eutrophic and various options are being considered for water quality improvement. The model was calibrated with 11 years of water quality data (2000 to 2010) and simulates seasonal loads and yields for a total of 44 seasons. Phosphorus sources to the watershed include animal manure, farm fertilizer, discharges of treated wastewater, and natural sources (soil and streambed sediment). The model predicts that phosphorus delivery to the lake is strongly affected by intra- and inter-annual changes in precipitation and by temporary seasonal storage of phosphorus in the watershed. The model can be used to predict how different management actions for mitigating phosphorus sources might affect phosphorus loading to the lake as well as the time required for any changes in loading to occur following implementation of the action.

Potential for western US seasonal snowpack prediction

USGS Publications Warehouse

Kapnick, Sarah B.; Yang, Xiaosong; Vecchi, Gabriel A.; Delworth, Thomas L.; Gudgel, Rich; Malyshev, Sergey; Milly, Paul C. D.; Shevliakova, Elena; Underwood, Seth; Margulis, Steven A.

2018-01-01

Western US snowpack—snow that accumulates on the ground in the mountains—plays a critical role in regional hydroclimate and water supply, with 80% of snowmelt runoff being used for agriculture. While climate projections provide estimates of snowpack loss by the end of th ecentury and weather forecasts provide predictions of weather conditions out to 2 weeks, less progress has been made for snow predictions at seasonal timescales (months to 2 years), crucial for regional agricultural decisions (e.g., plant choice and quantity). Seasonal predictions with climate models first took the form of El Niño predictions 3 decades ago, with hydroclimate predictions emerging more recently. While the field has been focused on single-season predictions (3 months or less), we are now poised to advance our predictions beyond this timeframe. Utilizing observations, climate indices, and a suite of global climate models, we demonstrate the feasibility of seasonal snowpack predictions and quantify the limits of predictive skill 8 month sin advance. This physically based dynamic system outperforms observation-based statistical predictions made on July 1 for March snowpack everywhere except the southern Sierra Nevada, a region where prediction skill is nonexistent for every predictor presently tested. Additionally, in the absence of externally forced negative trends in snowpack, narrow maritime mountain ranges with high hydroclimate variability pose a challenge for seasonal prediction in our present system; natural snowpack variability may inherently be unpredictable at this timescale. This work highlights present prediction system successes and gives cause for optimism for developing seasonal predictions for societal needs.

Error Estimation of An Ensemble Statistical Seasonal Precipitation Prediction Model

NASA Technical Reports Server (NTRS)

Shen, Samuel S. P.; Lau, William K. M.; Kim, Kyu-Myong; Li, Gui-Long

2001-01-01

This NASA Technical Memorandum describes an optimal ensemble canonical correlation forecasting model for seasonal precipitation. Each individual forecast is based on the canonical correlation analysis (CCA) in the spectral spaces whose bases are empirical orthogonal functions (EOF). The optimal weights in the ensemble forecasting crucially depend on the mean square error of each individual forecast. An estimate of the mean square error of a CCA prediction is made also using the spectral method. The error is decomposed onto EOFs of the predictand and decreases linearly according to the correlation between the predictor and predictand. Since new CCA scheme is derived for continuous fields of predictor and predictand, an area-factor is automatically included. Thus our model is an improvement of the spectral CCA scheme of Barnett and Preisendorfer. The improvements include (1) the use of area-factor, (2) the estimation of prediction error, and (3) the optimal ensemble of multiple forecasts. The new CCA model is applied to the seasonal forecasting of the United States (US) precipitation field. The predictor is the sea surface temperature (SST). The US Climate Prediction Center's reconstructed SST is used as the predictor's historical data. The US National Center for Environmental Prediction's optimally interpolated precipitation (1951-2000) is used as the predictand's historical data. Our forecast experiments show that the new ensemble canonical correlation scheme renders a reasonable forecasting skill. For example, when using September-October-November SST to predict the next season December-January-February precipitation, the spatial pattern correlation between the observed and predicted are positive in 46 years among the 50 years of experiments. The positive correlations are close to or greater than 0.4 in 29 years, which indicates excellent performance of the forecasting model. The forecasting skill can be further enhanced when several predictors are used.

Exploiting the atmosphere's memory for monthly, seasonal and interannual temperature forecasting using Scaling LInear Macroweather Model (SLIMM)

NASA Astrophysics Data System (ADS)

Del Rio Amador, Lenin; Lovejoy, Shaun

2016-04-01

. The corresponding space-time model (the ScaLIng Macroweather Model (SLIMM) is thus only multifractal in space where the spatial intermittency is associated with different climate zones. SLIMM exploits the power law (scaling) behavior in time of the temperature field and uses the long historical memory of the temperature series to improve the skill. The only model parameter is the fluctuation scaling exponent, H (usually in the range -0.5 - 0), which is directly related to the skill and can be obtained from the data. The results predicted analytically by the model have been tested by performing actual hindcasts in different 5° x 5° regions covering the planet using ERA-Interim, 20CRv2 and NCEP/NCAR reanalysis as reference datasets. We report maps of theoretical skill predicted by the model and we compare it with actual skill based on hindcasts for monthly, seasonal and annual resolutions. We also present maps of calibrated probability hindcasts with their respective validations. Comparisons between our results using SLIMM, some other stochastic autoregressive model, and hindcasts from the Canadian Seasonal to Interannual Prediction System (CanSIPS) and the National Centers for Environmental Prediction (NCEP)'s model CFSv2, are also shown. For seasonal temperature forecasts, SLIMM outperforms the GCM based forecasts in over 90% of the earth's surface. SLIMM forecasts can be accessed online through the site: http://www.to_be_announced.mcgill.ca.

Remotely Sensed Northern Vegetation Response to Changing Climate: Growing Season and Productivity Perspective

NASA Technical Reports Server (NTRS)

Ganguly, S.; Park, Taejin; Choi, Sungho; Bi, Jian; Knyazikhin, Yuri; Myneni, Ranga

2016-01-01

Vegetation growing season and maximum photosynthetic state determine spatiotemporal variability of seasonal total gross primary productivity of vegetation. Recent warming induced impacts accelerate shifts on growing season and physiological status over Northern vegetated land. Thus, understanding and quantifying these changes are very important. Here, we first investigate how vegetation growing season and maximum photosynthesis state are evolved and how such components contribute on inter-annual variation of seasonal total gross primary productivity. Furthermore, seasonally different response of northern vegetation to changing temperature and water availability is also investigated. We utilized both long-term remotely sensed data to extract larger scale growing season metrics (growing season start, end and duration) and productivity (i.e., growing season summed vegetation index, GSSVI) for answering these questions. We find that regionally diverged growing season shift and maximum photosynthetic state contribute differently characterized productivity inter-annual variability and trend. Also seasonally different response of vegetation gives different view of spatially varying interaction between vegetation and climate. These results highlight spatially and temporally varying vegetation dynamics and are reflective of biome-specific responses of northern vegetation to changing climate.

Enhancement of seasonal prediction of East Asian summer rainfall related to western tropical Pacific convection

NASA Astrophysics Data System (ADS)

Lee, Doo Young; Ahn, Joong-Bae; Yoo, Jin-Ho

2015-08-01

The prediction skills of climate model simulations in the western tropical Pacific (WTP) and East Asian region are assessed using the retrospective forecasts of seven state-of-the-art coupled models and their multi-model ensemble (MME) for boreal summers (June-August) during the period 1983-2005, along with corresponding observed and reanalyzed data. The prediction of summer rainfall anomalies in East Asia is difficult, while the WTP has a strong correlation between model prediction and observation. We focus on developing a new approach to further enhance the seasonal prediction skill for summer rainfall in East Asia and investigate the influence of convective activity in the WTP on East Asian summer rainfall. By analyzing the characteristics of the WTP convection, two distinct patterns associated with El Niño-Southern Oscillation developing and decaying modes are identified. Based on the multiple linear regression method, the East Asia Rainfall Index (EARI) is developed by using the interannual variability of the normalized Maritime continent-WTP Indices (MPIs), as potentially useful predictors for rainfall prediction over East Asia, obtained from the above two main patterns. For East Asian summer rainfall, the EARI has superior performance to the East Asia summer monsoon index or each MPI. Therefore, the regressed rainfall from EARI also shows a strong relationship with the observed East Asian summer rainfall pattern. In addition, we evaluate the prediction skill of the East Asia reconstructed rainfall obtained by hybrid dynamical-statistical approach using the cross-validated EARI from the individual models and their MME. The results show that the rainfalls reconstructed from simulations capture the general features of observed precipitation in East Asia quite well. This study convincingly demonstrates that rainfall prediction skill is considerably improved by using a hybrid dynamical-statistical approach compared to the dynamical forecast alone.

Seasonal scale water deficit forecasting in Africa and the Middle East using NASA's Land Information System (LIS)

NASA Astrophysics Data System (ADS)

Peters-Lidard, C. D.; Arsenault, K. R.; Shukla, S.; Getirana, A.; McNally, A.; Koster, R. D.; Zaitchik, B. F.; Badr, H. S.; Roningen, J. M.; Kumar, S.; Funk, C. C.

2017-12-01

A seamless and effective water deficit monitoring and early warning system is critical for assessing food security in Africa and the Middle East. In this presentation, we report on the ongoing development and validation of a seasonal scale water deficit forecasting system based on NASA's Land Information System (LIS) and seasonal climate forecasts. First, our presentation will focus on the implementation and validation of drought and water availability monitoring products in the region. Next, it will focus on evaluating drought and water availability forecasts. Finally, details will be provided of our ongoing collaboration with end-user partners in the region (e.g., USAID's Famine Early Warning Systems Network, FEWS NET), on formulating meaningful early warning indicators, effective communication and seamless dissemination of the products through NASA's web-services. The water deficit forecasting system thus far incorporates NASA GMAO's Catchment and the Noah Multi-Physics (MP) LSMs. In addition, the LSMs' surface and subsurface runoff are routed through the Hydrological Modeling and Analysis Platform (HyMAP) to simulate surface water dynamics. To establish a climatology from 1981-2015, the two LSMs are driven by NASA/GMAO's Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2), and the USGS and UCSB Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) daily rainfall dataset. Comparison of the models' energy and hydrological budgets with independent observations suggests that major droughts are well-reflected in the climatology. The system uses seasonal climate forecasts from NASA's GEOS-5 (the Goddard Earth Observing System Model-5) and NCEP's Climate Forecast System-2, and it produces forecasts of soil moisture, ET and streamflow out to 6 months in the future. Forecasts of those variables are formulated in terms of indicators to provide forecasts of drought and water availability in the region. Current work suggests

On the predictability of the interannual behaviour of the Madden-Julian oscillation and its relationship with El Nino

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

Sperber, K.R., LLNL

The Madden-Julian Oscillation (MJO) is the dominant mode of tropical variability at intraseasonal timescales. It displays substantial interannual variability in intensity which may have important implications for the predictability of the coupled system. The reasons for this interannual variability are not understood. The aim of this paper is to investigate whether the interannual behavior of the MJO is related to tropical sea surface temperature (SST) anomalies, particularly El Nino, and hence whether it is predictable. The interannual behavior of the MJO has been diagnosed initially in the 40-year NCEP/ NCAR Reanalysis. The results suggest that prior to the mid-1970s themore » activity of the MJO was consistently lower than during the latter part of the record. This may be related to either inadequacies in the data coverage, particularly over the tropical Indian Ocean prior to the introduction of satellite observations, or to the real effects of a decadal timescale warming in the tropical SSTs. The teleconnection patterns between interannual variations in MJO activity and SST show only a weak, barely significant, influence of El Nino in which the MJO is more active during the cold phase. As well as the NCEP/NCAR Reanalysis, a 4-member ensemble of 45 year integrations with the Hadley Centre climate model (HadAM2a), forced by observed SSTs for 1949-93, has been used to investigate the relationship between MJO activity and SST. HadAM2a is known to give a reasonable simulation of the MJO and the extended record provided by this ensemble of integrations allows a more robust investigation of the predictability of MJO activity than was possible with the 40-year NCEP/NCAR Reanalysis. The results have shown that, for the uncoupled system, with the atmosphere being driven by imposed SSTS, there is no reproducibility for the activity of the MJO from year to year. The interannual behavior of the MJO is not controlled by the phase of El Nino and would appear to be chaotic

Climate extremes in the Pacific: improving seasonal prediction of tropical cyclones and extreme ocean temperatures to improve resilience

NASA Astrophysics Data System (ADS)

Kuleshov, Y.; Jones, D.; Spillman, C. M.

2012-04-01

Climate change and climate extremes have a major impact on Australia and Pacific Island countries. Of particular concern are tropical cyclones and extreme ocean temperatures, the first being the most destructive events for terrestrial systems, while the latter has the potential to devastate ocean ecosystems through coral bleaching. As a practical response to climate change, under the Pacific-Australia Climate Change Science and Adaptation Planning program (PACCSAP), we are developing enhanced web-based information tools for providing seasonal forecasts for climatic extremes in the Western Pacific. Tropical cyclones are the most destructive weather systems that impact on coastal areas. Interannual variability in the intensity and distribution of tropical cyclones is large, and presently greater than any trends that are ascribable to climate change. In the warming environment, predicting tropical cyclone occurrence based on historical relationships, with predictors such as sea surface temperatures (SSTs) now frequently lying outside of the range of past variability meaning that it is not possible to find historical analogues for the seasonal conditions often faced by Pacific countries. Elevated SSTs are the primary trigger for mass coral bleaching events, which can lead to widespread damage and mortality on reef systems. Degraded coral reefs present many problems, including long-term loss of tourism and potential loss or degradation of fisheries. The monitoring and prediction of thermal stress events enables the support of a range of adaptive and management activities that could improve reef resilience to extreme conditions. Using the climate model POAMA (Predictive Ocean-Atmosphere Model for Australia), we aim to improve accuracy of seasonal forecasts of tropical cyclone activity and extreme SSTs for the regions of Western Pacific. Improved knowledge of extreme climatic events, with the assistance of tailored forecast tools, will help enhance the resilience and

Impact of the initialisation on the predictability of the Southern Ocean sea ice at interannual to multi-decadal timescales

NASA Astrophysics Data System (ADS)

Zunz, Violette; Goosse, Hugues; Dubinkina, Svetlana

2015-04-01

In this study, we assess systematically the impact of different initialisation procedures on the predictability of the sea ice in the Southern Ocean. These initialisation strategies are based on three data assimilation methods: the nudging, the particle filter with sequential importance resampling and the nudging proposal particle filter. An Earth system model of intermediate complexity is used to perform hindcast simulations in a perfect model approach. The predictability of the Antarctic sea ice at interannual to multi-decadal timescales is estimated through two aspects: the spread of the hindcast ensemble, indicating the uncertainty of the ensemble, and the correlation between the ensemble mean and the pseudo-observations, used to assess the accuracy of the prediction. Our results show that at decadal timescales more sophisticated data assimilation methods as well as denser pseudo-observations used to initialise the hindcasts decrease the spread of the ensemble. However, our experiments did not clearly demonstrate that one of the initialisation methods systematically provides with a more accurate prediction of the sea ice in the Southern Ocean than the others. Overall, the predictability at interannual timescales is limited to 3 years ahead at most. At multi-decadal timescales, the trends in sea ice extent computed over the time period just after the initialisation are clearly better correlated between the hindcasts and the pseudo-observations if the initialisation takes into account the pseudo-observations. The correlation reaches values larger than 0.5 in winter. This high correlation has likely its origin in the slow evolution of the ocean ensured by its strong thermal inertia, showing the importance of the quality of the initialisation below the sea ice.

Evaporation from a shallow, saline lake in the Nebraska Sandhills: Energy balance drivers of seasonal and interannual variability

NASA Astrophysics Data System (ADS)

Riveros-Iregui, Diego A.; Lenters, John D.; Peake, Colin S.; Ong, John B.; Healey, Nathan C.; Zlotnik, Vitaly A.

2017-10-01

Despite potential evaporation rates in excess of the local precipitation, dry climates often support saline lakes through groundwater inputs of water and associated solutes. These groundwater-fed lakes are important indicators of environmental change, in part because their shallow water levels and salinity are very sensitive to weather and climatic variability. Some of this sensitivity arises from high rates of open-water evaporation, which is a dominant but poorly quantified process for saline lakes. This study used the Bowen ratio energy budget method to calculate open-water evaporation rates for Alkali Lake, a saline lake in the Nebraska Sandhills region (central United States), where numerous groundwater-fed lakes occupy the landscape. Evaporation rates were measured during the warm season (May - October) over three consecutive years (2007-2009) to gain insights into the climatic and limnological factors driving evaporation, as well as the partitioning of energy balance components at seasonal and interannual time scales. Results show a seasonal peak in evaporation rate in late June of 7.0 mm day-1 (on average), with a maximum daily rate of 10.5 mm day-1 and a 3-year mean July-September (JAS) rate of 5.1 mm day-1, which greatly exceeds the long-term JAS precipitation rate of 1.3 mm day-1. Seasonal variability in lake evaporation closely follows that of net radiation and lake surface temperature, with sensible heat flux and heat storage variations being relatively small, except in response to short-term, synoptic events. Interannual changes in the surface energy balance were weak, by comparison, although a 6-fold increase in mean lake level over the three years (0.05-0.30 m) led to greater heat storage within the lake, an enhanced JAS lake-air temperature gradient, and greater sensible heat loss. These large variations in water level were also associated with large changes in absolute salinity (from 28 to 118 g kg-1), with periods of high salinity characterized

Variability and Predictability of Land-Atmosphere Interactions: Observational and Modeling Studies

NASA Technical Reports Server (NTRS)

Roads, John; Oglesby, Robert; Marshall, Susan; Robertson, Franklin R.

2002-01-01

The overall goal of this project is to increase our understanding of seasonal to interannual variability and predictability of atmosphere-land interactions. The project objectives are to: 1. Document the low frequency variability in land surface features and associated water and energy cycles from general circulation models (GCMs), observations and reanalysis products. 2. Determine what relatively wet and dry years have in common on a region-by-region basis and then examine the physical mechanisms that may account for a significant portion of the variability. 3. Develop GCM experiments to examine the hypothesis that better knowledge of the land surface enhances long range predictability. This investigation is aimed at evaluating and predicting seasonal to interannual variability for selected regions emphasizing the role of land-atmosphere interactions. Of particular interest are the relationships between large, regional and local scales and how they interact to account for seasonal and interannual variability, including extreme events such as droughts and floods. North and South America, including the Global Energy and Water Cycle Experiment Continental International Project (GEWEX GCIP), MacKenzie, and LBA basins, are currently being emphasized. We plan to ultimately generalize and synthesize to other land regions across the globe, especially those pertinent to other GEWEX projects.

Seasonal and Interannual Variations of Evaporation and their Relations with Precipitation, Net Radiation, and Net Carbon Accumulation for the Gediz Basin Area

NASA Technical Reports Server (NTRS)

Choudhury, Bhaskar J.

1999-01-01

A model combining the rate of carbon assimilation with water and energy balance equations has been run using satellite and ancillary data for a period of 60 months (January 1986 to December 1990). Calculations for the Gediz basin area give mean annual evaporation as 395 mm, which is composed of 45% transpiration, 42% soil evaporation and 13% interception. The coefficient of interannual variation of evaporation is found to be 6%, while that for precipitation and net radiation are, respectively, 16% and 2%, illustrating that net radiation has an important effect in modulating interannual variation of evaporation. The mean annual water use efficiency (i.e., the ratio of net carbon accumulation and total evaporation) is ca. 1 g/sq m/mm, and has a coefficient of interannual variation of 5%. A comparison of the mean water use efficiency with field observations suggests that evaporation over the area is utilized well for biomass production. The reference crop evaporation for irrigated areas has annual mean and coefficient of variation as, respectively, 1176 mm and 3%. The total evaporation during three summer months of peak evaporation (June-August) is estimated to be about 575 mm for irrigated crops like maize and cotton. Seasonal variations of the fluxes are presented.

Seasonal and Interannual Trends in Largest Cholera Endemic Megacity: Water Sustainability - Climate - Health Challenges in Dhaka, Bangladesh

NASA Astrophysics Data System (ADS)

Akanda, Ali S.; Jutla, Antarpreet; Faruque, Abu S. G.; Huq, Anwar; Colwell, Rita R.

2014-05-01

region. Our findings may prove to be useful in both water sustainability and disaster management perspectives as the dry and wet seasonal trends are affecting both endemic and epidemic outbreaks, respectively, and are influenced by distinctly different seasonal and interannual drivers.

Performance of the WRF model to simulate the seasonal and interannual variability of hydrometeorological variables in East Africa: a case study for the Tana River basin in Kenya

NASA Astrophysics Data System (ADS)

Kerandi, Noah Misati; Laux, Patrick; Arnault, Joel; Kunstmann, Harald

2017-10-01

This study investigates the ability of the regional climate model Weather Research and Forecasting (WRF) in simulating the seasonal and interannual variability of hydrometeorological variables in the Tana River basin (TRB) in Kenya, East Africa. The impact of two different land use classifications, i.e., the Moderate Resolution Imaging Spectroradiometer (MODIS) and the US Geological Survey (USGS) at two horizontal resolutions (50 and 25 km) is investigated. Simulated precipitation and temperature for the period 2011-2014 are compared with Tropical Rainfall Measuring Mission (TRMM), Climate Research Unit (CRU), and station data. The ability of Tropical Rainfall Measuring Mission (TRMM) and Climate Research Unit (CRU) data in reproducing in situ observation in the TRB is analyzed. All considered WRF simulations capture well the annual as well as the interannual and spatial distribution of precipitation in the TRB according to station data and the TRMM estimates. Our results demonstrate that the increase of horizontal resolution from 50 to 25 km, together with the use of the MODIS land use classification, significantly improves the precipitation results. In the case of temperature, spatial patterns and seasonal cycle are well reproduced, although there is a systematic cold bias with respect to both station and CRU data. Our results contribute to the identification of suitable and regionally adapted regional climate models (RCMs) for East Africa.

Interannual drivers of the seasonal cycle of CO2 in the Southern Ocean

NASA Astrophysics Data System (ADS)

Gregor, Luke; Kok, Schalk; Monteiro, Pedro M. S.

2018-04-01

Resolving and understanding the drivers of variability of CO2 in the Southern Ocean and its potential climate feedback is one of the major scientific challenges of the ocean-climate community. Here we use a regional approach on empirical estimates of pCO2 to understand the role that seasonal variability has in long-term CO2 changes in the Southern Ocean. Machine learning has become the preferred empirical modelling tool to interpolate time- and location-restricted ship measurements of pCO2. In this study we use an ensemble of three machine-learning products: support vector regression (SVR) and random forest regression (RFR) from Gregor et al. (2017), and the self-organising-map feed-forward neural network (SOM-FFN) method from Landschützer et al. (2016). The interpolated estimates of ΔpCO2 are separated into nine regions in the Southern Ocean defined by basin (Indian, Pacific, and Atlantic) and biomes (as defined by Fay and McKinley, 2014a). The regional approach shows that, while there is good agreement in the overall trend of the products, there are periods and regions where the confidence in estimated ΔpCO2 is low due to disagreement between the products. The regional breakdown of the data highlighted the seasonal decoupling of the modes for summer and winter interannual variability. Winter interannual variability had a longer mode of variability compared to summer, which varied on a 4-6-year timescale. We separate the analysis of the ΔpCO2 and its drivers into summer and winter. We find that understanding the variability of ΔpCO2 and its drivers on shorter timescales is critical to resolving the long-term variability of ΔpCO2. Results show that ΔpCO2 is rarely driven by thermodynamics during winter, but rather by mixing and stratification due to the stronger correlation of ΔpCO2 variability with mixed layer depth. Summer pCO2 variability is consistent with chlorophyll a variability, where higher concentrations of chlorophyll a correspond with lower pCO2

Towards seasonal Arctic shipping route predictions

NASA Astrophysics Data System (ADS)

Haines, K.; Melia, N.; Hawkins, E.; Day, J. J.

2017-12-01

In our previous work [1] we showed how trans-Arctic shipping routes would become more available through the 21st century as sea ice declines, using CMIP5 models with means and stds calibrated to PIOMAS sea ice observations. Sea ice will continue to close shipping routes to open water vessels through the winter months for the foreseeable future so the availability of open sea routes will vary greatly from year to year. Here [2] we look at whether the trans-Arctic shipping season period can be predicted in seasonal forecasts, again using several climate models, and testing both perfect and imperfect knowledge of the initial sea ice conditions. We find skilful predictions of the upcoming summer shipping season can be made from as early as January, although typically forecasts may show lower skill before a May `predictability barrier'. Focussing on the northern sea route (NSR) off Siberia, the date of opening of this sea route is twice as variable as the closing date, and this carries through to reduced predictability at the start of the season. Under climate change the later freeze-up date accounts for 60% of the lengthening season, Fig1 We find that predictive skill is state dependent with predictions for high or low ice years exhibiting greater skill than for average ice years. Forecasting the exact timing of route open periods is harder (more weather dependent) under average ice conditions while in high and low ice years the season is more controlled by the initial ice conditions from spring onwards. This could be very useful information for companies planning vessel routing for the coming season. We tested this dependence on the initial ice conditions by changing the initial ice state towards climatologically average conditions and show directly that early summer sea-ice thickness information is crucial to obtain skilful forecasts of the coming shipping season. Mechanisms for this are discussed. This strongly suggests that good sea ice thickness observations

Diurnal, seasonal and inter-annual variations in the Schumann resonance parameters

NASA Astrophysics Data System (ADS)

Price, Colin; Melnikov, Alexander

2004-09-01

The Schumann resonances (SR) represent an electromagnetic phenomenon in the Earth's atmosphere related to global lightning activity. The spectral characteristics of the SR modes are defined by their resonant mode amplitude, center frequency and half-width (Q-factor). Long-term (4 years) diurnal and seasonal variations of these parameters are presented based on measurements at a field site in the Negev desert, Israel. Variations of the different modes (8, 14 and 20Hz) and the different electromagnetic components (Hns, Hew and Ez) are presented. The power variations of the various modes and components show three dominant maxima in the diurnal cycle related to lightning activity in south-east Asia (0800UT), Africa (1400UT) and South America (2000UT). The largest global lightning activity occurs during the northern hemisphere summer (JJA) with the southern hemisphere summer (DJF) having the least lightning around the globe. The frequency and half-width (Q-factor) variations of the different modes and SR components are fairly complicated in structure, and will need additional theoretical work to explain their variations. However, the frequency variations are in excellent agreement with previous studies, implying that the frequency variations are robust features of the SR. The inter-annual variability of global lightning activity is shown to vary differently for each of the three major source regions of global lightning.

Accounting for inter-annual and seasonal variability in regionalization of hydrologic response in the Great Lakes basin

NASA Astrophysics Data System (ADS)

Kult, J. M.; Fry, L. M.; Gronewold, A. D.

2012-12-01

Methods for predicting streamflow in areas with limited or nonexistent measures of hydrologic response typically invoke the concept of regionalization, whereby knowledge pertaining to gauged catchments is transferred to ungauged catchments. In this study, we identify watershed physical characteristics acting as primary drivers of hydrologic response throughout the US portion of the Great Lakes basin. Relationships between watershed physical characteristics and hydrologic response are generated from 166 catchments spanning a variety of climate, soil, land cover, and land form regimes through regression tree analysis, leading to a grouping of watersheds exhibiting similar hydrologic response characteristics. These groupings are then used to predict response in ungauged watersheds in an uncertainty framework. Results from this method are assessed alongside one historical regionalization approach which, while simple, has served as a cornerstone of Great Lakes regional hydrologic research for several decades. Our approach expands upon previous research by considering multiple temporal characterizations of hydrologic response. Due to the substantial inter-annual and seasonal variability in hydrologic response observed over the Great Lakes basin, results from the regression tree analysis differ considerably depending on the level of temporal aggregation used to define the response. Specifically, higher levels of temporal aggregation for the response metric (for example, indices derived from long-term means of climate and streamflow observations) lead to improved watershed groupings with lower within-group variance. However, this perceived improvement in model skill occurs at the cost of understated uncertainty when applying the regression to time series simulations or as a basis for model calibration. In such cases, our results indicate that predictions based on long-term characterizations of hydrologic response can produce misleading conclusions when applied at shorter

Relationship between the global electric circuit and electrified cloud parameters at diurnal, seasonal, and interannual timescales

NASA Astrophysics Data System (ADS)

Lavigne, Thomas; Liu, Chuntao; Deierling, Wiebke; Mach, Douglas

2017-08-01

In the early 1900s, J. W. Whipple began to validate C. T. R. Wilson's global electric circuit (GEC) hypothesis by correlating the diurnal variation of global thunder days with the diurnal variation of the fair weather electric field measured by the Carnegie Cruise. This study applies 16+ years of precipitation feature (PF) data from the Tropical Rainfall Measuring Mission, including lightning data from the Lightning Imaging Sensor, alongside 12 years of electric field measurements from Vostok, Antarctica, to further examine this relationship. Joint diurnal-seasonal variations of the electric field are introduced and compared with a variety of PF parameters that are potentially related to the GEC. All tested PF parameters showed significant correlations to the electric field on the joint seasonal-diurnal timescale, with the flash rate and volume of 30 dBZ between the -5°C and -35°C isotherms showing the best linear correlations with R2 values of 0.67 and 0.62, respectively. Furthermore, these relationships are analyzed during the two different phases of the El Niño-Southern Oscillation. Results show different seasonal-diurnal variations of the electric field during El Niño and La Niña periods, with enhancements in the electric field between the months of January through April at 16-24 UTC in La Niña years. A similar trend is shown in global PF parameters, indicating relationships between the variations seen in the fair weather electric field and the variations of global PFs at diurnal, seasonal, and interannual timescales. This provides further evidence that PFs around the globe have a direct connection to the GEC.

Seasonal and interannual variations in coccolithophore abundance off Terceira Island, Azores (Central North Atlantic)

NASA Astrophysics Data System (ADS)

Narciso, Áurea; Gallo, Francesca; Valente, André; Cachão, Mário; Cros, Lluïsa; Azevedo, Eduardo B.; e Ramos, Joana Barcelos

2016-04-01

In order to characterize the natural coccolithophore community occurring offshore Azores and to determine their annual and interannual patterns, monthly samples were collected, from September 2010 to December 2014, in the photic zone off Terceira Island. The present study revealed a clear seasonal distribution and a considerable interannual variability of the living coccolithophore community. The highest coccolithophore abundances were observed during spring and winter months, especially due to the smaller species Emiliania huxleyi and Gephyrocapsa ericsonii. In fact, the highest biomass period was registered during April 2011, associated with enhanced abundance of the overcalcified morphotype of E. huxleyi, which was possibly influenced by subpolar waters and subsequent upwelling conditions. The highest abundances of Gephyrocapsa muellerae were recorded during June 2011 and 2014, indicating that this species characterizes the transition between the period of maximum productivity and the subsequent smoother environmental conditions, the first and the later stages of the phytoplankton succession described by Margalef, respectively. During summer to early fall, a gradual decrease of the overall coccolithophore abundance was observed, while the species richness (Margalef diversity index) increased. A subtropical coccolithophore assemblage mainly composed by Umbellosphaera tenuis, Syracosphaera spp., Discosphaera tubifera, Rhabdosphaera clavigera and Coronosphaera mediterranea indicated the presence of surface warmer waters accompanied by reduced mixing and low nutrients concentration. During late fall to winter, the coccolithophore abundance increased again with a concomitant reduction in species diversity. This is potentially linked to low sea surface temperatures, moderate nutrients concentration and surface mixed layer deepening. During 2011, colder and productive waters led to an increase in the total coccolithophore abundances. On contrary, during 2012

Global modeling of land water and energy balances. Part III: Interannual variability

USGS Publications Warehouse

Shmakin, A.B.; Milly, P.C.D.; Dunne, K.A.

2002-01-01

The Land Dynamics (LaD) model is tested by comparison with observations of interannual variations in discharge from 44 large river basins for which relatively accurate time series of monthly precipitation (a primary model input) have recently been computed. When results are pooled across all basins, the model explains 67% of the interannual variance of annual runoff ratio anomalies (i.e., anomalies of annual discharge volume, normalized by long-term mean precipitation volume). The new estimates of basin precipitation appear to offer an improvement over those from a state-of-the-art analysis of global precipitation (the Climate Prediction Center Merged Analysis of Precipitation, CMAP), judging from comparisons of parallel model runs and of analyses of precipitation-discharge correlations. When the new precipitation estimates are used, the performance of the LaD model is comparable to, but not significantly better than, that of a simple, semiempirical water-balance relation that uses only annual totals of surface net radiation and precipitation. This implies that the LaD simulations of interannual runoff variability do not benefit substantially from information on geographical variability of land parameters or seasonal structure of interannual variability of precipitation. The aforementioned analyses necessitated the development of a method for downscaling of long-term monthly precipitation data to the relatively short timescales necessary for running the model. The method merges the long-term data with a reference dataset of 1-yr duration, having high temporal resolution. The success of the method, for the model and data considered here, was demonstrated in a series of model-model comparisons and in the comparisons of modeled and observed interannual variations of basin discharge.

Relative importance of precipitation frequency and intensity in inter-annual variation of precipitation in Singapore during 1980-2013

NASA Astrophysics Data System (ADS)

Li, Xin; Babovic, Vladan

2017-04-01

Observed studies on inter-annual variation of precipitation provide insight into the response of precipitation to anthropogenic climate change and natural climate variability. Inter-annual variation of precipitation results from the concurrent variations of precipitation frequency and intensity, understanding of the relative importance of frequency and intensity in the variability of precipitation can help fathom its changing properties. Investigation of the long-term changes of precipitation schemes has been extensively carried out in many regions across the world, however, detailed studies of the relative importance of precipitation frequency and intensity in inter-annual variation of precipitation are still limited, especially in the tropics. Therefore, this study presents a comprehensive framework to investigate the inter-annual variation of precipitation and the dominance of precipitation frequency and intensity in a tropical urban city-state, Singapore, based on long-term (1980-2013) daily precipitation series from 22 rain gauges. First, an iterative Mann-Kendall trend test method is applied to detect long-term trends in precipitation total, frequency and intensity at both annual and seasonal time scales. Then, the relative importance of precipitation frequency and intensity in inducing the inter-annual variation of wet-day precipitation total is analyzed using a dominance analysis method based on linear regression. The results show statistically significant upward trends in wet-day precipitation total, frequency and intensity at annual time scale, however, these trends are not evident during the monsoon seasons. The inter-annual variation of wet-day precipitation is mainly dominated by precipitation intensity for most of the stations at annual time scale and during the Northeast monsoon season. However, during the Southwest monsoon season, the inter-annual variation of wet-day precipitation is mainly dominated by precipitation frequency. These results have

Association with humans and seasonality interact to reverse predictions for animal space use.

PubMed

Laver, Peter N; Alexander, Kathleen A

2018-01-01

Variation in animal space use reflects fitness trade-offs associated with ecological constraints. Associated theories such as the metabolic theory of ecology and the resource dispersion hypothesis generate predictions about what drives variation in animal space use. But, metabolic theory is usually tested in macro-ecological studies and is seldom invoked explicitly in within-species studies. Full evaluation of the resource dispersion hypothesis requires testing in more species. Neither have been evaluated in the context of anthropogenic landscape change. In this study, we used data for banded mongooses ( Mungos mungo ) in northeastern Botswana, along a gradient of association with humans, to test for effects of space use drivers predicted by these theories. We used Bayesian parameter estimation and inference from linear models to test for seasonal differences in space use metrics and to model seasonal effects of space use drivers. Results suggest that space use is strongly associated with variation in the level of overlap that mongoose groups have with humans. Seasonality influences this association, reversing seasonal space use predictions historically-accepted by ecologists. We found support for predictions of the metabolic theory when moderated by seasonality, by association with humans and by their interaction. Space use of mongooses living in association with humans was more concentrated in the dry season than the wet season, when historically-accepted ecological theory predicted more dispersed space use. Resource richness factors such as building density were associated with space use only during the dry season. We found negligible support for predictions of the resource dispersion hypothesis in general or for metabolic theory where seasonality and association with humans were not included. For mongooses living in association with humans, space use was not associated with patch dispersion or group size over both seasons. In our study, living in association

Seasonal Extratropical Storm Activity Potential Predictability and its Origins during the Cold Seasons

NASA Astrophysics Data System (ADS)

Pingree-Shippee, K. A.; Zwiers, F. W.; Atkinson, D. E.

2016-12-01

Extratropical cyclones (ETCs) often produce extreme hazardous weather conditions, such as high winds, blizzard conditions, heavy precipitation, and flooding, all of which can have detrimental socio-economic impacts. The North American east and west coastal regions are both strongly influenced by ETCs and, subsequently, land-based, coastal, and maritime economic sectors in Canada and the USA all experience strong adverse impacts from extratropical storm activity from time to time. Society would benefit if risks associated with ETCs and storm activity variability could be reliably predicted for the upcoming season. Skillful prediction would enable affected sectors to better anticipate, prepare for, manage, and respond to storm activity variability and the associated risks and impacts. In this study, the potential predictability of seasonal variations in extratropical storm activity is investigated using analysis of variance to provide quantitative and geographical observational evidence indicative of whether it may be possible to predict storm activity on the seasonal timescale. This investigation will also identify origins of the potential predictability using composite analysis and large-scale teleconnections (Southern Oscillation, Pacific Decadal Oscillation, and North Atlantic Oscillation), providing the basis upon which seasonal predictions can be developed. Seasonal potential predictability and its origins are investigated for the cold seasons (OND, NDJ, DJF, JFM) during the 1979-2015 time period using daily mean sea level pressure, absolute pressure tendency, and 10-m wind speed from the ECMWF ERA-Interim reanalysis as proxies for extratropical storm activity. Results indicate potential predictability of seasonal variations in storm activity in areas strongly influenced by ETCs and with origins in the investigated teleconnections. For instance, the North Pacific storm track has considerable potential predictability and with notable origins in the SO and PDO.

Effects of Changing Climate During the Snow Ablation Season on Seasonal Streamflow Forecasts

NASA Astrophysics Data System (ADS)

Gutzler, D. S.; Chavarria, S. B.

2017-12-01

Seasonal forecasts of total surface runoff (Q) in snowmelt-dominated watersheds derive most of their prediction skill from the historical relationship between late winter snowpack (SWE) and subsequent snowmelt runoff. Across the western US, however, the relationship between SWE and Q is weakening as temperatures rise. We describe the effects of climate variability and change during the springtime snow ablation season on water supply outlooks (forecasts of Q) for southwestern rivers. As snow melts earlier, the importance of post-snow rainfall increases: interannual variability of spring season precipitation accounts for an increasing fraction of the variability of Q in recent decades. The results indicate that improvements to the skill of S2S forecasts of spring season temperature and precipitation would contribute very significantly to water supply outlooks that are now based largely on observed SWE. We assess this hypothesis using historical data from several snowpack-dominated basins in the American Southwest (Rio Grande, Pecos, and Gila Rivers) which are undergoing rapid climate change.

Seasonal and interannual variations in total ozone revealed by the Nimbus-4 backscattered ultraviolet experiment

NASA Technical Reports Server (NTRS)

Hilsenrath, E.; Heath, D. F.; Schlesinger, B. M.

1978-01-01

The first two years of Backscattered Ultraviolet (BUV) ozone data from the Nimbus-4 spacecraft were reprocessed. The seasonal variations of total ozone for the period April 1970 to April 1972 are described using daily zonal means to 10 deg latitude zones and a time-latitude cross section. In addition, the BUV data are compared with analyzed Dobson data and with IRIS data also obtained from the Nimbus-4 spacecraft. A harmonic analysis was performed on the daily zonal means. Amplitudes, days of peaks, and percentage of variance were computed for annual and semi-annual waves and for higher harmonics of an annual period for the two years. Asymmetries are found in the annual waves in the two hemispheres, with a subtle interannual difference which may be due to changes in the general circulation. A significant semi-annual component is detected in the tropics for the first year, which appears to result from influences of the annual waves in the two hemispheres.

Ensemble Cannonical Correlation Prediction of Seasonal Precipitation Over the US

NASA Technical Reports Server (NTRS)

Lau, William K. M.; Kim, Kyu-Myong; Shen, Samuel; Einaudi, Franco (Technical Monitor)

2001-01-01

This paper presents preliminary results of an ensemble cannonical correlation (ECC) prediction scheme developed at the Climate and Radiation Branch, NASA/Goddard Space Flight Center for determining the potential predictability of regional precipitation, and for climate downscaling studies. The scheme is tested on seasonal hindcasts of anomalous precipitation over the continental United States using global sea surface temperature (SST) for 1951-2000. To maximize the forecast skill derived from SST, the world ocean is divided into nonoverlapping sectors. The cannonical SST modes for each sector are used as the predictor for the ensemble hindcasts. Results show that the ECC yields a substantial (10-25%) increase in prediction skills for all regions of the US and for all seasonal compared to traditional CCA prediction schemes. For the boreal winter, the tropical Pacific contributes the largest potential predictability to precipitation in the southwestern and southeastern regions, while the North Pacific and the North Atlantic are responsible for enhanced forecast skills in the Pacific Northwest, the northern Great Plains and Ohio Valley. Most importantly, the ECC increases skill for summertime precipitation prediction and substantially reduced the spring predictability barrier over all regions of the US continent. Besides SST, the ECC is designed with the flexibility to include any number of predictor fields, such as soil moisture, snow cover and regional regional data. Moreover, the ECC forecasts can be applied to other climate subsystems and, in conjunction with further diagnostic or model studies will enables a better understanding of the dynamic links between climate variations and precipitation, not only for the US, but also for other regions of the world.

Variability, trends, and predictability of seasonal sea ice retreat and advance in the Chukchi Sea

NASA Astrophysics Data System (ADS)

Serreze, Mark C.; Crawford, Alex D.; Stroeve, Julienne C.; Barrett, Andrew P.; Woodgate, Rebecca A.

2016-10-01

As assessed over the period 1979-2014, the date that sea ice retreats to the shelf break (150 m contour) of the Chukchi Sea has a linear trend of -0.7 days per year. The date of seasonal ice advance back to the shelf break has a steeper trend of about +1.5 days per year, together yielding an increase in the open water period of 80 days. Based on detrended time series, we ask how interannual variability in advance and retreat dates relate to various forcing parameters including radiation fluxes, temperature and wind (from numerical reanalyses), and the oceanic heat inflow through the Bering Strait (from in situ moorings). Of all variables considered, the retreat date is most strongly correlated (r ˜ 0.8) with the April through June Bering Strait heat inflow. After testing a suite of statistical linear models using several potential predictors, the best model for predicting the date of retreat includes only the April through June Bering Strait heat inflow, which explains 68% of retreat date variance. The best model predicting the ice advance date includes the July through September inflow and the date of retreat, explaining 67% of advance date variance. We address these relationships by discussing heat balances within the Chukchi Sea, and the hypothesis of oceanic heat transport triggering ocean heat uptake and ice-albedo feedback. Developing an operational prediction scheme for seasonal retreat and advance would require timely acquisition of Bering Strait heat inflow data. Predictability will likely always be limited by the chaotic nature of atmospheric circulation patterns.

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

Extremes in East African hydroclimate and links to Indo-Pacific variability on interannual to decadal timescales

NASA Astrophysics Data System (ADS)

Ummenhofer, Caroline C.; Kulüke, Marco; Tierney, Jessica E.

2018-04-01

East African hydroclimate exhibits considerable variability across a range of timescales, with implications for its population that depends on the region's two rainy seasons. Recent work demonstrated that current state-of-the-art climate models consistently underestimate the long rains in boreal spring over the Horn of Africa while overestimating the short rains in autumn. This inability to represent the seasonal cycle makes it problematic for climate models to project changes in East African precipitation. Here we consider whether this bias also has implications for understanding interannual and decadal variability in the East African long and short rains. Using a consistent framework with an unforced multi-century global coupled climate model simulation, the role of Indo-Pacific variability for East African rainfall is compared across timescales and related to observations. The dominant driver of East African rainfall anomalies critically depends on the timescale under consideration: Interannual variations in East African hydroclimate coincide with significant sea surface temperature (SST) anomalies across the Indo-Pacific, including those associated with the El Niño-Southern Oscillation (ENSO) in the eastern Pacific, and are linked to changes in the Walker circulation, regional winds and vertical velocities over East Africa. Prolonged drought/pluvial periods in contrast exhibit anomalous SST predominantly in the Indian Ocean and Indo-Pacific warm pool (IPWP) region, while eastern Pacific anomalies are insignificant. We assessed dominant frequencies in Indo-Pacific SST and found the eastern equatorial Pacific dominated by higher-frequency variability in the ENSO band, while the tropical Indian Ocean and IPWP exhibit lower-frequency variability beyond 10 years. This is consistent with the different contribution to regional precipitation anomalies for the eastern Pacific versus Indian Ocean and IPWP on interannual and decadal timescales, respectively. In the model

Global assessment of surfing conditions: seasonal, interannual and long-term variability

NASA Astrophysics Data System (ADS)

Espejo, A.; Losada, I.; Mendez, F.

2012-12-01

International surfing destinations owe a great debt to specific combinations of wind-wave, thermal conditions and local bathymetry. As surf quality depends on a vast number of geophysical variables, a multivariable standardized index on the basis of expert judgment is proposed to analyze surf resource in a worldwide domain. Data needed is obtained by combining several datasets (reanalyses): 60-year satellite-calibrated spectral wave hindcast (GOW, WaveWatchIII), wind fields from NCEP/NCAR, global sea surface temperature from ERSST.v3b, and global tides from TPXO7.1. A summary of the global surf resource is presented, which highlights the high degree of variability in surfable events. According to general atmospheric circulation, results show that west facing low to middle latitude coasts are more suitable for surfing, especially those in Southern Hemisphere. Month to month analysis reveals strong seasonal changes in the occurrence of surfable events, enhancing those in North Atlantic or North Pacific. Interannual variability is investigated by comparing occurrence values with global and regional climate patterns showing a great influence at both, global and regional scales. Analysis of long term trends shows an increase in the probability of surfable events over the west facing coasts on the planet (i.e. + 30 hours/year in California). The resulting maps provide useful information for surfers and surf related stakeholders, coastal planning, education, and basic research.; Figure 1. Global distribution of medium quality (a) and high quality surf conditions probability (b).

The impact of annual and seasonal rainfall patterns on growth and phenology of emergent tree species in Southeastern Amazonia, Brazil

Treesearch

James Grogan; Mark Schulze

2012-01-01

Understanding tree growth in response to rainfall distribution is critical to predicting forest and species population responses to climate change. We investigated inter-annual and seasonal variation in stem diameter by three emergent tree species in a seasonally dry tropical forest in southeast Pará, Brazil. Annual diameter growth rates by Swietenia macrophylla...

Interannual and seasonal variability of winter-spring cohort of neon flying squid abundance in the Northwest Pacific Ocean during 1995-2011

NASA Astrophysics Data System (ADS)

Yu, Wei; Chen, Xinjun; Yi, Qian

2016-06-01

The neon flying squid, Ommastrephes bartramii, is a species of economically important cephalopod in the Northwest Pacific Ocean. Its short lifespan increases the susceptibility of the distribution and abundance to the direct impact of the environmental conditions. Based on the generalized linear model (GLM) and generalized additive model (GAM), the commercial fishery data from the Chinese squid-jigging fleets during 1995 to 2011 were used to examine the interannual and seasonal variability in the abundance of O. bartramii, and to evaluate the influences of variables on the abundance (catch per unit effort, CPUE). The results from GLM suggested that year, month, latitude, sea surface temperature (SST), mixed layer depth (MLD), and the interaction term ( SST×MLD) were significant factors. The optimal model based on GAM included all the six significant variables and could explain 42.43% of the variance in nominal CPUE. The importance of the six variables was ranked by decreasing magnitude: year, month, latitude, SST, MLD and SST×MLD. The squid was mainly distributed in the waters between 40°N and 44°N in the Northwest Pacific Ocean. The optimal ranges of SST and MLD were from 14 to 20°C and from 10 to 30 m, respectively. The squid abundance greatly fluctuated from 1995 to 2011. The CPUE was low during 1995-2002 and high during 2003-2008. Furthermore, the squid abundance was typically high in August. The interannual and seasonal variabilities in the squid abundance were associated with the variations of marine environmental conditions and the life history characteristics of squid.

MJO prediction skill of the subseasonal-to-seasonal (S2S) prediction models

NASA Astrophysics Data System (ADS)

Son, S. W.; Lim, Y.; Kim, D.

2017-12-01

The Madden-Julian Oscillation (MJO), the dominant mode of tropical intraseasonal variability, provides the primary source of tropical and extratropical predictability on subseasonal to seasonal timescales. To better understand its predictability, this study conducts quantitative evaluation of MJO prediction skill in the state-of-the-art operational models participating in the subseasonal-to-seasonal (S2S) prediction project. Based on bivariate correlation coefficient of 0.5, the S2S models exhibit MJO prediction skill ranging from 12 to 36 days. These prediction skills are affected by both the MJO amplitude and phase errors, the latter becoming more important with forecast lead times. Consistent with previous studies, the MJO events with stronger initial amplitude are typically better predicted. However, essentially no sensitivity to the initial MJO phase is observed. Overall MJO prediction skill and its inter-model spread are further related with the model mean biases in moisture fields and longwave cloud-radiation feedbacks. In most models, a dry bias quickly builds up in the deep tropics, especially across the Maritime Continent, weakening horizontal moisture gradient. This likely dampens the organization and propagation of MJO. Most S2S models also underestimate the longwave cloud-radiation feedbacks in the tropics, which may affect the maintenance of the MJO convective envelop. In general, the models with a smaller bias in horizontal moisture gradient and longwave cloud-radiation feedbacks show a higher MJO prediction skill, suggesting that improving those processes would enhance MJO prediction skill.

Configuration and Intraseasonal Duration of Interannual Anomalies of the Great Plains Low-Level Jet

NASA Technical Reports Server (NTRS)

Helfand, H. M.

2002-01-01

Despite the fact that the low-level jet of the southern Great Plains (the GPLLJ) of the U.S. is primarily a nocturnal phenomenon that virtually vanishes during the daylight hours, it is one of the most persistent and stable climatological features of the low-level continental flow during the warm-season months, May through August. We have used significant-level data to validate the skill of the GEOS-1 Data Assimilation System (DAS) in realistically detecting this jet and inferring its structure and evolution. We have then carried out a 15-year reanalysis with the GEOS-1 DAS to determine its climatology and mean diurnal cycle and to study its interannual variability. Interannual anomalies of the meridional flow associated with the GPLLJ are much smaller than the mean diurnal fluctuations, than random intraseasonal anomalies, and than the mean wind itself. There are three maxima of low-level meridional flow variance over the Great Plains and the Gulf of Mexico: a 1.2 m2 s-2 peak over the southeast Texas, to the east and south of the mean velocity peak, a 1.0 m2 s-2 peak over the western Gulf of Mexico, and a .8 m2 s-2 peak over the upper Great Plains (UGP), near the Nebraska/South Dakota border. Each of the three variance maxima corresponds to a spatially coherent, jet-like pattern of low-level flow interannual variability. There are also three dominant modes of interannual variability corresponding to the three variance maxima, but not in a simple one-to-one relationship. Cross-sectional profiles of mean southerly wind over Texas remain relatively stable and recognizable from year to year with only its eastward flank showing significant variability. This variability, however, exhibits a distinct, biennial oscillation during the first six to seven years of the reanalysis period and only then. This intermittent biennial oscillation (IBO, one of the three modes discussed in the previous paragraph) in the lowlevel flow is restricted to the region surrounding eastern

Towards seasonal Arctic shipping route predictions

NASA Astrophysics Data System (ADS)

Melia, N.; Haines, K.; Hawkins, E.; Day, J. J.

2017-08-01

The continuing decline in Arctic sea-ice will likely lead to increased human activity and opportunities for shipping in the region, suggesting that seasonal predictions of route openings will become ever more important. Here we present results from a set of ‘perfect model’ experiments to assess the predictability characteristics of the opening of Arctic sea routes. We find skilful predictions of the upcoming summer shipping season can be made from as early as January, although typically forecasts show lower skill before a May ‘predictability barrier’. We demonstrate that in forecasts started from January, predictions of route opening date are twice as uncertain as predicting the closing date and that the Arctic shipping season is becoming longer due to climate change, with later closing dates mostly responsible. We find that predictive skill is state dependent with predictions for high or low ice years exhibiting greater skill than medium ice years. Forecasting the fastest open water route through the Arctic is accurate to within 200 km when predicted from July, a six-fold increase in accuracy compared to forecasts initialised from the previous November, which are typically no better than climatology. Finally we find that initialisation of accurate summer sea-ice thickness information is crucial to obtain skilful forecasts, further motivating investment into sea-ice thickness observations, climate models, and assimilation systems.

Large-scale Controls on Atlantic Tropical Cyclone Activity on Seasonal Time Scales

PubMed Central

Lim, Young-Kwon; Schubert, Siegfried D.; Reale, Oreste; Molod, Andrea M.; Suarez, Max J.; Auer, Benjamin M.

2018-01-01

Interannual variations in seasonal tropical cyclone (TC) activity (e.g., genesis frequency and location, track pattern, and landfall) over the Atlantic are explored by employing observationally-constrained simulations with the NASA Goddard Earth Observing System version (GEOS-5) atmospheric general circulation model. The climate modes investigated are El Niño-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Atlantic Meridional Mode (AMM). The results show that the NAO and AMM can strongly modify and even oppose the well-known ENSO impacts, like in 2005, when a strong positive AMM (associated with warm SSTs and a negative SLP anomaly over the western tropical Atlantic), led to a very active TC season with enhanced TC genesis over the Caribbean Sea and a number of landfalls over North America, under a neutral ENSO condition. On the other end, the weak TC activity during 2013 (characterized by weak negative Niño index) appears caused by a NAO-induced positive SLP anomaly with enhanced vertical wind shear over the tropical North Atlantic. During 2010, the combined impact of the three modes produced positive SST anomalies across the entire low- latitudinal Atlantic and a weaker subtropical high, leading to more early recurvers and thus fewer landfalls despite enhanced TC genesis. The study provides evidence that TC number and track are very sensitive to the relative phases and intensities of these three modes, and not just to ENSO alone. Examination of seasonal predictability reveals that predictive skill of the three modes is limited over tropics to sub-tropics, with the AMM having the highest predictability over the North Atlantic, followed by ENSO and NAO. PMID:29928071

Large-Scale Controls on Atlantic Tropical Cyclone Activity on Seasonal Time Scales

NASA Technical Reports Server (NTRS)

Lim, Young-Kwon; Schubert, Siegfried D.; Reale, Oreste; Molod, Andrea M.; Suarez, Max J.; Auer, Benjamin M.

2016-01-01

Interannual variations in seasonal tropical cyclone (TC) activity (e.g., genesis frequency and location, track pattern, and landfall) over the Atlantic are explored by employing observationally-constrained simulations with the NASA Goddard Earth Observing System version (GEOS-5) atmospheric general circulation model. The climate modes investigated are El Nino-Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Atlantic Meridional Mode (AMM). The results show that the NAO and AMM can strongly modify and even oppose the well- known ENSO impacts, like in 2005, when a strong positive AMM (associated with warm SSTs and a negative SLP anomaly over the western tropical Atlantic), led to a very active TC season with enhanced TC genesis over the Caribbean Sea and a number of landfalls over North America, under a neutral ENSO condition. On the other end, the weak TC activity during 2013 (characterized by weak negative Nio index) appears caused by a NAO-induced positive SLP anomaly with enhanced vertical wind shear over the tropical North Atlantic. During 2010, the combined impact of the three modes produced positive SST anomalies across the entire low-latitudinal Atlantic and a weaker subtropical high, leading to more early recurvers and thus fewer landfalls despite enhanced TC genesis. The study provides evidence that TC number and track are very sensitive to the relative phases and intensities of these three modes, and not just to ENSO alone. Examination of seasonal predictability reveals that predictive skill of the three modes is limited over tropics to sub-tropics, with the AMM having the highest predictability over the North Atlantic, followed by ENSO and NAO.

The NOAA-NASA CZCS Reanalysis Effort

NASA Technical Reports Server (NTRS)

Gregg, Watson W.; Conkright, Margarita E.; OReilly, John E.; Patt, Frederick S.; Wang, Meng-Hua; Yoder, James; Casey-McCabe, Nancy; Koblinsky, Chester J. (Technical Monitor)

2001-01-01

Satellite observations of global ocean chlorophyll span over two decades. However, incompatibilities between processing algorithms prevent us from quantifying natural variability. We applied a comprehensive reanalysis to the Coastal Zone Color Scanner (CZCS) archive, called the NOAA-NASA CZCS Reanalysis (NCR) Effort. NCR consisted of 1) algorithm improvement (AI), where CZCS processing algorithms were improved using modernized atmospheric correction and bio-optical algorithms, and 2) blending, where in situ data were incorporated into the CZCS AI to minimize residual errors. The results indicated major improvement over the previously available CZCS archive. Global spatial and seasonal patterns of NCR chlorophyll indicated remarkable correspondence with modern sensors, suggesting compatibility. The NCR permits quantitative analyses of interannual and interdecadal trends in global ocean chlorophyll.

Interannual Variability in Global Soil Respiration on a 0.5 Degree Grid Cell Basis (1980-1994)

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

Raich, J.W.

2003-09-15

We used a climate-driven regression model to develop spatially resolved estimates of soil-CO{sub 2} emissions from the terrestrial land surface for each month from January 1980 to December 1994, to evaluate the effects of interannual variations in climate on global soil-to-atmosphere CO{sub 2} fluxes. The mean annual global soil-CO{sub 2} flux over this 15-y period was estimated to be 80.4 (range 79.3-81.8) Pg C. Monthly variations in global soil-CO{sub 2} emissions followed closely the mean temperature cycle of the Northern Hemisphere. Globally, soil-CO{sub 2} emissions reached their minima in February and peaked in July and August. Tropical and subtropical evergreenmore » broad-leaved forests contributed more soil-derived CO{sub 2} to the atmosphere than did any other vegetation type ({approx}30% of the total) and exhibited a biannual cycle in their emissions. Soil-CO{sub 2} emissions in other biomes exhibited a single annual cycle that paralleled the seasonal temperature cycle. Interannual variability in estimated global soil-CO{sub 2} production is substantially less than is variability in net carbon uptake by plants (i.e., net primary productivity). Thus, soils appear to buffer atmospheric CO{sub 2} concentrations against far more dramatic seasonal and interannual differences in plant growth. Within seasonally dry biomes (savannas, bushlands, and deserts), interannual variability in soil-CO{sub 2} emissions correlated significantly with interannual differences in precipitation. At the global scale, however, annual soil-CO{sub 2} fluxes correlated with mean annual temperature, with a slope of 3.3 PgCY{sup -1} per degree Celsius. Although the distribution of precipitation influences seasonal and spatial patterns of soil-CO{sub 2} emissions, global warming is likely to stimulate CO{sub 2} emissions from soils.« less

Interannual Variability in Global Soil Respiration on a 0.5 Degree Grid Cell Basis (1980-1994)

DOE Data Explorer

Raich, James W. [Iowa State University, Ames, IA (USA); Potter, Christopher S. [NASA Ames Research Center (ARC), Moffett Field, Mountain View, CA (United States); Bhagawat, Dwipen [Iowa State Univ., Ames, IA (United States); Olson, L. M. [CDIAC, Oak Ridge National Laboratory, Oak Ridge, TN

2003-08-01

The Principal Investigators used a climate-driven regression model to develop spatially resolved estimates of soil-CO2 emissions from the terrestrial land surface for each month from January 1980 to December 1994, to evaluate the effects of interannual variations in climate on global soil-to-atmosphere CO2 fluxes. The mean annual global soil-CO2 flux over this 15-y period was estimated to be 80.4 (range 79.3-81.8) Pg C. Monthly variations in global soil-CO2 emissions followed closely the mean temperature cycle of the Northern Hemisphere. Globally, soil-CO2 emissions reached their minima in February and peaked in July and August. Tropical and subtropical evergreen broad-leaved forests contributed more soil-derived CO2 to the atmosphere than did any other vegetation type (~30% of the total) and exhibited a biannual cycle in their emissions. Soil-CO2 emissions in other biomes exhibited a single annual cycle that paralleled the seasonal temperature cycle. Interannual variability in estimated global soil-CO2 production is substantially less than is variability in net carbon uptake by plants (i.e., net primary productivity). Thus, soils appear to buffer atmospheric CO2 concentrations against far more dramatic seasonal and interannual differences in plant growth. Within seasonally dry biomes (savannas, bushlands, and deserts), interannual variability in soil-CO2 emmissions correlated significantly with interannual differences in precipitation. At the global scale, however, annual soil-CO2 fluxes correlated with mean annual temperature, with a slope of 3.3 PgCY-1 per degree Celsius. Although the distribution of precipitation influences seasonal and spatial patterns of soil-CO2 emissions, global warming is likely to stimulate CO2 emissions from soils.

Dual temperature effects on oxygen isotopic ratio of shallow-water coral skeleton: Consequences on seasonal and interannual records

NASA Astrophysics Data System (ADS)

Juillet-Leclerc, A.; Reynaud, S.

2009-04-01

Oxygen isotopic ratio from coral skeleton is regarded for a long time as promising climate archives at seasonal scale. Although in isotopic disequilibrium relative to seawater, it is supposed to obey to the isotope thermometer. Indeed, coral oxygen isotopic records are strongly temperature dependent, but d18O-temperature calibrations derived from different corals are highly variable. The isotope thermometer assumption does not take into account vital effects due to biogenic origin of the mineral. Corals are animals living in symbiosis with algae (zooxanthellae). Interactions between symbiont photosynthesis and coral skeleton carbonation have been abundantly observed but they remain poorly understood and the effects of photosynthesis on coral growth and skeleton oxygen ratio are ignored. Coral cultured under two light conditions enabled to relate metabolic parameters and oxygen isotopic variability with photosynthetic activity. By examining responses provided by each colony they revealed that photosynthesis significantly affected d18O, by an opposite sense compared with the sole temperature influence. Since temperature and light changes are associated during seasonal variations, this complicates the interpretation of seasonal record. Additionally, this complexity is amplified because photosynthetic activity is also directly impacted by temperature variability. Thus, the annual isotopic amplitude due to the "physical" temperature influence is partly compensated through photosynthesis. Similar opposite effect is also shown by extension rate of the cultured colonies. First, we will examine and quantify consequences of photosynthesis on growth rate and oxygen isotopic signature, from cultured corals. Second, we will consider the consequences of this vital effect on data series, at seasonal and interannual time scales.

Interannual Modulation of Northern Hemisphere Winter Storm Tracks by the QBO

NASA Astrophysics Data System (ADS)

Wang, Jiabao; Kim, Hye-Mi; Chang, Edmund K. M.

2018-03-01

Storm tracks, defined as the preferred regions of extratropical synoptic-scale disturbances, have remarkable impacts on global weather and climate systems. Causes of interannual storm track variation have been investigated mostly from a troposphere perspective. As shown in this study, Northern Hemisphere winter storm tracks are significantly modulated by the tropical stratosphere through the quasi-biennial oscillation (QBO). The North Pacific storm track shifts poleward during the easterly QBO winters associated with a dipole change in the eddy refraction and baroclinicity. The North Atlantic storm track varies vertically with a downward shrinking (upward expansion) in easterly (westerly) QBO winters associated with the change of the tropopause height. These results not only fill the knowledge gap of QBO-storm track relationship but also suggest a potential route to improve the seasonal prediction of extratropical storm activities owing to the high predictability of the QBO.

Understanding seasonal variability of uncertainty in hydrological prediction

NASA Astrophysics Data System (ADS)

Li, M.; Wang, Q. J.

2012-04-01

Understanding uncertainty in hydrological prediction can be highly valuable for improving the reliability of streamflow prediction. In this study, a monthly water balance model, WAPABA, in a Bayesian joint probability with error models are presented to investigate the seasonal dependency of prediction error structure. A seasonal invariant error model, analogous to traditional time series analysis, uses constant parameters for model error and account for no seasonal variations. In contrast, a seasonal variant error model uses a different set of parameters for bias, variance and autocorrelation for each individual calendar month. Potential connection amongst model parameters from similar months is not considered within the seasonal variant model and could result in over-fitting and over-parameterization. A hierarchical error model further applies some distributional restrictions on model parameters within a Bayesian hierarchical framework. An iterative algorithm is implemented to expedite the maximum a posterior (MAP) estimation of a hierarchical error model. Three error models are applied to forecasting streamflow at a catchment in southeast Australia in a cross-validation analysis. This study also presents a number of statistical measures and graphical tools to compare the predictive skills of different error models. From probability integral transform histograms and other diagnostic graphs, the hierarchical error model conforms better to reliability when compared to the seasonal invariant error model. The hierarchical error model also generally provides the most accurate mean prediction in terms of the Nash-Sutcliffe model efficiency coefficient and the best probabilistic prediction in terms of the continuous ranked probability score (CRPS). The model parameters of the seasonal variant error model are very sensitive to each cross validation, while the hierarchical error model produces much more robust and reliable model parameters. Furthermore, the result of the

Interannual Variability of Snow and Ice and Impact on the Carbon Cycle

NASA Technical Reports Server (NTRS)

Yung, Yuk L.

2004-01-01

The goal of this research is to assess the impact of the interannual variability in snow/ice using global satellite data sets acquired in the last two decades. This variability will be used as input to simulate the CO2 interannual variability at high latitudes using a biospheric model. The progress in the past few years is summarized as follows: 1) Albedo decrease related to spring snow retreat; 2) Observed effects of interannual summertime sea ice variations on the polar reflectance; 3) The Northern Annular Mode response to Arctic sea ice loss and the sensitivity of troposphere-stratosphere interaction; 4) The effect of Arctic warming and sea ice loss on the growing season in northern terrestrial ecosystem.

Seasonal, interannual, and long-term variabilities in biomass burning activity over South Asia.

PubMed

Bhardwaj, P; Naja, M; Kumar, R; Chandola, H C

2016-03-01

The seasonal, interannual, and long-term variations in biomass burning activity and related emissions are not well studied over South Asia. In this regard, active fire location retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS), the retrievals of aerosol optical depth (AOD) from MODIS Terra, and tropospheric column NO2 from Ozone Monitoring Instrument (OMI) are used to understand the effects of biomass burning on the tropospheric pollution loadings over South Asia during 2003-2013. Biomass burning emission estimates from Global Fire Emission Database (GFED) and Global Fire Assimilation System (GFAS) are also used to quantify uncertainties and regional discrepancies in the emissions of carbon monoxide (CO), nitrogen oxide (NOx), and black carbon (BC) due to biomass burning in South Asia. In the Asian continent, the frequency of fire activity is highest over Southeast Asia, followed by South Asia and East Asia. The biomass burning activity in South Asia shows a distinct seasonal cycle that peaks during February-May with some differences among four (north, central, northeast, and south) regions in India. The annual biomass burning activity in north, central, and south regions shows an increasing tendency, particularly after 2008, while a decrease is seen in northeast region during 2003-2013. The increase in fire counts over the north and central regions contributes 24 % of the net enhancement in fire counts over South Asia. MODIS AOD and OMI tropospheric column NO2 retrievals are classified into high and low fire activity periods and show that biomass burning leads to significant enhancement in tropospheric pollution loading over both the cropland and forest regions. The enhancement is much higher (110-176 %) over the forest region compared to the cropland (34-62 %) region. Further efforts are required to understand the implications of biomass burning on the regional air quality and climate of South Asia.

Prediction of seasonal runoff in ungauged basins

USDA-ARS?s Scientific Manuscript database

Many regions of the world experience strong seasonality in climate (i.e. precipitation and temperature), and strong seasonal runoff variability. Predictable patterns in seasonal water availability are of significant benefit to society because they allow reliable planning and infrastructure developme...

On the Vertical Structure of Seasonal, Interannual and Intraseasonal Flows

DTIC Science & Technology

1992-12-01

regions. Extensive use is made of a primitive equation (PE) model, as a diagnostic tool, to explore the extent to which tropical heating might influence ...vertical modes, while Wiin-Nielsen (1971a and b) studied the time 2 behaviour of long waves for various vertical structures. More recent investigations...nonlinear three-leve PE model, are used to determine the influence of tropical heating on extratropica wave response. In Chapter 4, the interannual changes

Mechanisms for Seasonal and Interannual Sea Surface Salinity Variability in the Indian Ocean

NASA Astrophysics Data System (ADS)

Köhler, J.; Stammer, D.; Serra, N.; Bryan, F.

2016-12-01

Space-borne salinity data in the Indian Ocean are analyzed over the period 2000-2015 based on data from the European Space Agency's (ESA) "Soil Moisture and Ocean Salinity" (SMOS) and the National Aeronautical Space Agency's (NASA) "Aquarius/SAC-D" missions. The seasonal variability is the dominant mode of sea surface salinity (SSS) variability in the Indian Ocean, accounting for more than 50% of salinity variance. Through a combined analysis of the satellite and ARGO data, dominant forcing terms for seasonal salinity changes are identified. It is found, that E-P controls seasonal salinity tendency in the western Indian Ocean, where the ITCZ has a strong seasonal cycle. In contrast, Ekman advection is the dominant term in the northern and eastern equatorial Indian Ocean. The influence of vertical processes on the salinity tendency is enhanced in coastal upwelling regions and south of the equator due to mid-ocean upwelling. Jointly those processes can explain most of the observed seasonal cycle with a correlation of 0.85 and an RMS difference of 0.07/month. However, the detailed composition of driving terms depends on underlying data products. In general, our study confirms previous results from Lisan Yu (2011); however, in the eastern Indian Ocean contrasting results indicate the leading role of meridional Ekman advection to the seasonal salinity tendency instead of surface external forces due to precipitation. The inferred dominant salinity budget terms are confirmed by results obtained from a high resolution NCAR Core model run driven by NCEP forcing fields. From an EOF analysis of the salinity fields after substracting the annual and semiannual cycle we found that the first EOF mode explains more than 20% of salinity variance. The first principal component of SSS EOF is correlated with the Indian Ocean Dipole Mode Index. Nevertheless the EOF pattern shows a meridional tripole structure, while the IOD describes a zonal SST dipole (Saji et al, 1999).

Interannual covariability between actual evapotranspiration and PAL and GIMMS NDVIs of northern Asia

USGS Publications Warehouse

Suzuki, Rikie; Masuda, Kooiti; Dye, Dennis G.

2007-01-01

This study examined the covariability between interannual changes in the normalized difference vegetation index (NDVI) and actual evapotranspiration (ET). To reduce possible uncertainty in the NDVI time series, two NDVI datasets derived from Pathfinder AVHRR Land (PAL) data and the Global Inventory Monitoring and Modeling Studies (GIMMS) group were used. Analyses were conducted using data over northern Asia from 1982 to 2000. Interannual changes over 19 years in the PAL-NDVI and GIMMS-NDVI were compared with interannual changes in ET estimated from model-assimilated atmospheric data and gridded precipitation data. For both NDVI datasets, the annual maximum correlation with ET occurred in June, which is the beginning of the vegetation growing season. The PAL and GIMMS datasets showed a significant, positive correlation between interannual changes in the NDVI and ET over most of the vegetated land area in June. These results suggest that interannual changes in vegetation activity predominantly control interannual changes in ET in June. Based on analyses of interannual changes in temperature, precipitation, and the NDVI in June, the study area can be roughly divided into two regions, the warmth-dominated northernmost region and the wetness-dominated southern region, indicating that interannual changes in vegetation and the resultant interannual changes in ET are controlled by warmth and wetness in these two regions, respectively.

The NASA Energy and Water Cycle Extreme (NEWSE) Integration Project

NASA Technical Reports Server (NTRS)

House, P. R.; Lapenta, W.; Schiffer, R.

2008-01-01

Skillful predictions of water and energy cycle extremes (flood and drought) are elusive. To better understand the mechanisms responsible for water and energy extremes, and to make decisive progress in predicting these extremes, the collaborative NASA Energy and Water cycle Extremes (NEWSE) Integration Project, is studying these extremes in the U.S. Southern Great Plains (SGP) during 2006-2007, including their relationships with continental and global scale processes, and assessment of their predictability on multiple space and time scales. It is our hypothesis that an integrative analysis of observed extremes which reflects the current understanding of the role of SST and soil moisture variability influences on atmospheric heating and forcing of planetary waves, incorporating recently available global and regional hydro- meteorological datasets (i.e., precipitation, water vapor, clouds, etc.) in conjunction with advances in data assimilation, can lead to new insights into the factors that lead to persistent drought and flooding. We will show initial results of this project, whose goals are to provide an improved definition, attribution and prediction on sub-seasonal to interannual time scales, improved understanding of the mechanisms of decadal drought and its predictability, including the impacts of SST variability and deep soil moisture variability, and improved monitoring/attributions, with transition to applications; a bridging of the gap between hydrological forecasts and stakeholders (utilization of probabilistic forecasts, education, forecast interpretation for different sectors, assessment of uncertainties for different sectors, etc.).

Using the concept of Shannon's Entropy to evaluate impacts of climate extremes on interannual variability in ecosystem CO2 fluxes

NASA Astrophysics Data System (ADS)

Ma, S.; Baldocchi, D. D.

2016-12-01

Although interannual variability in ecosystem CO2 fluxes have been observed in the field and described with empirical or process-based models, we still lack tools for evaluating and comparing impacts of climate extremes or unusual biogeophysical events on the variability. We examined a 15-year-long dataset of net ecosystem exchange of CO2 (NEE) measured at a woody savanna and a grassland site in California from 2000 to 2015. We proposed a conceptual framework to quantify season contributions by computing relatively contributions of each season to annual anomalies of gross ecosystem productivity (GPP) and ecosystem respiration (Reco). According to the framework, we calculated the Shannon's Entropy for each year. The values of Shannon Entropy were higher in the year that variations in GPP and Reco were beyond predictions of empirical models established for the study site. We specifically examined the outliers compared to model predictions and concluded that the outliers were related to occurrences of unexpected biogeophysical events in those years. This study offers a new application of Shannon's Entropy in understanding complicated biophysical and ecological processes involved in ecosystem carbon cycling.

Relationships between interannual and intraseasonal variations of the Asian-western Pacific summer monsoon hindcasted by BCC_CSM1.1(m)

NASA Astrophysics Data System (ADS)

Liu, Xiangwen; Wu, Tongwen; Yang, Song; Li, Qiaoping; Cheng, Yanjie; Liang, Xiaoyun; Fang, Yongjie; Jie, Weihua; Nie, Suping

2014-09-01

Using hindcasts of the Beijing Climate Center Climate System Model, the relationships between interannual variability (IAV) and intraseasonal variability (ISV) of the Asian-western Pacific summer monsoon are diagnosed. Predictions show reasonable skill with respect to some basic characteristics of the ISV and IAV of the western North Pacific summer monsoon (WNPSM) and the Indian summer monsoon (ISM). However, the links between the seasonally averaged ISV (SAISV) and seasonal mean of ISM are overestimated by the model. This deficiency may be partially attributable to the overestimated frequency of long breaks and underestimated frequency of long active spells of ISV in normal ISM years, although the model is capable of capturing the impact of ISV on the seasonal mean by its shift in the probability of phases. Furthermore, the interannual relationships of seasonal mean, SAISV, and seasonally averaged long-wave variability (SALWV; i.e., the part with periods longer than the intraseasonal scale) of the WNPSM and ISM with SST and low-level circulation are examined. The observed seasonal mean, SAISV, and SALWV show similar correlation patterns with SST and atmospheric circulation, but with different details. However, the model presents these correlation distributions with unrealistically small differences among different scales, and it somewhat overestimates the teleconnection between monsoon and tropical central-eastern Pacific SST for the ISM, but underestimates it for the WNPSM, the latter of which is partially related to the too-rapid decrease in the impact of El Niño-Southern Oscillation with forecast time in the model.

Will seasonally dry tropical forests be sensitive or resistant to future changes in rainfall regimes?

DOE PAGES

Allen, Kara; Dupuy, Juan Manuel; Gei, Maria G.; ...

2017-02-03

Seasonally dry tropical forests (SDTF) are located in regions with alternating wet and dry seasons, with dry seasons that last several months or more. By the end of the 21st century, climate models predict substantial changes in rainfall regimes across these regions, but little is known about how individuals, species, and communities in SDTF will cope with the hotter, drier conditions predicted by climate models. In this review, we explore different rainfall scenarios that may result in ecological drought in SDTF through the lens of two alternative hypotheses: 1) these forests will be sensitive to drought because they are alreadymore » limited by water and close to climatic thresholds, or 2) they will be resistant/resilient to intra- and inter-annual changes in rainfall because they are adapted to predictable, seasonal drought. In our review of literature that spans microbial to ecosystem processes, a majority of the available studies suggests that increasing frequency and intensity of droughts in SDTF will likely alter species distributions and ecosystem processes. Though we conclude that SDTF will be sensitive to altered rainfall regimes, many gaps in the literature remain. Future research should focus on geographically comparative studies and well-replicated drought experiments that can provide empirical evidence to improve simulation models used to forecast SDTF responses to future climate change at coarser spatial and temporal scales.« less

Will seasonally dry tropical forests be sensitive or resistant to future changes in rainfall regimes?

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

Allen, Kara; Dupuy, Juan Manuel; Gei, Maria G.

Seasonally dry tropical forests (SDTF) are located in regions with alternating wet and dry seasons, with dry seasons that last several months or more. By the end of the 21st century, climate models predict substantial changes in rainfall regimes across these regions, but little is known about how individuals, species, and communities in SDTF will cope with the hotter, drier conditions predicted by climate models. In this review, we explore different rainfall scenarios that may result in ecological drought in SDTF through the lens of two alternative hypotheses: 1) these forests will be sensitive to drought because they are alreadymore » limited by water and close to climatic thresholds, or 2) they will be resistant/resilient to intra- and inter-annual changes in rainfall because they are adapted to predictable, seasonal drought. In our review of literature that spans microbial to ecosystem processes, a majority of the available studies suggests that increasing frequency and intensity of droughts in SDTF will likely alter species distributions and ecosystem processes. Though we conclude that SDTF will be sensitive to altered rainfall regimes, many gaps in the literature remain. Future research should focus on geographically comparative studies and well-replicated drought experiments that can provide empirical evidence to improve simulation models used to forecast SDTF responses to future climate change at coarser spatial and temporal scales.« less

Will seasonally dry tropical forests be sensitive or resistant to future changes in rainfall regimes?

NASA Astrophysics Data System (ADS)

Allen, Kara; Dupuy, Juan Manuel; Gei, Maria G.; Hulshof, Catherine; Medvigy, David; Pizano, Camila; Salgado-Negret, Beatriz; Smith, Christina M.; Trierweiler, Annette; Van Bloem, Skip J.; Waring, Bonnie G.; Xu, Xiangtao; Powers, Jennifer S.

2017-02-01

Seasonally dry tropical forests (SDTF) are located in regions with alternating wet and dry seasons, with dry seasons that last several months or more. By the end of the 21st century, climate models predict substantial changes in rainfall regimes across these regions, but little is known about how individuals, species, and communities in SDTF will cope with the hotter, drier conditions predicted by climate models. In this review, we explore different rainfall scenarios that may result in ecological drought in SDTF through the lens of two alternative hypotheses: 1) these forests will be sensitive to drought because they are already limited by water and close to climatic thresholds, or 2) they will be resistant/resilient to intra- and inter-annual changes in rainfall because they are adapted to predictable, seasonal drought. In our review of literature that spans microbial to ecosystem processes, a majority of the available studies suggests that increasing frequency and intensity of droughts in SDTF will likely alter species distributions and ecosystem processes. Though we conclude that SDTF will be sensitive to altered rainfall regimes, many gaps in the literature remain. Future research should focus on geographically comparative studies and well-replicated drought experiments that can provide empirical evidence to improve simulation models used to forecast SDTF responses to future climate change at coarser spatial and temporal scales.

Mechanisms Governing Interannual Variability of Stratosphere-to-Troposphere Ozone Transport

NASA Astrophysics Data System (ADS)

Albers, John R.; Perlwitz, Judith; Butler, Amy H.; Birner, Thomas; Kiladis, George N.; Lawrence, Zachary D.; Manney, Gloria L.; Langford, Andrew O.; Dias, Juliana

2018-01-01

Factors governing the strength and frequency of stratospheric ozone intrusions over the Pacific-North American region are considered for their role in modulating tropospheric ozone on interannual timescales. The strength of the association between two major modes of climate variability—the El Niño-Southern Oscillation (ENSO) and the Northern Annular Mode (NAM)—and the amount of ozone contained in stratospheric intrusions are tested in the context of two mechanisms that modulate stratosphere-to-troposphere transport (STT) of ozone: (StratVarO3) the winter season buildup of ozone abundances in the lowermost stratosphere (LMS) and (JetVar) Pacific jet and wave breaking variability during spring. In essence, StratVarO3 corresponds to variability in the amount of ozone per intrusion, while JetVar governs the frequency of intrusions. The resulting analysis, based on two different reanalysis products, suggests that StratVarO3 is more important than JetVar for driving interannual variations in STT of ozone over the Pacific-North American region. In particular, the abundance of ozone in the LMS at the end of winter is shown to be a robust indicator of the amount of ozone that will be contained in stratospheric intrusions during the ensuing spring. Additionally, it is shown that the overall strength of the winter season stratospheric NAM is a useful predictor of ozone intrusion strength. The results also suggest a nuanced relationship between the phase of ENSO and STT of ozone. While ENSO-related jet variability is associated with STT variability, it is wave breaking frequency rather than typical ENSO teleconnection patterns that is responsible for the ENSO-STT relationship.

Enhancement of seasonal prediction of East Asian summer rainfall related to the western tropical Pacific convection

NASA Astrophysics Data System (ADS)

Lee, D. Y.; Ahn, J. B.; Yoo, J. H.

2014-12-01

The prediction skills of climate model simulations in the western tropical Pacific (WTP) and East Asian region are assessed using the retrospective forecasts of seven state-of-the-art coupled models and their multi-model ensemble (MME) for boreal summers (June-August) during the period 1983-2005, along with corresponding observed and reanalyzed data. The prediction of summer rainfall anomalies in East Asia is difficult, while the WTP has a strong correlation between model prediction and observation. We focus on developing a new approach to further enhance the seasonal prediction skill for summer rainfall in East Asia and investigate the influence of convective activity in the WTP on East Asian summer rainfall. By analyzing the characteristics of the WTP convection, two distinct patterns associated with El Niño-Southern Oscillation (ENSO) developing and decaying modes are identified. Based on the multiple linear regression method, the East Asia Rainfall Index (EARI) is developed by using the interannual variability of the normalized Maritime continent-WTP indices (MPIs), as potentially useful predictors for rainfall prediction over East Asia, obtained from the above two main patterns. For East Asian summer rainfall, the EARI has superior performance to the East Asia summer monsoon index (EASMI) or each MP index (MPI). Therefore, the regressed rainfall from EARI also shows a strong relationship with the observed East Asian summer rainfall pattern. In addition, we evaluate the prediction skill of the East Asia reconstructed rainfall obtained by statistical-empirical approach using the cross-validated EARI from the individual models and their MME. The results show that the rainfalls reconstructed from simulations capture the general features of observed precipitation in East Asia quite well. This study convincingly demonstrates that rainfall prediction skill is considerably improved by using the statistical-empirical method compared to the dynamical models

Meridional Propagation of the MJO/ISO and Prediction of Off-equatorial Monsoon Variability

NASA Technical Reports Server (NTRS)

Wu, Man Li C.; Schubert, S.; Suarez, M.; Pegion, P.; Waliser, D.

2003-01-01

This study was examine the links between tropical heating, the Madden Julian Oscillation (MJO)/Intraseasonal Oscillation (ISO), and the off-equatorial monsoon development. We examine both observations and idealized "MJO heating" experiments employing the NASA Seasonal-Interannual Prediction Project (NSIPP) atmospheric general circulation model (AGCM). In the simulations, the model is forced by climatological SST and an idealized eastward propagating heating profile that is meant 'to mimic the canonical heating associated with the MJO in the Indian Ocean and western Pacific. The observational analysis highlights the strong link between the Indian summer monsoon and the tropical ISO/MJO activity and heating. Here we focus on the potential for skillful predictions of the monsoon on sub-seasonal time scales associated with the meridional propagation of the ISO/MJO. In particular, we show that the variability of the Indian summer monsoon lags behind the variability of tropical ISO/MJO heating by about 15 days when the tropical heating is around 60E and 90E. This feature of the ISO/MJO is reproduced in the AGCM experiments with the idealized eastward propagating MJO-like heating, suggesting that models with realistic ISO/MJO variability should provide useful skill of monsoon breaks and surges on sub-seasonal time scales.

Seasonal prediction of Indian summer monsoon rainfall in NCEP CFSv2: forecast and predictability error

NASA Astrophysics Data System (ADS)

Pokhrel, Samir; Saha, Subodh Kumar; Dhakate, Ashish; Rahman, Hasibur; Chaudhari, Hemantkumar S.; Salunke, Kiran; Hazra, Anupam; Sujith, K.; Sikka, D. R.

2016-04-01

A detailed analysis of sensitivity to the initial condition for the simulation of the Indian summer monsoon using retrospective forecast by the latest version of the Climate Forecast System version-2 (CFSv2) is carried out. This study primarily focuses on the tropical region of Indian and Pacific Ocean basin, with special emphasis on the Indian land region. The simulated seasonal mean and the inter-annual standard deviations of rainfall, upper and lower level atmospheric circulations and Sea Surface Temperature (SST) tend to be more skillful as the lead forecast time decreases (5 month lead to 0 month lead time i.e. L5-L0). In general spatial correlation (bias) increases (decreases) as forecast lead time decreases. This is further substantiated by their averaged value over the selected study regions over the Indian and Pacific Ocean basins. The tendency of increase (decrease) of model bias with increasing (decreasing) forecast lead time also indicates the dynamical drift of the model. Large scale lower level circulation (850 hPa) shows enhancement of anomalous westerlies (easterlies) over the tropical region of the Indian Ocean (Western Pacific Ocean), which indicates the enhancement of model error with the decrease in lead time. At the upper level circulation (200 hPa) biases in both tropical easterly jet and subtropical westerlies jet tend to decrease as the lead time decreases. Despite enhancement of the prediction skill, mean SST bias seems to be insensitive to the initialization. All these biases are significant and together they make CFSv2 vulnerable to seasonal uncertainties in all the lead times. Overall the zeroth lead (L0) seems to have the best skill, however, in case of Indian summer monsoon rainfall (ISMR), the 3 month lead forecast time (L3) has the maximum ISMR prediction skill. This is valid using different independent datasets, wherein these maximum skill scores are 0.64, 0.42 and 0.57 with respect to the Global Precipitation Climatology Project

Interannual, seasonal, and retrospective analysis of the methane and carbon dioxide budgets of a temperate peatland

NASA Astrophysics Data System (ADS)

Olson, D. M.; Griffis, T. J.; Noormets, A.; Kolka, R.; Chen, J.

2013-03-01

Three years (2009-2011) of near-continuous methane (CH4) and carbon dioxide (CO2) fluxes were measured with the eddy covariance (EC) technique at a temperate peatland located within the Marcell Experimental Forest, in northern Minnesota, USA. The peatland was a net source of CH4 and a net sink of CO2 in each year with annual carbon budgets of -26.8 (±18.7), -15.5 (±14.8), and -14.6 (±21.5) g C m-2 yr-1 for 2009-2011, respectively. Differences in the seasonal hydrometeorological conditions among the three study years were most pronounced during 2011, which was considerably warmer (+1.3°C) and wetter (+40 mm) than the 30 year average. The annual CH4 budget was +11.8 (±3.1), +12.2 (±3.0), and +24.9 (±5.6) g C m-2 yr-1 for the respective years and accounted for 23%-39% of the annual carbon budget. The larger CH4 emission in 2011 is attributed to significant warming of the peat column coupled with a high water table position throughout the entire growing season. Historical (1991-2011) CH4 emissions were estimated based on long-term hydrometeorological records and functional relationships derived from our 3 year field study. The predicted historical annual CH4 budget ranged from +7.8 to +15.2 (±2.7) g CH4-C m-2 yr-1. Recent (2007-2011) increases in temperature, precipitation, and rising water table at this site suggest that CH4 emissions have been increasing, but were generally greater from 1991 to 1999 when average soil temperature and precipitation were higher than in recent years. The global warming potential (considering CO2 and CH4) for this peatland was calculated based on a 100 year time horizon. In all three study years, the peatland had a net positive radiative forcing on climate and was greatest (+187 g C m-2) in 2011. The interannual variability in CH4 exchange at this site suggests high sensitivity to variations in hydrometeorological conditions.

Seasonal and interannual variability of atmospheric heat sources and moisture sinks as determined from NCEP/NCAR ranalysis. Part I

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

Yanai, Michio; Tomita, Tomohiko

1997-11-01

In this paper, an analysis of the heat and moisture budgets of the troposphere is revised and extended. The analysis is based on the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR) reanalysis from 1980 to 1994. The seasonal and interannual variability of heat sources and sinks and the nature of heating over various geographical locations is examined in detail. Results presented include global distributions of the 15-year mean of the vertically integrated heat source and moisture sink and the outgoing longwave radiation flux for northern winter and northern summer. A time series of monthlymore » mean anomalies of the apparent heat source, the apparent moisture sink, outgoing longwave radiation, sea surface temperature, and divergence at wind fields of 850 hPa and 200 hPa are presented for the equatorial Indian Ocean, the equatorial eastern Pacific Ocean, western Tibet, and eastern Tibet. In the equatorial Indian Ocean, short period oscillation is superimposed upon longer periods. Over the eastern Pacific, a longer periodicity is dominant and the variability of the heat source is very well correlated with similar variations of outgoing longwave radiation, sea surface temperature, and horizontal divergence. The high correlation with these variables suggests that anomalous heating is accompanied by intensified convective activity favored by warmer sea surface temperature. 13 refs., 5 figs.« less

Prediction of the Arctic Oscillation in Boreal Winter by Dynamical Seasonal Forecasting Systems

NASA Technical Reports Server (NTRS)

Kang, Daehyun; Lee, Myong-In; Im, Jungho; Kim, Daehyun; Kim, Hye-Mi; Kang, Hyun-Suk; Shubert, Siegfried D.; Arriba, Albertom; MacLachlan, Craig

2013-01-01

This study assesses the prediction skill of the boreal winter Arctic Oscillation (AO) in the state-of-the-art dynamical ensemble prediction systems (EPSs): the UKMO GloSea4, the NCEP CFSv2, and the NASA GEOS-5. Long-term reforecasts made with the EPSs are used to evaluate representations of the AO, and to examine skill scores for the deterministic and probabilistic forecast of the AO index. The reforecasts reproduce the observed changes in the large-scale patterns of the Northern Hemispheric surface temperature, upper-level wind, and precipitation according to the AO phase. Results demonstrate that all EPSs have better prediction skill than the persistence prediction for lead times up to 3-month, suggesting a great potential for skillful prediction of the AO and the associated climate anomalies in seasonal time scale. It is also found that the deterministic and probabilistic forecast skill of the AO in the recent period (1997-2010) is higher than that in the earlier period (1983-1996).

CASSIA--a dynamic model for predicting intra-annual sink demand and interannual growth variation in Scots pine.

PubMed

Schiestl-Aalto, Pauliina; Kulmala, Liisa; Mäkinen, Harri; Nikinmaa, Eero; Mäkelä, Annikki

2015-04-01

The control of tree growth vs environment by carbon sources or sinks remains unresolved although it is widely studied. This study investigates growth of tree components and carbon sink-source dynamics at different temporal scales. We constructed a dynamic growth model 'carbon allocation sink source interaction' (CASSIA) that calculates tree-level carbon balance from photosynthesis, respiration, phenology and temperature-driven potential structural growth of tree organs and dynamics of stored nonstructural carbon (NSC) and their modifying influence on growth. With the model, we tested hypotheses that sink demand explains the intra-annual growth dynamics of the meristems, and that the source supply is further needed to explain year-to-year growth variation. The predicted intra-annual dimensional growth of shoots and needles and the number of cells in xylogenesis phases corresponded with measurements, whereas NSC hardly limited the growth, supporting the first hypothesis. Delayed GPP influence on potential growth was necessary for simulating the yearly growth variation, indicating also at least an indirect source limitation. CASSIA combines seasonal growth and carbon balance dynamics with long-term source dynamics affecting growth and thus provides a first step to understanding the complex processes regulating intra- and interannual growth and sink-source dynamics. © 2015 The Authors. New Phytologist © 2015 New Phytologist Trust.

Relationship Between the Global Electric Circuit and Electrified Cloud Parameters at Diurnal, Seasonal and Interannual Timescales

NASA Astrophysics Data System (ADS)

Lavigne, Thomas

In the early 1900's, J.W. Whipple began validating C.R. Wilson's Global Electric Circuit (GEC) hypothesis by correlating diurnal variations of global thunder days with diurnal variations of the fair weather electric field. This study applies 16+ years of Precipitation Feature (PF) data from the Tropical Rainfall Measuring Mission (TRMM), including lightning data from the Lightning Imaging Sensor (LIS), alongside 12-years of electric field measurements from Vostok, Antarctica to further examine this relationship. Joint diurnal-seasonal variations of the electric field are compared with PF parameters that are potentially related to the GEC. The flash rate and volume of 30 dBZ between -5°C and -35°C variables are shown to have the best direct relationship to the electric field, with r2 values of 0.67 and 0.62, respectively. However, the Coefficient of Variation (COV) of the flash rate (28%) and the electric field (12%), display relatively large differences in the spread of the variables. The volume of 30 dBZ between -5°C and -35°C shows a closer amplitude agreement to the variance of the electric field (COV=17%). Furthermore, these relationships are analyzed during two different phases of the El Nino Southern Oscillation (ENSO). Results show different seasonal-diurnal variations of the electric field during ENSO phases, with enhancements in the electric field between January through April at 16-24 UTC in La Nina years. In all, similar variations have been found in the fair weather electric field, and the variation of properties of global PFs with high potential of electrification at diurnal, seasonal, and interannual timescales. These confirm the dominant role of the global thunderclouds and electrified clouds in the global electric circuit.

Trophic status drives interannual variability in nesting numbers of marine turtles.

PubMed

Broderick, A C; Godley, B J; Hays, G C

2001-07-22

Large annual fluctuations are seen in breeding numbers in many populations of non-annual breeders. We examined the interannual variation in nesting numbers of populations of green (Chelonia mydas) (n = 16 populations), loggerhead (Caretta caretta) (n = 10 populations), leatherback (Dermochelys coriacea) (n = 9 populations) and hawksbill turtles (Eretmochelys imbricata) (n = 10 populations). Interannual variation was greatest in the green turtle. When comparing green and loggerhead turtles nesting in Cyprus we found that green turtles were more likely to change the interval between laying seasons and showed greater variation in the number of clutches laid in a season. We suggest that these differences are driven by the varying trophic statuses of the different species. Green turtles are herbivorous, feeding on sea grasses and macro-algae, and this primary production will be more tightly coupled with prevailing environmental conditions than the carnivorous diet of the loggerhead turtle.

Phytoplankton dynamics in relation to seasonal variability and upwelling and relaxation patterns at the mouth of Ria de Aveiro (West Iberian Margin) over a four-year period

PubMed Central

Calado, António José; Moita, Maria Teresa; Cunha, Marina R.

2017-01-01

From June 2004 to December 2007, samples were weekly collected at a fixed station located at the mouth of Ria de Aveiro (West Iberian Margin). We examined the seasonal and inter-annual fluctuations in composition and community structure of the phytoplankton in relation to the main environmental drivers and assessed the influence of the oceanographic regime, namely changes in frequency and intensity of upwelling events, over the dynamics of the phytoplankton assemblage. The samples were consistently handled and a final subset of 136 OTUs (taxa with relative abundance > 0.01%) was subsequently submitted to various multivariate analyses. The phytoplankton assemblage showed significant changes at all temporal scales but with an overriding importance of seasonality over longer- (inter-annual) or shorter-term fluctuations (upwelling-related). Sea-surface temperature, salinity and maximum upwelling index were retrieved as the main driver of seasonal change. Seasonal signal was most evident in the fluctuations of chlorophyll a concentration and in the high turnover from the winter to spring phytoplankton assemblage. The seasonal cycle of production and succession was disturbed by upwelling events known to disrupt thermal stratification and induce changes in the phytoplankton assemblage. Our results indicate that both the frequency and intensity of physical forcing were important drivers of such variability, but the outcome in terms of species composition was highly dependent on the available local pool of species and the timing of those events in relation to the seasonal cycle. We conclude that duration, frequency and intensity of upwelling events, which vary seasonally and inter-annually, are paramount for maintaining long-term phytoplankton diversity likely by allowing unstable coexistence and incorporating species turnover at different scales. Our results contribute to the understanding of the complex mechanisms of coastal phytoplankton dynamics in relation to changing

Phytoplankton dynamics in relation to seasonal variability and upwelling and relaxation patterns at the mouth of Ria de Aveiro (West Iberian Margin) over a four-year period.

PubMed

Vidal, Tânia; Calado, António José; Moita, Maria Teresa; Cunha, Marina R

2017-01-01

From June 2004 to December 2007, samples were weekly collected at a fixed station located at the mouth of Ria de Aveiro (West Iberian Margin). We examined the seasonal and inter-annual fluctuations in composition and community structure of the phytoplankton in relation to the main environmental drivers and assessed the influence of the oceanographic regime, namely changes in frequency and intensity of upwelling events, over the dynamics of the phytoplankton assemblage. The samples were consistently handled and a final subset of 136 OTUs (taxa with relative abundance > 0.01%) was subsequently submitted to various multivariate analyses. The phytoplankton assemblage showed significant changes at all temporal scales but with an overriding importance of seasonality over longer- (inter-annual) or shorter-term fluctuations (upwelling-related). Sea-surface temperature, salinity and maximum upwelling index were retrieved as the main driver of seasonal change. Seasonal signal was most evident in the fluctuations of chlorophyll a concentration and in the high turnover from the winter to spring phytoplankton assemblage. The seasonal cycle of production and succession was disturbed by upwelling events known to disrupt thermal stratification and induce changes in the phytoplankton assemblage. Our results indicate that both the frequency and intensity of physical forcing were important drivers of such variability, but the outcome in terms of species composition was highly dependent on the available local pool of species and the timing of those events in relation to the seasonal cycle. We conclude that duration, frequency and intensity of upwelling events, which vary seasonally and inter-annually, are paramount for maintaining long-term phytoplankton diversity likely by allowing unstable coexistence and incorporating species turnover at different scales. Our results contribute to the understanding of the complex mechanisms of coastal phytoplankton dynamics in relation to changing

Using Empirical Orthogonal Teleconnections to Analyze Interannual Precipitation Variability in China

NASA Astrophysics Data System (ADS)

Stephan, C.; Klingaman, N. P.; Vidale, P. L.; Turner, A. G.; Demory, M. E.; Guo, L.

2017-12-01

Interannual rainfall variability in China affects agriculture, infrastructure and water resource management. A consistent and objective method, Empirical Orthogonal Teleconnection (EOT) analysis, is applied to precipitation observations over China in all seasons. Instead of maximizing the explained space-time variance, the method identifies regions in China that best explain the temporal variability in domain-averaged rainfall. It produces known teleconnections, that include high positive correlations with ENSO in eastern China in winter, along the Yangtze River in summer, and in southeast China during spring. New findings include that variability along the southeast coast in winter, in the Yangtze valley in spring, and in eastern China in autumn, are associated with extratropical Rossby wave trains. The same analysis is applied to six climate simulations of the Met Office Unified Model with and without air-sea coupling and at various horizontal resolutions of 40, 90 and 200 km. All simulations reproduce the observed patterns of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are all patterns associated with the observed physical mechanism. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. Finer resolution does not improve the fidelity of these patterns or their associated mechanisms. Evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient; attention must be paid to associated mechanisms.

Seasonal and interannual variation in the planktonic communities of the northeastern Chukchi Sea during the summer and early fall

NASA Astrophysics Data System (ADS)

Questel, Jennifer M.; Clarke, Cheryl; Hopcroft, Russell R.

2013-09-01

We analyzed the seasonal and interannual variability of the planktonic communities in a densely sampled region of the northeastern Chukchi Sea as part of a multidisciplinary ecosystem study from 2008 to 2010. Observations of chlorophyll-a, inorganic macronutrients, and zooplankton (using both 150-μm and 505-μm mesh nets) were made within two 900-NM 2 grids (Klondike and Burger) at high spatial resolution three times each in 2008 and 2009, with a third grid (Statoil) sampled twice in 2010. Sea-ice conditions prior to sampling varied notably during the study: seasonal sea ice retreat was earlier and sea-surface temperatures (SSTs) were warmer in 2009 than in 2008, whereas SSTs for 2010 were intermediate between the 2008 and 2009 values. Eighty taxonomic categories of zooplankton, including 11 meroplanktonic categories, were recorded, with the greatest diversity found within the copepods (25 species), followed by the cnidarians (11 species). All species are typical for the region and most are seeded from the Bering Sea. A seasonal progression of the community structure was apparent over each survey area and was likely influenced by temperature. Cold oceanographic conditions in 2008 likely slowed growth and development of the zooplankton, such that holozooplankton abundance averaged 2389 and 106 individuals m-3 and biomass averaged 10.5 and 8.3 mg DW m-3 in the 150- and 505-μm nets, respectively. An early phytoplankton bloom in 2009 apparently supported a zooplankton community of greater abundance, but moderate biomass, averaging 6842 and 189 individuals m-3, and 16.3 and 7.0 mg DW m-3 in the 150- and 505-μm nets, respectively. Highest zooplankton abundance and biomass values among the three years occurred in 2010: 7396 and 198 individuals m-3 and 102.9 and 33.5 mg DW m-3 in the 150- and 505-μm nets, respectively. Holozooplankton biomass changes were driven by increases in large-bodied, lipid-rich copepods. The contribution of meroplankton was substantial in this

NOAA-NASA Coastal Zone Color Scanner reanalysis effort.

PubMed

Gregg, Watson W; Conkright, Margarita E; O'Reilly, John E; Patt, Frederick S; Wang, Menghua H; Yoder, James A; Casey, Nancy W

2002-03-20

Satellite observations of global ocean chlorophyll span more than two decades. However, incompatibilities between processing algorithms prevent us from quantifying natural variability. We applied a comprehensive reanalysis to the Coastal Zone Color Scanner (CZCS) archive, called the National Oceanic and Atmospheric Administration and National Aeronautics and Space Administration (NOAA-NASA) CZCS reanalysis (NCR) effort. NCR consisted of (1) algorithm improvement (AI), where CZCS processing algorithms were improved with modernized atmospheric correction and bio-optical algorithms and (2) blending where in situ data were incorporated into the CZCS AI to minimize residual errors. Global spatial and seasonal patterns of NCR chlorophyll indicated remarkable correspondence with modern sensors, suggesting compatibility. The NCR permits quantitative analyses of interannual and interdecadal trends in global ocean chlorophyll.

Long-Term Seasonal and Interannual Patterns of Marine Mammal Strandings in Subtropical Western South Atlantic.

PubMed

Prado, Jonatas H F; Mattos, Paulo H; Silva, Kleber G; Secchi, Eduardo R

2016-01-01

Understanding temporal patterns of marine mammal occurrence is useful for establishing conservation strategies. We used a 38 yr-long dataset spanning 1976 to 2013 to describe temporal patterns and trends in marine mammal strandings along a subtropical stretch of the east coast of South America. This region is influenced by a transitional zone between tropical and temperate waters and is considered an important fishing ground off Brazil. Generalized Additive Models were used to evaluate the temporal stranding patterns of the most frequently stranded species. Forty species were documented in 12,540 stranding events. Franciscana (n = 4,574), South American fur seal, (n = 3,419), South American sea lion (n = 2,049), bottlenose dolphins (n = 293) and subantarctic fur seal (n = 219) were the most frequently stranded marine mammals. The seasonality of strandings of franciscana and bottlenose dolphin coincided with periods of higher fishing effort and strandings of South American and subantarctic fur seals with post-reproductive dispersal. For South American sea lion the seasonality of strandings is associated with both fishing effort and post-reproductive dispersal. Some clear seasonal patterns were associated with occurrence of cold- (e.g. subantarctic fur seal) and warm-water (e.g. rough-toothed dolphin) species in winter and summer, respectively. Inter-annual increases in stranding rate were observed for franciscana and South American fur seal and these are likely related to increased fishing effort and population growth, respectively. For subantarctic fur seal the stranding rate showed a slight decline while for bottlenose dolphin it remained steady. No significant year to year variation in stranding rate was observed for South American sea lion. The slight decrease in frequency of temperate/polar marine mammals and the increased occurrence of subtropical/tropical species since the late 1990s might be associated with environmental changes linked to climate change

Long-Term Seasonal and Interannual Patterns of Marine Mammal Strandings in Subtropical Western South Atlantic

PubMed Central

Prado, Jonatas H. F.; Mattos, Paulo H.; Silva, Kleber G.; Secchi, Eduardo R.

2016-01-01

Understanding temporal patterns of marine mammal occurrence is useful for establishing conservation strategies. We used a 38 yr-long dataset spanning 1976 to 2013 to describe temporal patterns and trends in marine mammal strandings along a subtropical stretch of the east coast of South America. This region is influenced by a transitional zone between tropical and temperate waters and is considered an important fishing ground off Brazil. Generalized Additive Models were used to evaluate the temporal stranding patterns of the most frequently stranded species. Forty species were documented in 12,540 stranding events. Franciscana (n = 4,574), South American fur seal, (n = 3,419), South American sea lion (n = 2,049), bottlenose dolphins (n = 293) and subantarctic fur seal (n = 219) were the most frequently stranded marine mammals. The seasonality of strandings of franciscana and bottlenose dolphin coincided with periods of higher fishing effort and strandings of South American and subantarctic fur seals with post-reproductive dispersal. For South American sea lion the seasonality of strandings is associated with both fishing effort and post-reproductive dispersal. Some clear seasonal patterns were associated with occurrence of cold- (e.g. subantarctic fur seal) and warm-water (e.g. rough-toothed dolphin) species in winter and summer, respectively. Inter-annual increases in stranding rate were observed for franciscana and South American fur seal and these are likely related to increased fishing effort and population growth, respectively. For subantarctic fur seal the stranding rate showed a slight decline while for bottlenose dolphin it remained steady. No significant year to year variation in stranding rate was observed for South American sea lion. The slight decrease in frequency of temperate/polar marine mammals and the increased occurrence of subtropical/tropical species since the late 1990s might be associated with environmental changes linked to climate change

Diel, Seasonal, and Interannual Variability in Abundance of Major Mesozooplankton Taxa in the Sargasso Sea as Related to Changing Environmental Parameters

NASA Astrophysics Data System (ADS)

Ivory, J.; Steinberg, D. K.; Latour, R. J.

2016-02-01

Temporal changes in mesozooplankton community structure affect planktonic food web interactions and biogeochemical cycling. Epipelagic mesozooplankton biomass in the Sargasso Sea has increased over the last two decades, with a related increase in zooplankton-mediated carbon export. Unknown, however, is what are the patterns and variability at different temporal scales (diel, seasonal, and interannual) in abundance of each major zooplankton taxon, and how do these patterns relate to physical and other environmental changes? We enumerated major taxa of mesozooplankton collected from monthly day and night net tows in the epipelagic zone at the Bermuda Atlantic Time-series Study (BATS) site in the Sargasso Sea from 1999 to 2010. Abundances of each taxon were determined using a ZooScan optical imaging system and microscopy. Generalized Linear Models (GLMs) were used to determine what environmental parameters best explain abundance of major taxa. We used annual averages to consider broader patterns. Zooplankton taxa with the most pronounced diel vertical migration (i.e., night:day ratio, N:D, >>1) included Limacina spp. pteropods (N:D=2.02), euphausiids (1.93), calanoid copepods (1.34), and heteropods (1.34). Taxa with a pronounced spring abundance peak included chaetognaths, larvaceans, and Limacina spp. pteropods, while harpacticoid copepods peaked in late summer, and calanoid copepods in both spring and summer. Environmental variables affecting abundance differed amongst taxa. For example, calanoid copepod density was highly influenced by the abundance of a major predator- chaetognaths. Multi-year densities of calanoid copepods and ostracods both increased with increasing Water Column Stratification Index and the Atlantic Multidecadal Oscillation (AMO) index, indicating warmer sea surface temperatures are favorable for these taxa. We discuss how these temporal patterns at different scales help predict effects of global climate change on the zooplankton community.

Interannual variability of cut-off low systems over the European sector: The role of blocking and the Northern Hemisphere circulation modes

NASA Astrophysics Data System (ADS)

Nieto, R.; Gimeno, L.; de La Torre, L.; Ribera, P.; Barriopedro, D.; García-Herrera, R.; Serrano, A.; Gordillo, A.; Redaño, A.; Lorente, J.

2007-04-01

An earlier developed multidecadal database of Northern Hemisphere cut-off low systems (COLs), covering a 41 years period (from 1958 to 1998) is used to study COLs interannual variability in the European sector (25°-47.5° N, 50° W-40° E) and the major factors controlling it. The study focus on the influence on COLs interannual variability, of larger scale phenomena such as blocking events and other main circulation modes defined over the Euro-Atlantic region. It is shown that there is a very large interannual variability in the COLs occurrence at the annual and seasonal scales, although without significant trends. The influence of larger scale phenomena is seasonal dependent, with the positive phase of the NAO favoring autumn COL development, while winter COL occurrence is mostly related to blocking events. During summer, the season when more COLs occur, no significant influences were found.

Interannual Rainfall Variability in the Tropical Atlantic Region

NASA Technical Reports Server (NTRS)

Gu, Guojun

2005-01-01

Rainfall variability on seasonal and interannual-to-interdecadal time scales in the tropical Atlantic is quantified using a 25-year (1979-2003) monthly rainfall dataset from the Global Precipitation Climatology Project (GPCP). The ITCZ measured by monthly rainfall between 15-37.5 deg W attains its peak as moving to the northernmost latitude (4-10 deg N) during July-September in which the most total rainfall is observed in the tropical Atlantic basin (17.5 deg S-22.5 deg N, 15 deg-37.5 deg W); the ITCZ becomes weakest during January-February with the least total rainfall as it moves to the south. In contrast, rainfall variability on interannual to interdecadal time scales shows a quite different seasonal preference. The most intense interannual variability occurs during March-May when the ITCZ tends to be near the equator and becomes weaker. Significant, negative correlations between the ITCZ strength and latitude anomalies are observed during boreal spring and early summer. The ITCZ strength and total rainfall amount in the tropical Atlantic basin are significantly modulated by the Pacific El Nino and the Atlantic equatorial mode (or Atlantic Nino) particularly during boreal spring and summer; whereas the impact of the Atlantic interhemispheric mode is considerably weaker. Regarding the anomalous latitudes of the ITCZ, the influence can come from both local, i.e., the Atlantic interhemispheric and equatorial modes, and remote forcings, i. e., El Nino; however, a direct impact of El Nino on the latitudes of the ITCZ can only be found during April-July, not in winter and early spring in which the warmest SST anomalies are usually observed in the equatorial Pacific.

Sub-seasonal to Seasonal Prediction in the Midst of Uncertainties: Recognizing the Music in What May Seem Like Noises Across this scale

NASA Astrophysics Data System (ADS)

Tiwari, P.; Kar, S. C.; Dey, S.; Mohanty, U. C.

2016-12-01

Sub-seasonal to Seasonal (S2S) prediction has long been considered a predictability desert and forecasting across this scale has received much less attention than medium and seasonal scale. Hoskins (2013) has suggested that there is an urgent need to understand the phenomena and structures that provide the potential sources of predictability across this scale. Therefore, after a problem on this scale and its associated implications in various sectors (for e.g. agriculture and food security, water and health), the question arises whether strategies of S2S prediction that have proved useful elsewhere can they be adapted to the North Indian plains and complex terrain of Himalayas as well? The aim of the present study is in three-folds. Firstly, it attempts to assess the sub seasonal to seasonal predictive skill of six general circulation models (GCMs) for a period of 31 years (1982-2012) and identify forecast windows of opportunity. Secondly, an attempt has been made to reproduce the information of the GCMs at higher resolution using both dynamical and statistical downscaling approaches along with bias correction. Thirdly, an attempt has been also made to use the S2S prediction for water cycle studies as lives of millions of people in North Indian plains depends on water availability from rivers of western Himalayan origin. Finally, the plausible reasons of model failure, potential sources of predictability across this scale and how S2S framework has played a key role in addressing such issues is highlighted. Key words: S2S, predictability, downscaling, water cycle.

Seasonal and interannual variabilities of coccolithophore blooms in the Bay of Biscay and the Celtic Sea observed from a 18-year time-series of non-algal Suspended Particulate Matter images

NASA Astrophysics Data System (ADS)

Perrot, Laurie; Gohin, Francis; Ruiz-Pino, Diana; Lampert, Luis

2016-04-01

Coccolithophores belong to the nano-phytoplankton size-class and produce CaCO3 scales called coccoliths which form the «shell» of the algae cell. Coccoliths are in the size range of a few μm and can also be detached from the cell in the water. This phytoplankton group has an ubiquitous distribution in all oceans but blooms only in some oceanic regions, like the North East Atlantic ocean and the South Western Atlantic (Patagonian Sea). At a global scale coccolithopore blooms are studied in regard of CaCO3 production and three potential feedback on climate change: albedo modification by the way of dimethylsulfide (DMS) production and atmospheric CO2 source by calcification and a CO2 pump by photosynthesis. As the oceans are more and more acidified by anthropogenic CO2 emissions, coccolithophores generally are expected to be negatively affected. However, recent studies have shown an increase in coccolithophore occurrence in the North Atlantic. A poleward expansion of the coccolithophore Emiliana Huxleyi has also been pointed out. By using a simplified fuzzy method applied to a 18-year time series of SeaWiFS (1998-2002) and MODIS (2003-2015) spectral reflectance, we assessed the seasonal and inter-annual variability of coccolithophore blooms in the vicinity of the shelf break in the Bay of Biscay and the Celtic Sea After identification of the coccolith pixels by applying the fuzzy method, the abundance of coccoliths is assessed from a database of non-algal Suspended Particulate Matter (SPM). Although a regular pattern in the phenology of the blooms is observed, starting south in April in Biscay and moving northwards until July in Ireland, there is a high seasonal and interannual variability in the extent of the blooms. Year 2014 shows very low concentrations of detached coccoliths (twice less than average) from space and anomalies point out the maximum level in 2001. Non-algal SPM, derived from a procedure defined for the continental shelf, appears to be well

Predicting the evolutionary dynamics of seasonal adaptation to novel climates in Arabidopsis thaliana

PubMed Central

Fournier-Level, Alexandre; Perry, Emily O.; Wang, Jonathan A.; Braun, Peter T.; Migneault, Andrew; Cooper, Martha D.; Metcalf, C. Jessica E.; Schmitt, Johanna

2016-01-01

Predicting whether and how populations will adapt to rapid climate change is a critical goal for evolutionary biology. To examine the genetic basis of fitness and predict adaptive evolution in novel climates with seasonal variation, we grew a diverse panel of the annual plant Arabidopsis thaliana (multiparent advanced generation intercross lines) in controlled conditions simulating four climates: a present-day reference climate, an increased-temperature climate, a winter-warming only climate, and a poleward-migration climate with increased photoperiod amplitude. In each climate, four successive seasonal cohorts experienced dynamic daily temperature and photoperiod variation over a year. We measured 12 traits and developed a genomic prediction model for fitness evolution in each seasonal environment. This model was used to simulate evolutionary trajectories of the base population over 50 y in each climate, as well as 100-y scenarios of gradual climate change following adaptation to a reference climate. Patterns of plastic and evolutionary fitness response varied across seasons and climates. The increased-temperature climate promoted genetic divergence of subpopulations across seasons, whereas in the winter-warming and poleward-migration climates, seasonal genetic differentiation was reduced. In silico “resurrection experiments” showed limited evolutionary rescue compared with the plastic response of fitness to seasonal climate change. The genetic basis of adaptation and, consequently, the dynamics of evolutionary change differed qualitatively among scenarios. Populations with fewer founding genotypes and populations with genetic diversity reduced by prior selection adapted less well to novel conditions, demonstrating that adaptation to rapid climate change requires the maintenance of sufficient standing variation. PMID:27140640

Predicting the evolutionary dynamics of seasonal adaptation to novel climates in Arabidopsis thaliana.

PubMed

Fournier-Level, Alexandre; Perry, Emily O; Wang, Jonathan A; Braun, Peter T; Migneault, Andrew; Cooper, Martha D; Metcalf, C Jessica E; Schmitt, Johanna

2016-05-17

Predicting whether and how populations will adapt to rapid climate change is a critical goal for evolutionary biology. To examine the genetic basis of fitness and predict adaptive evolution in novel climates with seasonal variation, we grew a diverse panel of the annual plant Arabidopsis thaliana (multiparent advanced generation intercross lines) in controlled conditions simulating four climates: a present-day reference climate, an increased-temperature climate, a winter-warming only climate, and a poleward-migration climate with increased photoperiod amplitude. In each climate, four successive seasonal cohorts experienced dynamic daily temperature and photoperiod variation over a year. We measured 12 traits and developed a genomic prediction model for fitness evolution in each seasonal environment. This model was used to simulate evolutionary trajectories of the base population over 50 y in each climate, as well as 100-y scenarios of gradual climate change following adaptation to a reference climate. Patterns of plastic and evolutionary fitness response varied across seasons and climates. The increased-temperature climate promoted genetic divergence of subpopulations across seasons, whereas in the winter-warming and poleward-migration climates, seasonal genetic differentiation was reduced. In silico "resurrection experiments" showed limited evolutionary rescue compared with the plastic response of fitness to seasonal climate change. The genetic basis of adaptation and, consequently, the dynamics of evolutionary change differed qualitatively among scenarios. Populations with fewer founding genotypes and populations with genetic diversity reduced by prior selection adapted less well to novel conditions, demonstrating that adaptation to rapid climate change requires the maintenance of sufficient standing variation.

Seasonal-to-interannual variation in biomass burning over the contiguous United States

NASA Astrophysics Data System (ADS)

Kim, K. M.; Lau, W. K. M.; Ichoku, I.; Pereira, G.; Darmenov, A.; da Silva, A. M., Jr.; Ellison, L.

2017-12-01

The intensity and frequency of wildfires are strongly affected by climatic factors, such as droughts and heat waves, which are governed by weather and climate dynamics. . Climatic impacts on wildfire and biomass burning can be complex involving not only natural variability, but also human activities. In this study, we examine the seasonality of occurrences and intensity of fires and climatic impact as a function of underlying biomes over the CONUS, using fire pixel data from MODIS instruments on-board Terra and Aqua. Results show that there are three distinct fire seasons, i.e., summer (June to August), spring (March-April), and Fall (September-October). In the evergreen needle leaf region where most fires occur, the fire season peaks in mid boreal summer. In this region, fires tend to start early (June) in southern US, and late (August) in northern US. Double peaks are distinctive features in grass land and crop land. Double peaks in crop land (spring and fall) appear to be associated with agricultural practices. However, the two peaks in grass land (spring and summer) are due to natural wildfires, associated with changes in seasonal weather pattern. To better understand the potential climatic impact on fire, we examine relationships between fire weather index (FWI) and fire pixel counts. Fire pixel count has a strong correlation with FWI in evergreen needle leaf forest, deciduous broad leaf forest, and open shrub land. However, no significant linear relations are found in crop land, grass land, and mixed forest. The implications of these findings, and possible impacts of atmospheric teleconnecon on the fire season in the CONUS will also be discussed.

Interannual rainfall variability over China in the MetUM GA6 and GC2 configurations

NASA Astrophysics Data System (ADS)

Stephan, Claudia Christine; Klingaman, Nicholas P.; Vidale, Pier Luigi; Turner, Andrew G.; Demory, Marie-Estelle; Guo, Liang

2018-05-01

Six climate simulations of the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against observations and reanalysis data for their ability to simulate the mean state and year-to-year variability of precipitation over China. To analyse the sensitivity to air-sea coupling and horizontal resolution, atmosphere-only and coupled integrations at atmospheric horizontal resolutions of N96, N216 and N512 (corresponding to ˜ 200, 90 and 40 km in the zonal direction at the equator, respectively) are analysed. The mean and interannual variance of seasonal precipitation are too high in all simulations over China but improve with finer resolution and coupling. Empirical orthogonal teleconnection (EOT) analysis is applied to simulated and observed precipitation to identify spatial patterns of temporally coherent interannual variability in seasonal precipitation. To connect these patterns to large-scale atmospheric and coupled air-sea processes, atmospheric and oceanic fields are regressed onto the corresponding seasonal mean time series. All simulations reproduce the observed leading pattern of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are the four leading patterns associated with the observed physical mechanisms. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. However, finer resolution does not improve the fidelity of these patterns or their associated mechanisms. This shows that evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient. The EOT analysis adds knowledge about coherent variability and associated mechanisms.

Evaluation of the North American Multi-Model Ensemble System for Monthly and Seasonal Prediction

NASA Astrophysics Data System (ADS)

Zhang, Q.

2014-12-01

Since August 2011, the real time seasonal forecasts of the U.S. National Multi-Model Ensemble (NMME) have been made on 8th of each month by NCEP Climate Prediction Center (CPC). The participating models were NCEP/CFSv1&2, GFDL/CM2.2, NCAR/U.Miami/COLA/CCSM3, NASA/GEOS5, IRI/ ECHAM-a & ECHAM-f in the first year of the real time NMME forecast. Two Canadian coupled models CMC/CanCM3 and CM4 joined in and CFSv1 and IRI's models dropped out in the second year. The NMME team at CPC collects monthly means of three variables, precipitation, temperature at 2m and sea surface temperature from each modeling center on a 1x1 global grid, removes systematic errors, makes the grand ensemble mean in equal weight for each model mean and probability forecast with equal weight for each member of each model. This provides the NMME forecast locked in schedule for the CPC operational seasonal and monthly outlook. The basic verification metrics of seasonal and monthly prediction of NMME are calculated as an evaluation of skill, including both deterministic and probabilistic forecasts for the 3-year real time (August, 2011- July 2014) period and the 30-year retrospective forecast (1982-2011) of the individual models as well as the NMME ensemble. The motivation of this study is to provide skill benchmarks for future improvements of the NMME seasonal and monthly prediction system. We also want to establish whether the real time and hindcast periods (used for bias correction in real time) are consistent. The experimental phase I of the project already supplies routine guidance to users of the NMME forecasts.

Assessment of NASA's Aircraft Noise Prediction Capability

NASA Technical Reports Server (NTRS)

Dahl, Milo D. (Editor)

2012-01-01

A goal of NASA s Fundamental Aeronautics Program is the improvement of aircraft noise prediction. This document provides an assessment, conducted from 2006 to 2009, on the current state of the art for aircraft noise prediction by carefully analyzing the results from prediction tools and from the experimental databases to determine errors and uncertainties and compare results to validate the predictions. The error analysis is included for both the predictions and the experimental data and helps identify where improvements are required. This study is restricted to prediction methods and databases developed or sponsored by NASA, although in many cases they represent the current state of the art for industry. The present document begins with an introduction giving a general background for and a discussion on the process of this assessment followed by eight chapters covering topics at both the system and the component levels. The topic areas, each with multiple contributors, are aircraft system noise, engine system noise, airframe noise, fan noise, liner physics, duct acoustics, jet noise, and propulsion airframe aeroacoustics.

Interannual variation, decadal trend, and future change in ozone outflow from East Asia

NASA Astrophysics Data System (ADS)

Zhu, Jia; Liao, Hong; Mao, Yuhao; Yang, Yang; Jiang, Hui

2017-03-01

We examine the past and future changes in the O3 outflow from East Asia using a global 3-D chemical transport model, GEOS-Chem. The simulations of Asian O3 outflow for 1986-2006 are driven by the assimilated GEOS-4 meteorological fields, and those for 2000-2050 are driven by the meteorological fields archived by the NASA Goddard Institute for Space Studies (GISS) general circulation model (GCM) 3 under the IPCC SRES A1B scenario. The evaluation of the model results against measurements shows that the GEOS-Chem model captures the seasonal cycles and interannual variations of tropospheric O3 concentrations fairly well with high correlation coefficients of 0.82-0.93 at four ground-based sites and 0.55-0.88 at two ozonesonde sites where observations are available. The increasing trends in surface-layer O3 concentrations in East Asia over the past 2 decades are captured by the model, although the modeled O3 trends have low biases. Sensitivity studies are conducted to examine the respective impacts of meteorological parameters and emissions on the variations in the outflow flux of O3. When both meteorological parameters and anthropogenic emissions varied from 1986-2006, the simulated Asian O3 outflow fluxes exhibited a statistically insignificant decadal trend; however, they showed large interannual variations (IAVs) with seasonal values of 4-9 % for the absolute percent departure from the mean (APDM) and an annual APDM value of 3.3 %. The sensitivity simulations indicated that the large IAVs in O3 outflow fluxes were mainly caused by variations in the meteorological conditions. The variations in meteorological parameters drove the IAVs in O3 outflow fluxes by altering the O3 concentrations over East Asia and by altering the zonal winds; the latter was identified to be the key factor, since the O3 outflow was highly correlated with zonal winds from 1986-2006. The simulations of the 2000-2050 changes show that the annual outflow flux of O3 will increase by 2.0, 7.9, and

Seasonal and interannual variability of carbon monoxide based on MOZAIC observations, MACC reanalysis, and model simulations over an urban site in India

NASA Astrophysics Data System (ADS)

Sheel, Varun; Sahu, L. K.; Kajino, M.; Deushi, M.; Stein, O.; Nedelec, P.

2014-07-01

The spatial and temporal variations of carbon monoxide (CO) are analyzed over a tropical urban site, Hyderabad (17°27'N, 78°28'E) in central India. We have used vertical profiles from the Measurement of ozone and water vapor by Airbus in-service aircraft (MOZAIC) aircraft observations, Monitoring Atmospheric Composition and Climate (MACC) reanalysis, and two chemical transport model simulations (Model for Ozone And Related Tracers (MOZART) and MRI global Chemistry Climate Model (MRI-CCM2)) for the years 2006-2008. In the lower troposphere, the CO mixing ratio showed strong seasonality, with higher levels (>300 ppbv) during the winter and premonsoon seasons associated with a stable anticyclonic circulation, while lower CO values (up to 100 ppbv) were observed in the monsoon season. In the planetary boundary layer (PBL), the seasonal distribution of CO shows the impact of both local meteorology and emissions. While the PBL CO is predominantly influenced by strong winds, bringing regional background air from marine and biomass burning regions, under calm conditions CO levels are elevated by local emissions. On the other hand, in the free troposphere, seasonal variation reflects the impact of long-range transport associated with the Intertropical Convergence Zone and biomass burning. The interannual variations were mainly due to transition from El Niño to La Niña conditions. The overall modified normalized mean biases (normalization based on the observed and model mean values) with respect to the observed CO profiles were lower for the MACC reanalysis than the MOZART and MRI-CCM2 models. The CO in the PBL region was consistently underestimated by MACC reanalysis during all the seasons, while MOZART and MRI-CCM2 show both positive and negative biases depending on the season.

Evaluate the seasonal cycle and interannual variability of carbon fluxes and the associated uncertainties using modeled and observed data

NASA Astrophysics Data System (ADS)

Zeng, F.; Collatz, G. J.; Ivanoff, A.

2013-12-01

We assessed the performance of the Carnegie-Ames-Stanford Approach - Global Fire Emissions Database (CASA-GFED3) terrestrial carbon cycle model in simulating seasonal cycle and interannual variability (IAV) of global and regional carbon fluxes and uncertainties associated with model parameterization. Key model parameters were identified from sensitivity analyses and their uncertainties were propagated through model processes using the Monte Carlo approach to estimate the uncertainties in carbon fluxes and pool sizes. Three independent flux data sets, the global gross primary productivity (GPP) upscaled from eddy covariance flux measurements by Jung et al. (2011), the net ecosystem exchange (NEE) estimated by CarbonTracker, and the eddy covariance flux observations, were used to evaluate modeled fluxes and the uncertainties. Modeled fluxes agree well with both Jung's GPP and CarbonTracker NEE in the amplitude and phase of seasonal cycle, except in the case of GPP in tropical regions where Jung et al. (2011) showed larger fluxes and seasonal amplitude. Modeled GPP IAV is positively correlated (p < 0.1) with Jung's GPP IAV except in the tropics and temperate South America. The correlations between modeled NEE IAV and CarbonTracker NEE IAV are weak at regional to continental scales but stronger when fluxes are aggregated to >40°N latitude. At regional to continental scales flux uncertainties were larger than the IAV in the fluxes for both Jung's GPP and CarbonTracker NEE. Comparisons with eddy covariance flux observations are focused on sites within regions and years of recorded large-scale climate anomalies. We also evaluated modeled biomass using other independent continental biomass estimates and found good agreement. From the comparisons we identify the strengths and weaknesses of the model to capture the seasonal cycle and IAV of carbon fluxes and highlight ways to improve model performance.

Skilful prediction of Sahel summer rainfall on inter-annual and multi-year timescales

PubMed Central

Sheen, K. L.; Smith, D. M.; Dunstone, N. J.; Eade, R.; Rowell, D. P.; Vellinga, M.

2017-01-01

Summer rainfall in the Sahel region of Africa exhibits one of the largest signals of climatic variability and with a population reliant on agricultural productivity, the Sahel is particularly vulnerable to major droughts such as occurred in the 1970s and 1980s. Rainfall levels have subsequently recovered, but future projections remain uncertain. Here we show that Sahel rainfall is skilfully predicted on inter-annual and multi-year (that is, >5 years) timescales and use these predictions to better understand the driving mechanisms. Moisture budget analysis indicates that on multi-year timescales, a warmer north Atlantic and Mediterranean enhance Sahel rainfall through increased meridional convergence of low-level, externally sourced moisture. In contrast, year-to-year rainfall levels are largely determined by the recycling rate of local moisture, regulated by planetary circulation patterns associated with the El Niño-Southern Oscillation. Our findings aid improved understanding and forecasting of Sahel drought, paramount for successful adaptation strategies in a changing climate. PMID:28541288

Changes in interannual climate sensitivities of terrestrial carbon fluxes during the 21st century predicted by CMIP5 Earth System Models

NASA Astrophysics Data System (ADS)

Liu, Yongwen; Wang, Tao; Huang, Mengtian; Yao, Yitong; Ciais, Philippe; Piao, Shilong

2016-03-01

Terrestrial carbon fluxes are sensitive to climate change, but the interannual climate sensitivity of the land carbon cycle can also change with time. We analyzed the changes in responses of net biome production (NBP), net primary production (NPP), and heterotrophic respiration (Rh) to interannual climate variations over the 21st century in the Earth System Models (ESMs) from the Coupled Model Intercomparison Project 5. Under Representative Concentration Pathway (RCP) 4.5, interannual temperature sensitivities of NBP (γTempNBP), NPP (γTempNPP), and Rh (γTempRh) remain relatively stable at global scale, yet with large differences among ESMs and spatial heterogeneity. Modeled γTempNPP and γTempRh appear to increase in parallel in boreal regions, resulting in unchanged γTempNBP. Tropical γTempNBP decreases in most models, due to decreasing γTempNPP and relatively stable γTempRh. Across models, the changes in γTempNBP can be mainly explained by changes in γTempNPP rather than changes in γTempRh, at both global and regional scales. Interannual precipitation sensitivities of global NBP (γPrecNBP), NPP (γPrecNPP), and Rh (γPrecRh) are predicted not to change significantly, with large differences among ESMs. Across models, the changes in γPrecNBP can be mainly explained by changes in γPrecNPP rather than changes in γPrecRh in temperate regions, but not in other regions. Changes in the interannual climate sensitivities of carbon fluxes are consistent across RCPs 4.5, 6.0, and 8.5 but larger in more intensive scenarios. More effort should be considered to improve terrestrial carbon flux responses to interannual climate variability, e.g., incorporating biogeochemical processes of nutrient limitation, permafrost dynamics, and microbial decomposition.

Seasonal Progression and Interannual Variability of Nutrient and Chlorophyll-a Distributions in the Northern Gulf of Alaska, 1998-2010

NASA Astrophysics Data System (ADS)

Trahanovsky, K.; Whitledge, T. E.

2016-02-01

We examined nutrient and chlorophyll-a (chl) concentrations from bottle samples collected from 0-50 m depth in the Northern Gulf of Alaska along the Seward Line transect on 56 cruises from 1998-2010. We computed monthly average concentrations of macronutrients (N, P, and Si) and chlorophyll-a by depth at four major stations along the transect to describe the regular seasonal progression of the nutricline and typical water column distributions of chlorophyll-a in this seasonally productive, downwelling coastal zone. The across-shelf transect displayed two different patterns of seasonal progression clearly associated with the Alaska Coastal Current (ACC) and Alaskan Stream (AS) current systems. The annual cycle of nutrient drawdown and replenishment is remarkably consistent from year to year within each system and is well correlated with chl measurements. The spring bloom begins earlier and nutrient depletion is sustained longer in the near-shore ACC then in the AS system centered over the shelf break. Chlorophyll-a concentrations frequently peak at 10-20m depth in both systems during July through October, as nutrients remain depleted in the top 10m. Subsurface nutrients (20 - 50 m depth) begin to recover between July and August and then experience a secondary drawdown between August and October, consistent with higher chl levels observed during the fall bloom. Interannual variability in the progression of the nutricline and the relative contribution of subsurface chl to total chl is presented. Physical data demonstrate increasing stratification in this region due to climate change; the implications for nutrient dynamics and primary production are discussed.

Seasonal and interannual variations in feeding station behavior of cattle: effects of sward and meteorological conditions.

PubMed

Hirata, M; Matsumoto, Y; Izumi, S; Soga, Y; Hirota, F; Tobisa, M

2015-04-01

A feeding station is the area of forage a grazing animal can reach without moving its forefeet. Grazing behavior can be divided into residence within feeding stations (with bites as benefits) and movement between feeding stations (with steps as costs). However, relatively little information has been reported on how grazing animals modify their feeding station behavior seasonally and interannually in response to varying environmental conditions. The feeding station behavior of beef cows (Japanese Black) stocked on a tropical grass pasture (bahiagrass dominant) was monitored for 4 years (2010 to 2013) in order to investigate the association of feeding station behavior with meteorological and sward conditions across the seasons and years. Mean air temperature during stocking often exceeded 30°C during summer months. A severe summer drought in 2013 decreased herbage mass and sward height of the pasture and increased nitrogen concentration of herbage from summer to autumn. A markedly high feeding station number per unit foraging time, low bite numbers per feeding station and a low bite rate were observed in summer 2013 compared with the other seasons and years. Bite number per feeding station was explained by a multiple regression equation, where sward height and dry matter digestibility of herbage had a positive effect, whereas air temperature during stocking had a negative effect (R 2=0.658, P<0.01). Feeding station number per minute was negatively correlated with bite number per feeding station (r=-0.838, P<0.001). It was interpreted that cows modified bite number per feeding station in response to the sward and meteorological conditions, and this largely determined the number of feeding stations the animals visited per minute. The results indicate potential value of bite number per feeding station as an indicator of daily intake in grazing animals, and an opportunity for livestock and pasture managers to control feeding station behavior of animals through

Orbit-spin coupling and the interannual variability of global-scale dust storm occurrence on Mars

NASA Astrophysics Data System (ADS)

Shirley, James H.; Mischna, Michael A.

2017-05-01

A new physical hypothesis predicts that a weak coupling of the orbital and rotational motions of extended bodies may give rise to a modulation of circulatory flows within their atmospheres. Driven cycles of intensification and relaxation of large-scale circulatory flows are predicted, with the phasing of these changes linked directly to the rate of change of the orbital angular momentum, dL/dt, with respect to inertial frames. We test the hypothesis that global-scale dust storms (GDS) on Mars may occur when periods of circulatory intensification (associated with positive and negative extrema of the dL/dt waveform) coincide with the southern summer dust storm season on Mars. The orbit-spin coupling hypothesis additionally predicts that the intervening 'transitional' periods, which are characterized by the disappearance and subsequent sign change of dL/dt, may be unfavorable for the occurrence of GDS, when they occur during the southern summer dust storm season. These hypotheses are strongly supported by comparisons between calculated dynamical time series of dL/dt and historic observations. All of the nine known global-scale dust storms on Mars took place during Mars years when circulatory intensification during the dust storm season is 'retrodicted' under the orbit-spin coupling hypothesis. None of the historic global-scale dust storms of our catalog occurred during transitional intervals. Orbit-spin coupling appears to play an important role in the excitation of the interannual variability of the atmospheric circulation of Mars.

Seasonal and Interannual Variability of the Arctic Sea Ice: A Comparison between AO-FVCOM and Observations

NASA Astrophysics Data System (ADS)

Zhang, Y.; Chen, C.; Beardsley, R. C.; Gao, G.; Qi, J.; Lin, H.

2016-02-01

A high-resolution (up to 2 km), unstructured-grid, fully ice-sea coupled Arctic Ocean Finite-Volume Community Ocean Model (AO-FVCOM) was used to simulate the Arctic sea ice over the period 1978-2014. Good agreements were found between simulated and observed sea ice extent, concentration, drift velocity and thickness, indicating that the AO-FVCOM captured not only the seasonal and interannual variability but also the spatial distribution of the sea ice in the Arctic in the past 37 years. Compared with other six Arctic Ocean models (ECCO2, GSFC, INMOM, ORCA, NAME and UW), the AO-FVCOM-simulated ice thickness showed a higher correlation coefficient and a smaller difference with observations. An effort was also made to examine the physical processes attributing to the model-produced bias in the sea ice simulation. The error in the direction of the ice drift velocity was sensitive to the wind turning angle; smaller when the wind was stronger, but larger when the wind was weaker. This error could lead to the bias in the near-surface current in the fully or partially ice-covered zone where the ice-sea interfacial stress was a major driving force.

Using Reanalysis Data for the Prediction of Seasonal Wind Turbine Power Losses Due to Icing

NASA Astrophysics Data System (ADS)

Burtch, D.; Mullendore, G. L.; Delene, D. J.; Storm, B.

2013-12-01

The Northern Plains region of the United States is home to a significant amount of potential wind energy. However, in winter months capturing this potential power is severely impacted by the meteorological conditions, in the form of icing. Predicting the expected loss in power production due to icing is a valuable parameter that can be used in wind turbine operations, determination of wind turbine site locations and long-term energy estimates which are used for financing purposes. Currently, losses due to icing must be estimated when developing predictions for turbine feasibility and financing studies, while icing maps, a tool commonly used in Europe, are lacking in the United States. This study uses the Modern-Era Retrospective Analysis for Research and Applications (MERRA) dataset in conjunction with turbine production data to investigate various methods of predicting seasonal losses (October-March) due to icing at two wind turbine sites located 121 km apart in North Dakota. The prediction of icing losses is based on temperature and relative humidity thresholds and is accomplished using three methods. For each of the three methods, the required atmospheric variables are determined in one of two ways: using industry-specific software to correlate anemometer data in conjunction with the MERRA dataset and using only the MERRA dataset for all variables. For each season, a percentage of the total expected generated power lost due to icing is determined and compared to observed losses from the production data. An optimization is performed in order to determine the relative humidity threshold that minimizes the difference between the predicted and observed values. Eight seasons of data are used to determine an optimal relative humidity threshold, and a further three seasons of data are used to test this threshold. Preliminary results have shown that the optimized relative humidity threshold for the northern turbine is higher than the southern turbine for all methods

Applications of NASA and NOAA Satellite Observations by NASA's Short-term Prediction Research and Transition (SPoRT) Center in Response to Natural Disasters

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Burks, Jason E.; McGrath, Kevin M.; Jedlovec, Gary J.

2012-01-01

NASA s Short-term Prediction Research and Transition (SPoRT) Center supports the transition of unique NASA and NOAA research activities to the operational weather forecasting community. SPoRT emphasizes real-time analysis and prediction out to 48 hours. SPoRT partners with NOAA s National Weather Service (NWS) Weather Forecast Offices (WFOs) and National Centers to improve current products, demonstrate future satellite capabilities and explore new data assimilation techniques. Recently, the SPoRT Center has been involved in several activities related to disaster response, in collaboration with NOAA s National Weather Service, NASA s Applied Sciences Disasters Program, and other partners.

Orbit-related sea level errors for TOPEX altimetry at seasonal to decadal timescales

NASA Astrophysics Data System (ADS)

Esselborn, Saskia; Rudenko, Sergei; Schöne, Tilo

2018-03-01

Interannual to decadal sea level trends are indicators of climate variability and change. A major source of global and regional sea level data is satellite radar altimetry, which relies on precise knowledge of the satellite's orbit. Here, we assess the error budget of the radial orbit component for the TOPEX/Poseidon mission for the period 1993 to 2004 from a set of different orbit solutions. The errors for seasonal, interannual (5-year), and decadal periods are estimated on global and regional scales based on radial orbit differences from three state-of-the-art orbit solutions provided by different research teams: the German Research Centre for Geosciences (GFZ), the Groupe de Recherche de Géodésie Spatiale (GRGS), and the Goddard Space Flight Center (GSFC). The global mean sea level error related to orbit uncertainties is of the order of 1 mm (8 % of the global mean sea level variability) with negligible contributions on the annual and decadal timescales. In contrast, the orbit-related error of the interannual trend is 0.1 mm yr-1 (27 % of the corresponding sea level variability) and might hamper the estimation of an acceleration of the global mean sea level rise. For regional scales, the gridded orbit-related error is up to 11 mm, and for about half the ocean the orbit error accounts for at least 10 % of the observed sea level variability. The seasonal orbit error amounts to 10 % of the observed seasonal sea level signal in the Southern Ocean. At interannual and decadal timescales, the orbit-related trend uncertainties reach regionally more than 1 mm yr-1. The interannual trend errors account for 10 % of the observed sea level signal in the tropical Atlantic and the south-eastern Pacific. For decadal scales, the orbit-related trend errors are prominent in a several regions including the South Atlantic, western North Atlantic, central Pacific, South Australian Basin, and the Mediterranean Sea. Based on a set of test orbits calculated at GFZ, the sources of the

Seasonal variation in parasite infection patterns of marine fish species from the Northern Wadden Sea in relation to interannual temperature fluctuations

NASA Astrophysics Data System (ADS)

Schade, Franziska M.; Raupach, Michael J.; Mathias Wegner, K.

2016-07-01

Marine environmental conditions are naturally changing throughout the year, affecting life cycles of hosts as well as parasites. In particular, water temperature is positively correlated with the development of many parasites and pathogenic bacteria, increasing the risk of infection and diseases during summer. Interannual temperature fluctuations are likely to alter host-parasite interactions, which may result in profound impacts on sensitive ecosystems. In this context we investigated the parasite and bacterial Vibrionaceae communities of four common small fish species (three-spined stickleback Gasterosteus aculeatus, Atlantic herring Clupea harengus, European sprat Sprattus sprattus and lesser sand eel Ammodytes tobianus) in the Northern Wadden Sea over a period of two years. Overall, we found significantly increased relative diversities of infectious species at higher temperature differentials. On the taxon-specific level some macroparasite species (trematodes, nematodes) showed a shift in infection peaks that followed the water temperatures of preceding months, whereas other parasite groups showed no effects of temperature differentials on infection parameters. Our results show that even subtle changes in seasonal temperatures may shift and modify the phenology of parasites as well as opportunistic pathogens that can have far reaching consequences for sensitive ecosystems.

Heavy Metals in the Atmosphere over the Northern Coast of Eurasia: Interannual Variations in Winter and Summer

NASA Astrophysics Data System (ADS)

Vinogradova, A. A.; Ivanova, Yu. A.

2017-12-01

Interannual variations in the level of anthropogenic contamination of the surface air in the northern areas of Russia are studied, which are related to a change in the direction of air mass transport. The transport of air and heavy metals to four sites located on territories of nature reserves on the coast of the Arctic Ocean (from the Kola Peninsula to a delta of the Lena River) in winter (January) and summer (July) is analyzed for 2000-2013. Indices of atmospheric circulation and data on the emission of pollutants into the atmosphere in cities and regions of Russia are involved in the analysis. Concentrations of seven heavy metals in the surface air are evaluated in the Arctic regions under study and their interannual, spatial, and seasonal variations are discussed. A strong interannual variability of atmospheric circulation differently influences the variations in the atmosphere contamination with different anthropogenic heavy metals in various areas of the north of Russia. The concentration ratios of heavy metals under study are different for each site in different years. The interannual and seasonal variations in the contamination level have maximum values for heavy metals arriving from most distant sources. Thus, the results of measuring the content of anthropogenic contaminants in the air of reference areas during one season or even one year should not serve a basis for longterm conclusions and forecasts. It would be also unjustified to make general conclusions on the contamination level of the environment from observation results for only one contaminant and/or only at a single site.

Global seasonal climate predictability in a two tiered forecast system: part I: boreal summer and fall seasons

NASA Astrophysics Data System (ADS)

Misra, Vasubandhu; Li, H.; Wu, Z.; DiNapoli, S.

2014-03-01

This paper shows demonstrable improvement in the global seasonal climate predictability of boreal summer (at zero lead) and fall (at one season lead) seasonal mean precipitation and surface temperature from a two-tiered seasonal hindcast forced with forecasted SST relative to two other contemporary operational coupled ocean-atmosphere climate models. The results from an extensive set of seasonal hindcasts are analyzed to come to this conclusion. This improvement is attributed to: (1) The multi-model bias corrected SST used to force the atmospheric model. (2) The global atmospheric model which is run at a relatively high resolution of 50 km grid resolution compared to the two other coupled ocean-atmosphere models. (3) The physics of the atmospheric model, especially that related to the convective parameterization scheme. The results of the seasonal hindcast are analyzed for both deterministic and probabilistic skill. The probabilistic skill analysis shows that significant forecast skill can be harvested from these seasonal hindcasts relative to the deterministic skill analysis. The paper concludes that the coupled ocean-atmosphere seasonal hindcasts have reached a reasonable fidelity to exploit their SST anomaly forecasts to force such relatively higher resolution two tier prediction experiments to glean further boreal summer and fall seasonal prediction skill.

Seasonal to Decadal Discharge Predictions: Dream, Reality, or Somewhere in Between?

NASA Astrophysics Data System (ADS)

Villarini, G.

2016-12-01

Rivers have always played a central role in human activities including transportation and the provision of freshwater resources for agriculture, industry, and household use. However, too much or too little water in our rivers can have profound societal and economic repercussions, affecting the livelihood of millions of people and resulting in billions of dollars in economic damage. Despite these profound impacts and the critical role that rivers have played in our society, the development of systems enabling skillful predictions of discharge with lead times ranging from several months to several years is still in its infancy. The availability of discharge predictions could have major impacts on a number of sectors and industries, from water resources management to disaster prevention, policy-making and transportation. In this presentation, I will use recent developments from my research group to discuss some of the advances that we have made towards answering the question: Are skillful seasonal to decadal discharge predictions a dream, a reality, or somewhere in between? Results are based on a statistical-dynamical prediction system providing probabilistic seasonal discharge forecasts across the central United States with lead times ranging from a few months to several years.

A seasonal hydrologic ensemble prediction system for water resource management

NASA Astrophysics Data System (ADS)

Luo, L.; Wood, E. F.

2006-12-01

A seasonal hydrologic ensemble prediction system, developed for the Ohio River basin, has been improved and expanded to several other regions including the Eastern U.S., Africa and East Asia. The prediction system adopts the traditional Extended Streamflow Prediction (ESP) approach, utilizing the VIC (Variable Infiltration Capacity) hydrological model as the central tool for producing ensemble prediction of soil moisture, snow and streamflow with lead times up to 6-month. VIC is forced by observed meteorology to estimate the hydrological initial condition prior to the forecast, but during the forecast period the atmospheric forcing comes from statistically downscaled, seasonal forecast from dynamic climate models. The seasonal hydrologic ensemble prediction system is currently producing realtime seasonal hydrologic forecast for these regions on a monthly basis. Using hindcasts from a 19-year period (1981-1999), during which seasonal hindcasts from NCEP Climate Forecast System (CFS) and European Union DEMETER project are available, we evaluate the performance of the forecast system over our forecast regions. The evaluation shows that the prediction system using the current forecast approach is able to produce reliable and accurate precipitation, soil moisture and streamflow predictions. The overall skill is much higher then the traditional ESP. In particular, forecasts based on multiple climate model forecast are more skillful than single model-based forecast. This emphasizes the significant need for producing seasonal climate forecast with multiple climate models for hydrologic applications. Forecast from this system is expected to provide very valuable information about future hydrologic states and associated risks for end users, including water resource management and financial sectors.

Impact of hindcast length on estimates of seasonal climate predictability.

PubMed

Shi, W; Schaller, N; MacLeod, D; Palmer, T N; Weisheimer, A

2015-03-16

It has recently been argued that single-model seasonal forecast ensembles are overdispersive, implying that the real world is more predictable than indicated by estimates of so-called perfect model predictability, particularly over the North Atlantic. However, such estimates are based on relatively short forecast data sets comprising just 20 years of seasonal predictions. Here we study longer 40 year seasonal forecast data sets from multimodel seasonal forecast ensemble projects and show that sampling uncertainty due to the length of the hindcast periods is large. The skill of forecasting the North Atlantic Oscillation during winter varies within the 40 year data sets with high levels of skill found for some subperiods. It is demonstrated that while 20 year estimates of seasonal reliability can show evidence of overdispersive behavior, the 40 year estimates are more stable and show no evidence of overdispersion. Instead, the predominant feature on these longer time scales is underdispersion, particularly in the tropics. Predictions can appear overdispersive due to hindcast length sampling errorLonger hindcasts are more robust and underdispersive, especially in the tropicsTwenty hindcasts are an inadequate sample size to assess seasonal forecast skill.

Potential Seasonal Predictability for Winter Storms over Europe

NASA Astrophysics Data System (ADS)

Wild, Simon; Befort, Daniel J.; Leckebusch, Gregor C.

2017-04-01

Reliable seasonal forecasts of strong extra-tropical cyclones and windstorms would have great social and economical benefits, as these events are the most costly natural hazards over Europe. In a previous study we have shown good agreement of spatial climatological distributions of extra-tropical cyclones and wind storms in state-of-the-art multi-member seasonal prediction systems with reanalysis. We also found significant seasonal prediction skill of extra-tropical cyclones and windstorms affecting numerous European countries. We continue this research by investigating the mechanisms and precursor conditions (primarily over the North Atlantic) on a seasonal time scale leading to enhanced extra-tropical cyclone activity and winter storm frequency over Europe. Our results regarding mechanisms show that an increased surface temperature gradient at the western edge of the North Atlantic can be related to enhanced winter storm frequency further downstream causing for example a greater number of storms over the British Isles, as observed in winter 2013-14.The so-called "Horseshoe Index", a SST tripole anomaly pattern over the North Atlantic in the summer months can also cause a higher number of winter storms over Europe in the subsequent winter. We will show results of AMIP-type sensitivity experiments using an AGCM (ECHAM5), supporting this hypothesis. Finally we will analyse whether existing seasonal forecast systems are able to capture these identified mechanisms and precursor conditions affecting the models' seasonal prediction skill.

A simple next-best alternative to seasonal predictions in Europe

NASA Astrophysics Data System (ADS)

Buontempo, Carlo; De Felice, Matteo

2016-04-01

In order to build a climate proof society, we need to learn how to best use the climate information we have. Having spent time and resources in developing complex numerical models has often blinded us on the value some of this information really has in the eyes of a decision maker. An effective way to assess this is to check the quality of the forecast (and its cost) to the quality of the forecast from a prediction system based on simpler assumption (and thus cheaper to run). Such a practice is common in marketing analysis where it is often referred to as the next-best alternative. As a way to facilitate such an analysis, climate service providers should always provide alongside the predictions a set of skill scores. These are usually based on climatological means, anomaly persistence or more recently multiple linear regressions. We here present an equally simple benchmark based on a Markov chain process locally trained at a monthly or seasonal time-scale. We demonstrate that in spite of its simplicity the model easily outperforms not only the standard benchmark but also most of the seasonal predictions system at least in EUROPE. We suggest that a benchmark of this kind could represent a useful next-best alternative for a number of users.

Climate predictability and prediction skill on seasonal time scales over South America from CHFP models

NASA Astrophysics Data System (ADS)

Osman, Marisol; Vera, C. S.

2017-10-01

This work presents an assessment of the predictability and skill of climate anomalies over South America. The study was made considering a multi-model ensemble of seasonal forecasts for surface air temperature, precipitation and regional circulation, from coupled global circulation models included in the Climate Historical Forecast Project. Predictability was evaluated through the estimation of the signal-to-total variance ratio while prediction skill was assessed computing anomaly correlation coefficients. Both indicators present over the continent higher values at the tropics than at the extratropics for both, surface air temperature and precipitation. Moreover, predictability and prediction skill for temperature are slightly higher in DJF than in JJA while for precipitation they exhibit similar levels in both seasons. The largest values of predictability and skill for both variables and seasons are found over northwestern South America while modest but still significant values for extratropical precipitation at southeastern South America and the extratropical Andes. The predictability levels in ENSO years of both variables are slightly higher, although with the same spatial distribution, than that obtained considering all years. Nevertheless, predictability at the tropics for both variables and seasons diminishes in both warm and cold ENSO years respect to that in all years. The latter can be attributed to changes in signal rather than in the noise. Predictability and prediction skill for low-level winds and upper-level zonal winds over South America was also assessed. Maximum levels of predictability for low-level winds were found were maximum mean values are observed, i.e. the regions associated with the equatorial trade winds, the midlatitudes westerlies and the South American Low-Level Jet. Predictability maxima for upper-level zonal winds locate where the subtropical jet peaks. Seasonal changes in wind predictability are observed that seem to be related to

Skill of ENSEMBLES seasonal re-forecasts for malaria prediction in West Africa

NASA Astrophysics Data System (ADS)

Jones, A. E.; Morse, A. P.

2012-12-01

This study examines the performance of malaria-relevant climate variables from the ENSEMBLES seasonal ensemble re-forecasts for sub-Saharan West Africa, using a dynamic malaria model to transform temperature and rainfall forecasts into simulated malaria incidence and verifying these forecasts against simulations obtained by driving the malaria model with General Circulation Model-derived reanalysis. Two subregions of forecast skill are identified: the highlands of Cameroon, where low temperatures limit simulated malaria during the forecast period and interannual variability in simulated malaria is closely linked to variability in temperature, and northern Nigeria/southern Niger, where simulated malaria variability is strongly associated with rainfall variability during the peak rain months.

Interannual variability of the annual cycle of the surface temperature in the NCAR-NCEP reanalysis over the Northern Atlantic

NASA Astrophysics Data System (ADS)

Tesouro, M.; Gimeno, L.; Añel, J. A.; de La Torre, L.; Nieto, R.; Ribera, P.; García, R.; Hernández, E.

2003-04-01

The seasonal cycle of the surface temperature in the Northern Atlantic was investigated with the aim of studying interannual variability. To know how seasonal cycle is influenced by main climate modes could be a powerful tool to improve our seasonal prediction abilities. Data consist of daily temperatures at 2 metres taken from the Climate Research Unit (University of East Anglic_UK) (www.cru.uea.ac.uk) corresponding to the region from 90 W to 90 E longitude and from 88.5 N to 21.9 N latitude and for the last 44 years. Daily data were adjusted to the following expression for each year: y=a+b*sin(((2*PI)/d)x+c) The amplitude of the wave and the first inflexion point were used as indicators of the seasonal cycle. Results show a negative correlation between the NAO index and the amplitude over Northern Europe and over Mexico and a positive correlation over Northern United States and Canada. They also show a negative correlation between the NAO index and the first inflexion point over Northern Europe.

Seasonal predictions for wildland fire severity

Treesearch

Shyh-Chin Chen; Haiganoush Preisler; Francis Fujioka; John W. Benoit; John O. Roads

2009-01-01

The National Fire Danger Rating System (NFDRS) indices deduced from the monthly to seasonal predictions of a meteorological climate model at 50-km grid space from January 1998 through December 2003 were used in conjunction with a probability model to predict the expected number of fire occurrences and large fires over the U.S. West. The short-term climate forecasts are...

Seasonal and interannual variability in along-slope oceanic properties off the US West Coast: Inferences from a high-resolution regional model

NASA Astrophysics Data System (ADS)

Kurapov, A. L.; Pelland, N. A.; Rudnick, D. L.

2017-07-01

A 6 year, 2009-2014 simulation using a 2 km horizontal resolution ocean circulation model of the Northeast Pacific coast is analyzed with focus on seasonal and interannual variability in along-slope subsurface oceanic properties. Specifically, the fields are sampled on the isopycnal surface σ=26.5 kg m-3 that is found between depths of 150 and 300 m below the ocean surface over the continental slope. The fields analyzed include the depth z26.5, temperature T26.5, along-slope current v26.5, and the average potential vorticity PV between σ = 26.5 and 26.25 kg m-3. Each field is averaged in the cross-shore direction over the continental slope and presented as a function of the alongshore coordinate and time. The seasonal cycle in z26.5 shows a coherent upwelling-downwelling pattern from Mexico to Canada propagating to the north with a speed of 0.5 m s-1. The anomalously deep (-20 m) z26.5 displacement in spring-summer 2014 is forced by the southern boundary condition at 24°N as a manifestation of an emerging strong El Niño. The seasonal cycle in T26.5 is most pronounced between 36°N and 53°N indicating that subarctic waters are replaced by warmer Californian waters in summer with the speed close 0.15 m s-1, which is consistent with earlier estimates of the undercurrent speed and also present v26.5 analyses. The seasonal patterns and anomalies in z26.5 and T26.5 find confirmation in available long-term glider and shipborne observations. The PV seasonality over the slope is qualitatively different to the south and north of the southern edge of Heceta Bank (43.9°N).

Global Weather Prediction and High-End Computing at NASA

NASA Technical Reports Server (NTRS)

Lin, Shian-Jiann; Atlas, Robert; Yeh, Kao-San

2003-01-01

We demonstrate current capabilities of the NASA finite-volume General Circulation Model an high-resolution global weather prediction, and discuss its development path in the foreseeable future. This model can be regarded as a prototype of a future NASA Earth modeling system intended to unify development activities cutting across various disciplines within the NASA Earth Science Enterprise.

Attribution of Large-Scale Climate Patterns to Seasonal Peak-Flow and Prospects for Prediction Globally

NASA Astrophysics Data System (ADS)

Lee, Donghoon; Ward, Philip; Block, Paul

2018-02-01

Flood-related fatalities and impacts on society surpass those from all other natural disasters globally. While the inclusion of large-scale climate drivers in streamflow (or high-flow) prediction has been widely studied, an explicit link to global-scale long-lead prediction is lacking, which can lead to an improved understanding of potential flood propensity. Here we attribute seasonal peak-flow to large-scale climate patterns, including the El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), North Atlantic Oscillation (NAO), and Atlantic Multidecadal Oscillation (AMO), using streamflow station observations and simulations from PCR-GLOBWB, a global-scale hydrologic model. Statistically significantly correlated climate patterns and streamflow autocorrelation are subsequently applied as predictors to build a global-scale season-ahead prediction model, with prediction performance evaluated by the mean squared error skill score (MSESS) and the categorical Gerrity skill score (GSS). Globally, fair-to-good prediction skill (20% ≤ MSESS and 0.2 ≤ GSS) is evident for a number of locations (28% of stations and 29% of land area), most notably in data-poor regions (e.g., West and Central Africa). The persistence of such relevant climate patterns can improve understanding of the propensity for floods at the seasonal scale. The prediction approach developed here lays the groundwork for further improving local-scale seasonal peak-flow prediction by identifying relevant global-scale climate patterns. This is especially attractive for regions with limited observations and or little capacity to develop flood early warning systems.

An Analysis of Inter-annual Variability and Uncertainty of Continental Surface Heat Fluxes

NASA Astrophysics Data System (ADS)

Huang, S. Y.; Deng, Y.; Wang, J.

2016-12-01

The inter-annual variability and the corresponding uncertainty of land surface heat fluxes during the first decade of the 21st century are re-evaluated at continental scale based on the heat fluxes estimated by the maximum entropy production (MEP) model. The MEP model predicted heat fluxes are constrained by surface radiation fluxes, automatically satisfy surface energy balance, and are independent of temperature/moisture gradient, wind speed, and roughness lengths. The surface radiation fluxes and temperature data from Clouds and the Earth's Radiant Energy System and the surface specific humidity data from Modern-Era Retrospective analysis for Research and Applications were used to reproduce the global surface heat fluxes with land-cover data from the NASA Energy and Water cycle Study (NEWS). Our analysis shows that the annual means of continental latent heat fluxes have increasing trends associated with increasing trends in surface net radiative fluxes. The sensible heat fluxes also have increasing trends over most continents except for South America. Ground heat fluxes have little trends. The continental-scale analysis of the MEP fluxes are compared with other existing global surface fluxes data products and the implications of the results for inter-annual to decadal variability of regional surface energy budget are discussed.

Predicting large wildfires across western North America by modeling seasonal variation in soil water balance.

PubMed

Waring, Richard H; Coops, Nicholas C

A lengthening of the fire season, coupled with higher temperatures, increases the probability of fires throughout much of western North America. Although regional variation in the frequency of fires is well established, attempts to predict the occurrence of fire at a spatial resolution <10 km 2 have generally been unsuccessful. We hypothesized that predictions of fires might be improved if depletion of soil water reserves were coupled more directly to maximum leaf area index (LAI max ) and stomatal behavior. In an earlier publication, we used LAI max and a process-based forest growth model to derive and map the maximum available soil water storage capacity (ASW max ) of forested lands in western North America at l km resolution. To map large fires, we used data products acquired from NASA's Moderate Resolution Imaging Spectroradiometers (MODIS) over the period 2000-2009. To establish general relationships that incorporate the major biophysical processes that control evaporation and transpiration as well as the flammability of live and dead trees, we constructed a decision tree model (DT). We analyzed seasonal variation in the relative availability of soil water ( fASW ) for the years 2001, 2004, and 2007, representing respectively, low, moderate, and high rankings of areas burned. For these selected years, the DT predicted where forest fires >1 km occurred and did not occur at ~100,000 randomly located pixels with an average accuracy of 69 %. Extended over the decade, the area predicted burnt varied by as much as 50 %. The DT identified four seasonal combinations, most of which included exhaustion of ASW during the summer as critical; two combinations involving antecedent conditions the previous spring or fall accounted for 86 % of the predicted fires. The approach introduced in this paper can help identify forested areas where management efforts to reduce fire hazards might prove most beneficial.

Real-time predictive seasonal influenza model in Catalonia, Spain

PubMed Central

Basile, Luca; Oviedo de la Fuente, Manuel; Torner, Nuria; Martínez, Ana; Jané, Mireia

2018-01-01

Influenza surveillance is critical to monitoring the situation during epidemic seasons and predictive mathematic models may aid the early detection of epidemic patterns. The objective of this study was to design a real-time spatial predictive model of ILI (Influenza Like Illness) incidence rate in Catalonia using one- and two-week forecasts. The available data sources used to select explanatory variables to include in the model were the statutory reporting disease system and the sentinel surveillance system in Catalonia for influenza incidence rates, the official climate service in Catalonia for meteorological data, laboratory data and Google Flu Trend. Time series for every explanatory variable with data from the last 4 seasons (from 2010–2011 to 2013–2014) was created. A pilot test was conducted during the 2014–2015 season to select the explanatory variables to be included in the model and the type of model to be applied. During the 2015–2016 season a real-time model was applied weekly, obtaining the intensity level and predicted incidence rates with 95% confidence levels one and two weeks away for each health region. At the end of the season, the confidence interval success rate (CISR) and intensity level success rate (ILSR) were analysed. For the 2015–2016 season a CISR of 85.3% at one week and 87.1% at two weeks and an ILSR of 82.9% and 82% were observed, respectively. The model described is a useful tool although it is hard to evaluate due to uncertainty. The accuracy of prediction at one and two weeks was above 80% globally, but was lower during the peak epidemic period. In order to improve the predictive power, new explanatory variables should be included. PMID:29513710

Network centrality and seasonality interact to predict lice load in a social primate

PubMed Central

Duboscq, Julie; Romano, Valeria; Sueur, Cédric; MacIntosh, Andrew J.J.

2016-01-01

Lice are socially-transmitted ectoparasites. Transmission depends upon their host’s degree of contact with conspecifics. While grooming facilitates ectoparasite transmission via body contact, it also constrains their spread through parasite removal. We investigated relations between parasite burden and sociality in female Japanese macaques following two opposing predictions: i) central females in contact/grooming networks harbour more lice, related to their numerous contacts; ii) central females harbour fewer lice, related to receiving more grooming. We estimated lice load non-invasively using the conspicuous louse egg-picking behaviour performed by macaques during grooming. We tested for covariation in several centrality measures and lice load, controlling for season, female reproductive state and dominance rank. Results show that the interaction between degree centrality (number of partners) and seasonality predicted lice load: females interacting with more partners had fewer lice than those interacting with fewer partners in winter and summer, whereas there was no relationship between lice load and centrality in spring and fall. This is counter to the prediction that increased contact leads to greater louse burden but fits the prediction that social grooming limits louse burden. Interactions between environmental seasonality and both parasite and host biology appeared to mediate the role of social processes in louse burden. PMID:26915589

Network centrality and seasonality interact to predict lice load in a social primate.

PubMed

Duboscq, Julie; Romano, Valeria; Sueur, Cédric; MacIntosh, Andrew J J

2016-02-26

Lice are socially-transmitted ectoparasites. Transmission depends upon their host's degree of contact with conspecifics. While grooming facilitates ectoparasite transmission via body contact, it also constrains their spread through parasite removal. We investigated relations between parasite burden and sociality in female Japanese macaques following two opposing predictions: i) central females in contact/grooming networks harbour more lice, related to their numerous contacts; ii) central females harbour fewer lice, related to receiving more grooming. We estimated lice load non-invasively using the conspicuous louse egg-picking behaviour performed by macaques during grooming. We tested for covariation in several centrality measures and lice load, controlling for season, female reproductive state and dominance rank. Results show that the interaction between degree centrality (number of partners) and seasonality predicted lice load: females interacting with more partners had fewer lice than those interacting with fewer partners in winter and summer, whereas there was no relationship between lice load and centrality in spring and fall. This is counter to the prediction that increased contact leads to greater louse burden but fits the prediction that social grooming limits louse burden. Interactions between environmental seasonality and both parasite and host biology appeared to mediate the role of social processes in louse burden.

Assessment of a new seasonal to inter-annual operational Great Lakes water supply, water levels, and connecting channel flow forecasting system

NASA Astrophysics Data System (ADS)

Gronewold, A.; Fry, L. M.; Hunter, T.; Pei, L.; Smith, J.; Lucier, H.; Mueller, R.

2017-12-01

The U.S. Army Corps of Engineers (USACE) has recently operationalized a suite of ensemble forecasts of Net Basin Supply (NBS), water levels, and connecting channel flows that was developed through a collaboration among USACE, NOAA's Great Lakes Environmental Research Laboratory, Ontario Power Generation (OPG), New York Power Authority (NYPA), and the Niagara River Control Center (NRCC). These forecasts are meant to provide reliable projections of potential extremes in daily discharge in the Niagara and St. Lawrence Rivers over a long time horizon (5 years). The suite of forecasts includes eight configurations that vary by (a) NBS model configuration, (b) meteorological forcings, and (c) incorporation of seasonal climate projections through the use of weighting. Forecasts are updated on a weekly basis, and represent the first operational forecasts of Great Lakes water levels and flows that span daily to inter-annual horizons and employ realistic regulation logic and lake-to-lake routing. We will present results from a hindcast assessment conducted during the transition from research to operation, as well as early indications of success rates determined through operational verification of forecasts. Assessment will include an exploration of the relative skill of various forecast configurations at different time horizons and the potential for application to hydropower decision making and Great Lakes water management.

Effects of modeled tropical sea surface temperature variability on coral reef bleaching predictions

NASA Astrophysics Data System (ADS)

van Hooidonk, R.; Huber, M.

2012-03-01

Future widespread coral bleaching and subsequent mortality has been projected using sea surface temperature (SST) data derived from global, coupled ocean-atmosphere general circulation models (GCMs). While these models possess fidelity in reproducing many aspects of climate, they vary in their ability to correctly capture such parameters as the tropical ocean seasonal cycle and El Niño Southern Oscillation (ENSO) variability. Such weaknesses most likely reduce the accuracy of predicting coral bleaching, but little attention has been paid to the important issue of understanding potential errors and biases, the interaction of these biases with trends, and their propagation in predictions. To analyze the relative importance of various types of model errors and biases in predicting coral bleaching, various intra- and inter-annual frequency bands of observed SSTs were replaced with those frequencies from 24 GCMs 20th century simulations included in the Intergovernmental Panel on Climate Change (IPCC) 4th assessment report. Subsequent thermal stress was calculated and predictions of bleaching were made. These predictions were compared with observations of coral bleaching in the period 1982-2007 to calculate accuracy using an objective measure of forecast quality, the Peirce skill score (PSS). Major findings are that: (1) predictions are most sensitive to the seasonal cycle and inter-annual variability in the ENSO 24-60 months frequency band and (2) because models tend to understate the seasonal cycle at reef locations, they systematically underestimate future bleaching. The methodology we describe can be used to improve the accuracy of bleaching predictions by characterizing the errors and uncertainties involved in the predictions.

Effects of climatic factors and ecosystem responses on the inter-annual variability of evapotranspiration in a coniferous plantation in subtropical China.

PubMed

Xu, Mingjie; Wen, Xuefa; Wang, Huimin; Zhang, Wenjiang; Dai, Xiaoqin; Song, Jie; Wang, Yidong; Fu, Xiaoli; Liu, Yunfen; Sun, Xiaomin; Yu, Guirui

2014-01-01

Because evapotranspiration (ET) is the second largest component of the water cycle and a critical process in terrestrial ecosystems, understanding the inter-annual variability of ET is important in the context of global climate change. Eight years of continuous eddy covariance measurements (2003-2010) in a subtropical coniferous plantation were used to investigate the impacts of climatic factors and ecosystem responses on the inter-annual variability of ET. The mean and standard deviation of annual ET for 2003-2010 were 786.9 and 103.4 mm (with a coefficient of variation of 13.1%), respectively. The inter-annual variability of ET was largely created in three periods: March, May-June, and October, which are the transition periods between seasons. A set of look-up table approaches were used to separate the sources of inter-annual variability of ET. The annual ETs were calculated by assuming that (a) both the climate and ecosystem responses among years are variable (Vcli-eco), (b) the climate is variable but the ecosystem responses are constant (Vcli), and (c) the climate is constant but ecosystem responses are variable (Veco). The ETs that were calculated under the above assumptions suggested that the inter-annual variability of ET was dominated by ecosystem responses and that there was a negative interaction between the effects of climate and ecosystem responses. These results suggested that for long-term predictions of water and energy balance in global climate change projections, the ecosystem responses must be taken into account to better constrain the uncertainties associated with estimation.

Effects of Climatic Factors and Ecosystem Responses on the Inter-Annual Variability of Evapotranspiration in a Coniferous Plantation in Subtropical China

PubMed Central

Xu, Mingjie; Wen, Xuefa; Wang, Huimin; Zhang, Wenjiang; Dai, Xiaoqin; Song, Jie; Wang, Yidong; Fu, Xiaoli; Liu, Yunfen; Sun, Xiaomin; Yu, Guirui

2014-01-01

Because evapotranspiration (ET) is the second largest component of the water cycle and a critical process in terrestrial ecosystems, understanding the inter-annual variability of ET is important in the context of global climate change. Eight years of continuous eddy covariance measurements (2003–2010) in a subtropical coniferous plantation were used to investigate the impacts of climatic factors and ecosystem responses on the inter-annual variability of ET. The mean and standard deviation of annual ET for 2003–2010 were 786.9 and 103.4 mm (with a coefficient of variation of 13.1%), respectively. The inter-annual variability of ET was largely created in three periods: March, May–June, and October, which are the transition periods between seasons. A set of look-up table approaches were used to separate the sources of inter-annual variability of ET. The annual ETs were calculated by assuming that (a) both the climate and ecosystem responses among years are variable (Vcli-eco), (b) the climate is variable but the ecosystem responses are constant (Vcli), and (c) the climate is constant but ecosystem responses are variable (Veco). The ETs that were calculated under the above assumptions suggested that the inter-annual variability of ET was dominated by ecosystem responses and that there was a negative interaction between the effects of climate and ecosystem responses. These results suggested that for long-term predictions of water and energy balance in global climate change projections, the ecosystem responses must be taken into account to better constrain the uncertainties associated with estimation. PMID:24465610

Modeling Modern Methane Emissions from Natural Wetlands. 2; Interannual Variations 1982-1993

NASA Technical Reports Server (NTRS)

Walter, Bernadette P.; Heimann, Martin; Mattews, Elaine; Hansen, James E. (Technical Monitor)

2001-01-01

A global run of a process-based methane model [Walter et al., this issue] is performed using high-frequency atmospheric forcing fields from ECMWF reanalyses of the period from 1982 to 1993. We calculate global annual methane emissions to be 260 Tg/ yr. 25% of methane emissions originate from wetlands north of 30 deg. N. Only 60% of the produced methane is emitted, while the rest is re-oxidized. A comparison of zonal integrals of simulated global wetland emissions and results obtained by an inverse modeling approach shows good agreement. In a test with data from two wetlands, the seasonality of simulated and observed methane emissions agrees well. The effects of sub-grid scale variations in model parameters and input data are examined. Modeled methane emissions show high regional, seasonal and interannual variability. Seasonal cycles of methane emissions are dominated by temperature in high latitude wetlands, and by changes in the water table in tropical wetlands. Sensitivity tests show that +/- 1 C changes in temperature lead to +/- 20 % changes in methane emissions from wetlands. Uniform changes of +/- 20% in precipitation alter methane emissions by about +/- 18%. Limitations in the model are analyzed. Simulated interannual variations in methane emissions from wetlands are compared to observed atmospheric growth rate anomalies. Our model simulation results suggest that contributions from other sources than wetlands and/or the sinks are more important in the tropics than north-of 30 deg. N. In higher northern latitudes, it seems that a large part, of the observed interannual variations can be explained by variations in wetland emissions. Our results also suggest that reduced wetland emissions played an important role in the observed negative methane growth rate anomaly in 1992.

Multi-tissue analyses reveal limited inter-annual and seasonal variation in mercury exposure in an Antarctic penguin community.

PubMed

Brasso, Rebecka L; Polito, Michael J; Emslie, Steven D

2014-10-01

Inter-annual variation in tissue mercury concentrations in birds can result from annual changes in the bioavailability of mercury or shifts in dietary composition and/or trophic level. We investigated potential annual variability in mercury dynamics in the Antarctic marine food web using Pygoscelis penguins as biomonitors. Eggshell membrane, chick down, and adult feathers were collected from three species of sympatrically breeding Pygoscelis penguins during the austral summers of 2006/2007-2010/2011. To evaluate the hypothesis that mercury concentrations in penguins exhibit significant inter-annual variation and to determine the potential source of such variation (dietary or environmental), we compared tissue mercury concentrations with trophic levels as indicated by δ(15)N values from all species and tissues. Overall, no inter-annual variation in mercury was observed in adult feathers suggesting that mercury exposure, on an annual scale, was consistent for Pygoscelis penguins. However, when examining tissues that reflected more discrete time periods (chick down and eggshell membrane) relative to adult feathers, we found some evidence of inter-annual variation in mercury exposure during penguins' pre-breeding and chick rearing periods. Evidence of inter-annual variation in penguin trophic level was also limited suggesting that foraging ecology and environmental factors related to the bioavailability of mercury may provide more explanatory power for mercury exposure compared to trophic level alone. Even so, the variable strength of relationships observed between trophic level and tissue mercury concentrations across and within Pygoscelis penguin species suggest that caution is required when selecting appropriate species and tissue combinations for environmental biomonitoring studies in Antarctica.

Influence of seasonal and inter-annual hydro-meteorological variability on surface water fecal coliform concentration under varying land-use composition.

PubMed

St Laurent, Jacques; Mazumder, Asit

2014-01-01

Quantifying the influence of hydro-meteorological variability on surface source water fecal contamination is critical to the maintenance of safe drinking water. Historically, this has not been possible due to the scarcity of data on fecal indicator bacteria (FIB). We examined the relationship between hydro-meteorological variability and the most commonly measured FIB, fecal coliform (FC), concentration for 43 surface water sites within the hydro-climatologically complex region of British Columbia. The strength of relationship was highly variable among sites, but tended to be stronger in catchments with nival (snowmelt-dominated) hydro-meteorological regimes and greater land-use impacts. We observed positive relationships between inter-annual FC concentration and hydro-meteorological variability for around 50% of the 19 sites examined. These sites are likely to experience increased fecal contamination due to the projected intensification of the hydrological cycle. Seasonal FC concentration variability appeared to be driven by snowmelt and rainfall-induced runoff for around 30% of the 43 sites examined. Earlier snowmelt in nival catchments may advance the timing of peak contamination, and the projected decrease in annual snow-to-precipitation ratio is likely to increase fecal contamination levels during summer, fall, and winter among these sites. Safeguarding drinking water quality in the face of such impacts will require increased monitoring of FIB and waterborne pathogens, especially during periods of high hydro-meteorological variability. This data can then be used to develop predictive models, inform source water protection measures, and improve drinking water treatment. Copyright © 2013 Elsevier Ltd. All rights reserved.

Recent Trends in the Arctic Navigable Ice Season and Links to Atmospheric Circulation

NASA Astrophysics Data System (ADS)

Maslanik, J.; Drobot, S.

2002-12-01

One of the potential effects of Arctic climate warming is an increase in the navigable ice season, perhaps resulting in development of the Arctic as a major shipping route. The distance from western North American ports to Europe through the Northwest Passage (NWP) or the Northern Sea Route (NSR) is typically 20 to 60 percent shorter than travel through the Panama Canal, while travel between Europe and the Far East may be reduced by as much as three weeks compared to transport through the Suez Canal. An increase in the navigable ice season would also improve commercial opportunities within the Arctic region, such as mineral and oil exploration and tourism, which could potentially expand the economic base of Arctic residents and companies, but which would also have negative environmental impacts. Utilizing daily passive-microwave derived sea ice concentrations, trends and variability in the Arctic navigable ice season are examined from 1979 through 2001. Trend analyses suggest large increases in the length of the navigable ice season in the Kara and Barents seas, the Sea of Okhotsk, and the Beaufort Sea, with decreases in the length of the navigable ice season in the Bering Sea. Interannual variations in the navigable ice season largely are governed by fluctuations in low-frequency atmospheric circulation, although the specific annular modes affecting the length of the navigable ice season vary by region. In the Beaufort and East Siberian seas, variations in the North Atlantic Oscillation/Arctic Oscillation control the navigable ice season, while variations in the East Pacific anomaly play an important role in controlling the navigable ice season in the Kara and Barents seas. In Hudson Bay, the Canadian Arctic Archipelago, and Baffin Bay, interannual variations in the navigable ice season are strongly related to the Pacific Decadal Oscillation.

Applications of subseasonal-to-seasonal (S2S) predictions

NASA Astrophysics Data System (ADS)

White, Christopher; Lamb, Rob; Carlsen, Henrik; Robertson, Andrew; Klein, Richard; Lazo, Jeffrey; Kumar, Arun; Vitart, Frederic; Coughlan de Perez, Erin; Ray, Andrea; Murray, Virginia; Graham, Richard; Buontempo, Carlo

2017-04-01

While long-range seasonal outlooks have been operational for many years, until recently the extended-range timescale - referred to as 'subseasonal-to-seasonal' (S2S) and which sits between the medium- to long-range forecasting timescales - has received relatively little attention. The S2S timescale has long been seen as a 'predictability desert', yet a new generation of S2S predictions are starting to bridge the gap between weather forecasts and longer-range prediction. Decisions in a range of sectors are made in this extended-range lead time, therefore there is a strong demand for this new generation of predictions. At least ten international weather centres now have some capability for issuing experimental or operational S2S predictions, including the European Centre for Medium-Range Weather Forecasting (ECMWF) and the National Oceanic and Atmospheric Administration (NOAA) that now have operational S2S outputs. International efforts are now underway to identify key sources of predictability, improve forecast skill and operationalise aspects of S2S forecasts, however challenges remain in advancing this new frontier. If S2S predictions are to be utilised effectively, it is important that along with science advances, we learn how to develop, communicate and apply these forecasts appropriately. In this study, we present the potential of the emerging operational S2S forecasts to the wider weather and climate applications community by undertaking the first comprehensive review of sectoral applications of S2S predictions, including public health, disaster preparedness, water management, energy and agriculture. We explore the value of applications-relevant S2S predictions, and highlight the opportunities and challenges facing their uptake. We show how social sciences can be integrated with S2S development - from communication to decision-making and valuation of forecasts - to enhance the benefits of 'climate services' approaches for extended-range forecasting. We

Inter-annual Variability in Tundra Phenology Captured with Digital Photography

NASA Astrophysics Data System (ADS)

Melendez, M.; Vargas, S. A.; Tweedie, C. E.

2012-12-01

The need to improve multi-scale phenological monitoring of arctic terrestrial ecosystems has been a persistent research challenge. Although there has been a range of advances in remote sensing capacities over the past decade, these present costly, and sometimes logistically challenging and technically demanding solutions for arctic terrestrial ecosystems. In this poster and undergraduate research project, we demonstrate how seasonal and inter-annual variability in landscape phenology can be derived for multiple tundra ecosystems using a low-cost and low-tech kite aerial photography (KAP) system that has been developed as a contribution to the US Arctic Observing Network. Seasonal landscape phenology was observed over the Networked Info-Mechanical Systems (NIMS) grids (2 x 50 meters) located in Barrow and Atqasuk, Alaska using imagery acquired with KAP and analyzed for a range of greenness indices. Preliminary results showed that the 2G-RB greenness index correlated the best with NDVI values calculated from ground based hyperspectral reflectance measurements. 2012 had the highest 2G-RB greenness index values for both Barrow and Atqasuk sites, which correlated well with NDVI values acquired from ground-based hyperspectral reflectance measurements. Wet vegetation types showed the most interannual variability at the Atqasuk site based on the 2G-RB greenness index while in Barrow the moist vegetation types showed the most interannual variability. These results show that vegetation indices similar to those acquired from hyperspectral remote sensing platforms can be derived using low-cost and low-tech techniques. Further analysis using these same techniques is required in order to link relatively small scale vegetation dynamics measured with KAP with those documented at large scales using satellite imagery.

Intraseasonal to interannual variations in the tropical wave activity revealed in reanalyses and their potential impact on the QBO

NASA Astrophysics Data System (ADS)

Kim, Young-Ha; Yoo, Changhyun

2017-04-01

We investigate activities of tropical waves represented in reanalysis products. The wave activities are quantified by the Eliassen-Palm (EP) flux at 100 hPa, after decomposed into the following four components: equatorially trapped Kelvin waves and mixed Rossby-gravity waves, gravity waves, and Rossby waves. Monthly EP fluxes of the four waves exhibit considerable temporal variations at intraseasonal and interannual, along with seasonal, time scales. These variations are discussed with the tropical large-scale variabilities, including the Madden-Julian Oscillation (MJO), the El Ninõ-Southern Oscillation, and the stratospheric quasi-biennial oscillation (QBO). We find that during boreal winter, the interannual variation of Kelvin wave activity is in phase with that of the MJO amplitude, while such a simultaneous variation cannot be seen in other seasons. The gravity wave is dominated by a semi-annual cycle, while the departure from its semi-annual cycle is largely correlated with the QBO phase in the stratosphere. Potential impacts of the variations in the wave activity upon the QBO properties will be assessed using a simple one-dimensional QBO model.

Evaluating the Capabilities of Soil Enthalpy, Soil Moisture and Soil Temperature in Predicting Seasonal Precipitation

NASA Astrophysics Data System (ADS)

Zhao, Changyu; Chen, Haishan; Sun, Shanlei

2018-04-01

Soil enthalpy ( H) contains the combined effects of both soil moisture ( w) and soil temperature ( T) in the land surface hydrothermal process. In this study, the sensitivities of H to w and T are investigated using the multi-linear regression method. Results indicate that T generally makes positive contributions to H, while w exhibits different (positive or negative) impacts due to soil ice effects. For example, w negatively contributes to H if soil contains more ice; however, after soil ice melts, w exerts positive contributions. In particular, due to lower w interannual variabilities in the deep soil layer (i.e., the fifth layer), H is more sensitive to T than to w. Moreover, to compare the potential capabilities of H, w and T in precipitation ( P) prediction, the Huanghe-Huaihe Basin (HHB) and Southeast China (SEC), with similar sensitivities of H to w and T, are selected. Analyses show that, despite similar spatial distributions of H-P and T-P correlation coefficients, the former values are always higher than the latter ones. Furthermore, H provides the most effective signals for P prediction over HHB and SEC, i.e., a significant leading correlation between May H and early summer (June) P. In summary, H, which integrates the effects of T and w as an independent variable, has greater capabilities in monitoring land surface heating and improving seasonal P prediction relative to individual land surface factors (e.g., T and w).

Southern Ocean Mixed-Layer Seasonal and Interannual Variations From Combined Satellite and In Situ Data

NASA Astrophysics Data System (ADS)

Buongiorno Nardelli, B.; Guinehut, S.; Verbrugge, N.; Cotroneo, Y.; Zambianchi, E.; Iudicone, D.

2017-12-01

The depth of the upper ocean mixed layer provides fundamental information on the amount of seawater that directly interacts with the atmosphere. Its space-time variability modulates water mass formation and carbon sequestration processes related to both the physical and biological pumps. These processes are particularly relevant in the Southern Ocean, where surface mixed-layer depth estimates are generally obtained either as climatological fields derived from in situ observations or through numerical simulations. Here we demonstrate that weekly observation-based reconstructions can be used to describe the variations of the mixed-layer depth in the upper ocean over a range of space and time scales. We compare and validate four different products obtained by combining satellite measurements of the sea surface temperature, salinity, and dynamic topography and in situ Argo profiles. We also compute an ensemble mean and use the corresponding spread to estimate mixed-layer depth uncertainties and to identify the more reliable products. The analysis points out the advantage of synergistic approaches that include in input the sea surface salinity observations obtained through a multivariate optimal interpolation. Corresponding data allow to assess mixed-layer depth seasonal and interannual variability. Specifically, the maximum correlations between mixed-layer anomalies and the Southern Annular Mode are found at different time lags, related to distinct summer/winter responses in the Antarctic Intermediate Water and Sub-Antarctic Mode Waters main formation areas.

Interannual Variations in Global Vegetation Phenology Derived from a Long Term AVHRR and MODIS Data Record

NASA Astrophysics Data System (ADS)

Zhang, X.; Friedl, M. A.; Yu, Y.

2013-12-01

Land surface phenology metrics are widely retrieved from satellite observations at regional and global scales, and have been shown to be valuable for monitoring terrestrial ecosystem dynamics in response to extreme climate events and predicting biological responses to future climate scenarios. While the response of spring vegetation greenup to climate warming at mid-to-high latitudes is well-documented, understanding of diverse phenological responses to climate change over entire growing cycles and at broad geographic scales is incomplete. Many studies assume that the timing of individual phenological indicators in responses to climate forcing is independent of phenological events that occur at other times during the growing season. In this paper we use a different strategy. Specifically, we hypothesize that integrating sequences of key phenological indicators across growing seasons provides a more effective way to capture long-term variation in phenology in response to climate change. To explore this hypothesis we use global land surface phenology metrics derived from the Version 3 Long Term Vegetation Index Products from Multiple Satellite Data Records data set to examine interannual variations and trends in global land surface phenology from 1982-2010. Using daily enhanced vegetation index (EVI) data at a spatial resolution of 0.05 degrees, we model the phenological trajectory for each individual pixel using piecewise logistic models. The modeled trajectories were then used to detect phenological indicators including the onset of greenness increase, the onset of greenness maximum, the onset of greenness decrease, the onset of greenness minimum, and the growing season length, among others at global scale. The quality of land surface phenology detection for individual pixels was calculated based on metrics that characterize the EVI quality and model fits in annual time series at each pixel. Phenological indicators characterized as having good quality were then

Contribution of Soil Moisture Information to Streamflow Prediction in the Snowmelt Season: A Continental-Scale Analysis

NASA Technical Reports Server (NTRS)

Reichle, Rolf; Mahanama, Sarith; Koster, Randal; Lettenmaier, Dennis

2009-01-01

In areas dominated by winter snowcover, the prediction of streamflow during the snowmelt season may benefit from three pieces of information: (i) the accurate prediction of weather variability (precipitation, etc.) leading up to and during the snowmelt season, (ii) estimates of the amount of snow present during the winter season, and (iii) estimates of the amount of soil moisture underlying the snowpack during the winter season. The importance of accurate meteorological predictions and wintertime snow estimates is obvious. The contribution of soil moisture to streamflow prediction is more subtle yet potentially very important. If the soil is dry below the snowpack, a significant fraction of the snowmelt may be lost to streamflow and potential reservoir storage, since it may infiltrate the soil instead for later evaporation. Such evaporative losses are presumably smaller if the soil below the snowpack is wet. In this paper, we use a state-of-the-art land surface model to quantify the contribution of wintertime snow and soil moisture information -- both together and separately -- to skill in forecasting springtime streamflow. We find that soil moisture information indeed contributes significantly to streamflow prediction skill.

Using Google Trends and ambient temperature to predict seasonal influenza outbreaks.

PubMed

Zhang, Yuzhou; Bambrick, Hilary; Mengersen, Kerrie; Tong, Shilu; Hu, Wenbiao

2018-05-16

The discovery of the dynamics of seasonal and non-seasonal influenza outbreaks remains a great challenge. Previous internet-based surveillance studies built purely on internet or climate data do have potential error. We collected influenza notifications, temperature and Google Trends (GT) data between January 1st, 2011 and December 31st, 2016. We performed time-series cross correlation analysis and temporal risk analysis to discover the characteristics of influenza epidemics in the period. Then, the seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to track influenza epidemics using GT and climate data. Influenza infection was significantly corrected with GT at lag of 1-7 weeks in Brisbane and Gold Coast, and temperature at lag of 1-10 weeks for the two study settings. SARIMA models with GT and temperature data had better predictive performance. We identified autoregression (AR) for influenza was the most important determinant for influenza occurrence in both Brisbane and Gold Coast. Our results suggested internet search metrics in conjunction with temperature can be used to predict influenza outbreaks, which can be considered as a pre-requisite for constructing early warning systems using search and temperature data. Copyright © 2018 Elsevier Ltd. All rights reserved.

GEOS S2S-2_1: GMAO's New High Resolution Seasonal Prediction System

NASA Technical Reports Server (NTRS)

Molod, Andrea; Akella, Santha; Andrews, Lauren; Barahona, Donifan; Borovikov, Anna; Chang, Yehui; Cullather, Richard; Hackert, Eric; Kovach, Robin; Koster, Randal;

Climate Prediction Center - Outlooks: CFS Forecast of Seasonal Climate

Science.gov Websites

National Weather Service NWS logo - Click to go to the NWS home page Climate Prediction Center Home Site government Web resources and services. CFS Seasonal Climate Forecasts CFS Forecast of Seasonal Climate discontinued after October 2012. This page displays seasonal climate anomalies from the NCEP coupled forecast

Interannual and seasonal variability in short-term grazing impact of Euphausia superba in nearshore and offshore waters west of the Antarctic Peninsula

NASA Astrophysics Data System (ADS)

Ross, R. M.; Quetin, L. B.; Haberman, K. L.

1998-11-01

Our focus in this paper is the interaction between macrozooplanktonic grazers and primary producers, and the interannual and seasonal variability in the Palmer Long-Term Ecological Research (Palmer LTER) study region from Anvers Island to Adelaide Island. Short-term grazing estimates are calculated by integrating (1) theoretical and experimental estimates of ingestion rates in response to the standing stock of phytoplankton, and (2) field measurements of phytoplankton standing stock and grazer biomass. Field data come from three austral summer cruises (January/February of 1993, 1994, and 1995) and one sequence of seasonal cruises (summer, fall and winter 1993). The relative and absolute abundance of the dominant macrozooplankton grazers, Euphausia superba and Salpa thompsoni, varied by at least an order of magnitude on the spatial and temporal scales observed. Mean grazing rates ranged from 0.4 to 9.0 μg chlorophyll m -2 h -1 for the Antarctic krill and salp populations over the three summer cruises. This leads to variability in the flow of carbon from the primary producers through the grazers on the same scales. Temporal and spatial variability in grazing impact and faecal pellet production are high.

Seasonally Varying Predation Behavior and Climate Shifts Are Predicted to Affect Predator-Prey Cycles.

PubMed

Tyson, Rebecca; Lutscher, Frithjof

2016-11-01

The functional response of some predator species changes from a pattern characteristic for a generalist to that for a specialist according to seasonally varying prey availability. Current theory does not address the dynamic consequences of this phenomenon. Since season length correlates strongly with altitude and latitude and is predicted to change under future climate scenarios, including this phenomenon in theoretical models seems essential for correct prediction of future ecosystem dynamics. We develop and analyze a two-season model for the great horned owl (Bubo virginialis) and snowshoe hare (Lepus americanus). These species form a predator-prey system in which the generalist to specialist shift in predation pattern has been documented empirically. We study the qualitative behavior of this predator-prey model community as summer season length changes. We find that relatively small changes in summer season length can have a profound impact on the system. In particular, when the predator has sufficient alternative resources available during the summer season, it can drive the prey to extinction, there can be coexisting stable states, and there can be stable large-amplitude limit cycles coexisting with a stable steady state. Our results illustrate that the impacts of global change on local ecosystems can be driven by internal system dynamics and can potentially have catastrophic consequences.

Prediction of Seasonal Climate-induced Variations in Global Food Production

NASA Technical Reports Server (NTRS)

Iizumi, Toshichika; Sakuma, Hirofumi; Yokozawa, Masayuki; Luo, Jing-Jia; Challinor, Andrew J.; Brown, Molly E.; Sakurai, Gen; Yamagata, Toshio

2013-01-01

Consumers, including the poor in many countries, are increasingly dependent on food imports and are therefore exposed to variations in yields, production, and export prices in the major food-producing regions of the world. National governments and commercial entities are paying increased attention to the cropping forecasts of major food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We assessed the reliability of hindcasts (i.e., retrospective forecasts for the past) of crop yield loss relative to the previous year for two lead times. Pre-season yield predictions employ climatic forecasts and have lead times of approximately 3 to 5 months for providing information regarding variations in yields for the coming cropping season. Within-season yield predictions use climatic forecasts with lead times of 1 to 3 months. Pre-season predictions can be of value to national governments and commercial concerns, complemented by subsequent updates from within-season predictions. The latter incorporate information on the most recent climatic data for the upcoming period of reproductive growth. In addition to such predictions, hindcasts using observations from satellites were performed to demonstrate the upper limit of the reliability of crop forecasting.

Seasonal and interannual evolution of Jakobshavn Isbrae, Greenland from a 2008-2015 high-res DEM and velocity time series

NASA Astrophysics Data System (ADS)

Shean, D. E.; Joughin, I.; Smith, B.; Floricioiu, D.

2015-12-01

Greenland's large marine-terminating outlet glaciers have displayed marked retreat, speedup, and thinning in recent decades. Jakobshavn Isbrae, one of Greenland's largest outlet glaciers, has retreated ~15 km, accelerated ~150%, and thinned ~200 m since the early 1990s. Here, we present a comprehensive analysis of high-resolution elevation (~2-5 m/px) and velocity (~100 m/px) time series with dense temporal coverage (daily-monthly). The Jakobshavn DEM time series consists of >70 WorldView-1/2/3 stereo DEMs and >11 TanDEM-X DEMs spanning 2008-2015. Complementary point elevation data from Operation IceBridge (ATM, LVIS), pre-IceBridge ATM flights, and ICESat-1 GLAS extend the surface elevation record to 1999 and provide essential absolute control data, enabling sub-meter horizontal/vertical accuracy for gridded DEMs. Velocity data are primarily derived from TerraSAR-X/TanDEM-X image pairs with 11-day interval from 2009-2015. These elevation and velocity data capture outlet glacier evolution with unprecedented detail during the post-ICESat era. The lower trunk of Jakobshavn displays significant seasonal velocity variations, with recent rates of ~8 km/yr during winter and >17 km/yr during summer. DEM data show corresponding seasonal elevation changes of -30 to -45 m in summer and +15 to +20 m in winter, with decreasing magnitude upstream. Seasonal discharge varies from ~30-35 Gt/yr in winter to ~45-55 Gt/yr in summer, and we integrate these measurements for improved long-term mass-balance estimates. Recent interannual trends show increased discharge, velocity, and thinning (-15 to -20 m/yr), which is consistent with long-term altimetry records. The DEM time series also reveal new details about calving front and mélange evolution during the seasonal cycle. Similar time series are available for Kangerdlugssuaq and Helheim Glaciers. These observations are improving our understanding of outlet glacier dynamics, while complementing ongoing efforts to constrain estimates

Climate Scenarios for the NASA / USAID SERVIR Project: Challenges for Multiple Planning Horizons

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Roberts, J. B.; Lyon, B.; Funk, C.; Bosilovich, M. G.

2014-01-01

SERVIR, an acronym meaning "to serve" in Spanish, is a joint venture between NASA and the U.S. Agency for International Development (USAID) which provides satellite-based Earth observation data, modeling, and science applications to help developing nations in Central America, East Africa and the Himalayas improve environmental decision making. Anticipating climate variability / climate change impacts has now become an important component of the SERVIR efforts to build capacity in these regions. Uncertainty in hydrometeorological components of climate variations and exposure to extreme events across scales from weather to climate are of particular concern. We report here on work to construct scenarios or outlooks that are being developed as input drivers for decision support systems (DSSs) in a variety of settings. These DSSs are being developed jointly by a broad array NASA Applied Science Team (AST) Investigations and user communities in the three SERVIR Hub Regions, Central America, East Africa and the Himalayas. Issues span hydrologic / water resources modeling, agricultural productivity, and forest carbon reserves. The scenarios needed for these efforts encompass seasonal forecasts, interannual outlooks, and likely decadal / multi-decadal trends. Providing these scenarios across the different AST efforts enables some level of integration in considering regional responses to climate events. We will discuss a number of challenges in developing this continuum of scenarios including the identification and "mining" of predictability, addressing multiple continental regions, issues of downscaling global model integrations to regional / local applications (i.e. hydrologic and crop modeling). We compare / contrast the role of the U.S. National Multi- Model Experiment initiative in seasonal forecasts and the CMIP-5 climate model experiments in supporting these efforts. Examples of these scenarios, their use, and an assessment of their utility as well as limitations will

Seasonal and inter-annual variation in ecosystem scale methane emission from a boreal fen

NASA Astrophysics Data System (ADS)

Rinne, Janne; Li, Xuefei; Raivonen, Maarit; Peltola, Olli; Sallantaus, Tapani; Haapanala, Sami; Smolander, Sampo; Alekseychik, Pavel; Aurela, Mika; Korrensalo, Aino; Mammarella, Ivan; Tuittila, Eeva-Stiina; Vesala, Timo

2016-04-01

Northern wetlands are one of the major sources of atmospheric methane. We have measured ecosystem scale methane emissions from a boreal fen continuously since 2005. The site is an oligotrophic fen in boreal vegetation zone situated in Siikaneva wetland complex in Southern Finland. The mean annual temperature in the area is 3.3°C and total annual precipitation 710 mm. We have conducted the methane emission measurements by the eddy covariance method. Additionally we have measured fluxes of carbon dioxide, water vapor, and sensible heat together with a suite of other environmental parameters. We have analyzed this data alongside with a model run with University of Helsinki methane model. The measured fluxes show generally highest methane emission in late summers coinciding with the highest temperatures in saturated peat zone. During winters the fluxes show small but detectable emission despite the snow and ice cover on the fen. More than 90% of the annual methane emission occurs in snow-free period. The methane emission and peat temperature are connected in exponential manner in seasonal scales, but methane emission does not show the expected behavior with water table. The lack of water table position dependence also contrasts with the spatial variation across microtopography. There is no systematic variation in sub-diurnal time scale. The general seasonal cycle in methane emission is captured well with the methane model. We will show how well the model reproduces the temperature and water table position dependencies observed. The annual methane emission is typically around 10 gC m-2. This is a significant part of the total carbon exchange between the fen and the atmosphere and about twice the estimated carbon loss by leaching from the fen area. The inter-annual variability in the methane emission is modest. The June-September methane emissions from different years, comprising most of the annual emission, correlates positively with peat temperature, but not with

Short-term favorable weather conditions are an important control of interannual variability in carbon and water fluxes

DOE PAGES

Zscheischler, Jakob; Fatichi, Simone; Wolf, Sebastian; ...

2016-08-08

Ecosystem models often perform poorly in reproducing interannual variability in carbon and water fluxes, resulting in considerable uncertainty when estimating the land-carbon sink. While many aggregated variables (growing season length, seasonal precipitation, or temperature) have been suggested as predictors for interannual variability in carbon fluxes, their explanatory power is limited and uncertainties remain as to their relative contributions. Recent results show that the annual count of hours where evapotranspiration (ET) is larger than its 95th percentile is strongly correlated with the annual variability of ET and gross primary production (GPP) in an ecosystem model. This suggests that the occurrence ofmore » favorable conditions has a strong influence on the annual carbon budget. Here we analyzed data from eight forest sites of the AmeriFlux network with at least 7 years of continuous measurements. We show that for ET and the carbon fluxes GPP, ecosystem respiration (RE), and net ecosystem production, counting the “most active hours/days” (i.e., hours/days when the flux exceeds a high percentile) correlates well with the respective annual sums, with correlation coefficients generally larger than 0.8. Phenological transitions have much weaker explanatory power. By exploiting the relationship between most active hours and interannual variability, we classify hours as most active or less active and largely explain interannual variability in ecosystem fluxes, particularly for GPP and RE. Our results suggest that a better understanding and modeling of the occurrence of large values in high-frequency ecosystem fluxes will result in a better understanding of interannual variability of these fluxes.« less

Natural and management influences on freshwater inflows and salinity in the San Francisco Estuary at monthly to interannual scales

USGS Publications Warehouse

Knowles, Noah

2002-01-01

Understanding the processes controlling the physics, chemistry, and biology of the San Francisco Estuary and their relation to climate variability is complicated by the combined influence on freshwater inflows of natural variability and upstream management. To distinguish these influences, alterations of estuarine inflow due to major reservoirs and freshwater pumping in the watershed were inferred from available data. Effects on salinity were estimated by using reconstructed estuarine inflows corresponding to differing levels of impairment to drive a numerical salinity model. Both natural and management inflow and salinity signals show strong interannual variability. Management effects raise salinities during the wet season, with maximum influence in spring. While year‐to‐year variations in all signals are very large, natural interannual variability can greatly exceed the range of management effects on salinity in the estuary.

The Arctic's sea ice cover: trends, variability, predictability, and comparisons to the Antarctic.

PubMed

Serreze, Mark C; Meier, Walter N

2018-05-28

As assessed over the period of satellite observations, October 1978 to present, there are downward linear trends in Arctic sea ice extent for all months, largest at the end of the melt season in September. The ice cover is also thinning. Downward trends in extent and thickness have been accompanied by pronounced interannual and multiyear variability, forced by both the atmosphere and ocean. As the ice thins, its response to atmospheric and oceanic forcing may be changing. In support of a busier Arctic, there is a growing need to predict ice conditions on a variety of time and space scales. A major challenge to providing seasonal scale predictions is the 7-10 days limit of numerical weather prediction. While a seasonally ice-free Arctic Ocean is likely well within this century, there is much uncertainty in the timing. This reflects differences in climate model structure, the unknown evolution of anthropogenic forcing, and natural climate variability. In sharp contrast to the Arctic, Antarctic sea ice extent, while highly variable, has increased slightly over the period of satellite observations. The reasons for this different behavior remain to be resolved, but responses to changing atmospheric circulation patterns appear to play a strong role. © 2018 New York Academy of Sciences.

Interannual, solar cycle, and trend terms in middle atmospheric temperature time series from HALOE

NASA Astrophysics Data System (ADS)

Remsberg, E. E.; Deaver, L. E.

2005-03-01

Temperature versus pressure or T(p) time series from the Halogen Occultation Experiment (HALOE) have been generated and analyzed for the period of 1991-2004 and for the mesosphere and upper stratosphere for latitude zones from 40N to 40S. Multiple linear regression (MLR) techniques were used for the analysis of the seasonal and the significant interannual and solar cycle (or decadal-scale) terms. An 11-yr solar cycle (SC) term of amplitude 0.5 to 1.7 K was found for the middle to upper mesosphere; its phase was determined by a Fourier fit to the de-seasonalized residual. This SC term is largest and has a lag of several years for northern hemisphere middle latitudes of the middle mesosphere, perhaps due to the interfering effects of wintertime wave dissipation. The SC response from the MLR models is weaker but essentially in-phase at low latitudes and in the southern hemisphere. An in-phase SC response term is also significant near the tropical stratopause with an amplitude of about 0.4 to 0.6 K, which is somewhat less than predicted from models. Both sub-biennial (688-dy) and QBO (800-dy) terms are resolved for the mid to upper stratosphere along with a decadal-scale term that is presumed to have a 13.5-yr period due to their predicted modulation. This decadal-scale term is out-of-phase with the SC during 1991-2004. However, the true nature and source of this term is still uncertain, especially at 5 hPa. Significant linear cooling trends ranging from -0.3 K to -1.1 K per decade were found in the tropical upper stratosphere and subtropical mesosphere. Trends have not emerged so far for the tropical mesosphere, so it is concluded that the cooling rates that have been resolved for the subtropics are likely upper limits. As HALOE-like measurements continue and their time series lengthen, it is anticipated that better accuracy can be achieved for these interannual, SC, and trend terms.

Interannual variability of the physical characteristics of North Thermaikos Gulf (NW Aegean Sea)

NASA Astrophysics Data System (ADS)

Krestenitis, Yannis N.; Kombiadou, Katerina D.; Androulidakis, Yannis S.

2012-08-01

Thermaikos Gulf is a marine ecosystem of major importance, not only environmental, but also due to the various socioeconomic activities associated with the area. The physical characteristics of the gulf's waters were studied, analyzing in situ measurements of oceanographic parameters, collected during 5 oceanographic surveys from 1994 to 2007, on the same grid of 26 sampling stations. Aim of this paper is the detection and description of the main changes (seasonal and interannual) in the water masses' characteristics that are related to the seawater quality of the North Thermaikos. The connection between the main forcing factors and the major circulation patterns is also under investigation. The interannual analysis of the collected data revealed the existence of strong seasonal fluctuations that present significant deviations from a mean seasonal pattern in specific periods. A general decreasing trend of the salinities of the domain is observed during the study period. At the same time, a strong relation between open Aegean Sea waters and riverine freshwater fluxes is identified, factors that significantly influence stratification, circulation and renewal of the gulf. Based on the thermohaline properties, two dense water formation events (February 2000 and 2005), not previously reported, are detected and analyzed for the first time.

Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns.

PubMed

Shen, Lu; Mickley, Loretta J

2017-03-07

We develop a statistical model to predict June-July-August (JJA) daily maximum 8-h average (MDA8) ozone concentrations in the eastern United States based on large-scale climate patterns during the previous spring. We find that anomalously high JJA ozone in the East is correlated with these springtime patterns: warm tropical Atlantic and cold northeast Pacific sea surface temperatures (SSTs), as well as positive sea level pressure (SLP) anomalies over Hawaii and negative SLP anomalies over the Atlantic and North America. We then develop a linear regression model to predict JJA MDA8 ozone from 1980 to 2013, using the identified SST and SLP patterns from the previous spring. The model explains ∼45% of the variability in JJA MDA8 ozone concentrations and ∼30% variability in the number of JJA ozone episodes (>70 ppbv) when averaged over the eastern United States. This seasonal predictability results from large-scale ocean-atmosphere interactions. Warm tropical Atlantic SSTs can trigger diabatic heating in the atmosphere and influence the extratropical climate through stationary wave propagation, leading to greater subsidence, less precipitation, and higher temperatures in the East, which increases surface ozone concentrations there. Cooler SSTs in the northeast Pacific are also associated with more summertime heatwaves and high ozone in the East. On average, models participating in the Atmospheric Model Intercomparison Project fail to capture the influence of this ocean-atmosphere interaction on temperatures in the eastern United States, implying that such models would have difficulty simulating the interannual variability of surface ozone in this region.

Seasonal prediction of US summertime ozone using statistical analysis of large scale climate patterns

PubMed Central

Mickley, Loretta J.

2017-01-01

We develop a statistical model to predict June–July–August (JJA) daily maximum 8-h average (MDA8) ozone concentrations in the eastern United States based on large-scale climate patterns during the previous spring. We find that anomalously high JJA ozone in the East is correlated with these springtime patterns: warm tropical Atlantic and cold northeast Pacific sea surface temperatures (SSTs), as well as positive sea level pressure (SLP) anomalies over Hawaii and negative SLP anomalies over the Atlantic and North America. We then develop a linear regression model to predict JJA MDA8 ozone from 1980 to 2013, using the identified SST and SLP patterns from the previous spring. The model explains ∼45% of the variability in JJA MDA8 ozone concentrations and ∼30% variability in the number of JJA ozone episodes (>70 ppbv) when averaged over the eastern United States. This seasonal predictability results from large-scale ocean–atmosphere interactions. Warm tropical Atlantic SSTs can trigger diabatic heating in the atmosphere and influence the extratropical climate through stationary wave propagation, leading to greater subsidence, less precipitation, and higher temperatures in the East, which increases surface ozone concentrations there. Cooler SSTs in the northeast Pacific are also associated with more summertime heatwaves and high ozone in the East. On average, models participating in the Atmospheric Model Intercomparison Project fail to capture the influence of this ocean–atmosphere interaction on temperatures in the eastern United States, implying that such models would have difficulty simulating the interannual variability of surface ozone in this region. PMID:28223483

Seasonal prediction of East Asian summer rainfall using a multi-model ensemble system

NASA Astrophysics Data System (ADS)

Ahn, Joong-Bae; Lee, Doo-Young; Yoo, Jin‑Ho

2015-04-01

Using the retrospective forecasts of seven state-of-the-art coupled models and their multi-model ensemble (MME) for boreal summers, the prediction skills of climate models in the western tropical Pacific (WTP) and East Asian region are assessed. The prediction of summer rainfall anomalies in East Asia is difficult, while the WTP has a strong correlation between model prediction and observation. We focus on developing a new approach to further enhance the seasonal prediction skill for summer rainfall in East Asia and investigate the influence of convective activity in the WTP on East Asian summer rainfall. By analyzing the characteristics of the WTP convection, two distinct patterns associated with El Niño-Southern Oscillation developing and decaying modes are identified. Based on the multiple linear regression method, the East Asia Rainfall Index (EARI) is developed by using the interannual variability of the normalized Maritime continent-WTP Indices (MPIs), as potentially useful predictors for rainfall prediction over East Asia, obtained from the above two main patterns. For East Asian summer rainfall, the EARI has superior performance to the East Asia summer monsoon index or each MPI. Therefore, the regressed rainfall from EARI also shows a strong relationship with the observed East Asian summer rainfall pattern. In addition, we evaluate the prediction skill of the East Asia reconstructed rainfall obtained by hybrid dynamical-statistical approach using the cross-validated EARI from the individual models and their MME. The results show that the rainfalls reconstructed from simulations capture the general features of observed precipitation in East Asia quite well. This study convincingly demonstrates that rainfall prediction skill is considerably improved by using a hybrid dynamical-statistical approach compared to the dynamical forecast alone. Acknowledgements This work was carried out with the support of Rural Development Administration Cooperative Research

Determination of Arctic sea ice variability modes on interannual timescales via nonhierarchical clustering

NASA Astrophysics Data System (ADS)

Fučkar, Neven-Stjepan; Guemas, Virginie; Massonnet, François; Doblas-Reyes, Francisco

2015-04-01

Over the modern observational era, the northern hemisphere sea ice concentration, age and thickness have experienced a sharp long-term decline superimposed with strong internal variability. Hence, there is a crucial need to identify robust patterns of Arctic sea ice variability on interannual timescales and disentangle them from the long-term trend in noisy datasets. The principal component analysis (PCA) is a versatile and broadly used method for the study of climate variability. However, the PCA has several limiting aspects because it assumes that all modes of variability have symmetry between positive and negative phases, and suppresses nonlinearities by using a linear covariance matrix. Clustering methods offer an alternative set of dimension reduction tools that are more robust and capable of taking into account possible nonlinear characteristics of a climate field. Cluster analysis aggregates data into groups or clusters based on their distance, to simultaneously minimize the distance between data points in a given cluster and maximize the distance between the centers of the clusters. We extract modes of Arctic interannual sea-ice variability with nonhierarchical K-means cluster analysis and investigate the mechanisms leading to these modes. Our focus is on the sea ice thickness (SIT) as the base variable for clustering because SIT holds most of the climate memory for variability and predictability on interannual timescales. We primarily use global reconstructions of sea ice fields with a state-of-the-art ocean-sea-ice model, but we also verify the robustness of determined clusters in other Arctic sea ice datasets. Applied cluster analysis over the 1958-2013 period shows that the optimal number of detrended SIT clusters is K=3. Determined SIT cluster patterns and their time series of occurrence are rather similar between different seasons and months. Two opposite thermodynamic modes are characterized with prevailing negative or positive SIT anomalies over the

Impact of the initialisation on the predictability of the Southern Ocean sea ice at interannual to multi-decadal timescales

NASA Astrophysics Data System (ADS)

Zunz, Violette; Goosse, Hugues; Dubinkina, Svetlana

2014-05-01

In this study, we assess systematically the impact of different initialisation procedures on the predictability of the sea ice in the Southern Ocean. These initialisation strategies are based on three data assimilation methods: the nudging, the particle filter with sequential resampling and the nudging proposal particle filter. An Earth-system model of intermediate complexity has been used to perform hindcast simulations in a perfect model approach. The predictability of the Southern Ocean sea ice is estimated through two aspects: the spread of the hindcast ensemble, indicating the uncertainty on the ensemble, and the correlation between the ensemble mean and the pseudo-observations, used to assess the accuracy of the prediction. Our results show that, at decadal timescales, more sophisticated data assimilation methods as well as denser pseudo-observations used to initialise the hindcasts decrease the spread of the ensemble but improve only slightly the accuracy of the prediction of the sea ice in the Southern Ocean. Overall, the predictability at interannual timescales is limited, at most, to three years ahead. At multi-decadal timescales, there is a clear improvement of the correlation of the trend in sea ice extent between the hindcasts and the pseudo-observations if the initialisation takes into account the pseudo-observations. The correlation reaches values larger than 0.5 and is due to the inertia of the ocean, showing the importance of the quality of the initialisation below the sea ice.

Interannual Comparison of Water Vapor in the North Polar Region of Mars

NASA Technical Reports Server (NTRS)

Tamppari, L. K.; Smith, M. D.; Hale, A. S.; Bass, D. S.

2003-01-01

In order to better understand the current climate of Mars, we seek to understand atmospheric water in the north polar region. Our approach is to examine the water transport and cycling issues within the north polar region and in/out of the region on seasonal and annual timescales. Viking Mars Atmospheric Water Detector (MAWD) data showed that water vapor increased as the northern summer season progressed and temperatures increased, and that vapor appeared to be transported southward . However, there has been uncertainty about the amount of water cycling in and out of the north polar region, as evidenced by residual polar cap visible brightness changes between one Martian year (Mariner 9 data) and a subsequent year (Viking data). These changes were originally thought to be interannual variations in the amount of frost sublimed based on global dust storm activity . However, Viking thermal and imaging data were re-examined and it was found that 14-35 pr m of water -ice appeared to be deposited on the cap later in the summer season, indicating that some water may be retained and redistributed within the polar cap region. This late summer deposition could be due to adsorption directly onto the cap surface or due to snowfall. We seek to understand what happens to the water on seasonal and interannual timescales. We address these issues by examining water vapor in the north polar region of Mars during the north spring and summer period from MGS TES data and by comparing these results to the Viking MAWD results.

Interannual variability of snowmelt in the Sierra Nevada and Rocky Mountains, United States: Examples from two alpine watersheds

NASA Astrophysics Data System (ADS)

Jepsen, Steven M.; Molotch, Noah P.; Williams, Mark W.; Rittger, Karl E.; Sickman, James O.

2012-02-01

The distribution of snow and the energy flux components of snowmelt are intrinsic characteristics of the alpine water cycle controlling the location of source waters and the effect of climate on streamflow. Interannual variability of these characteristics is relevant to the effect of climate change on alpine hydrology. Our objective is to characterize the interannual variability in the spatial distribution of snow and energy fluxes of snowmelt in watersheds of a maritime setting, Tokopah Basin (TOK) in California's southern Sierra Nevada, and a continental setting, Green Lake 4 Valley (GLV4) in Colorado's Front Range, using a 12 year database (1996-2007) of hydrometeorological observations and satellite-derived snow cover. Snowpacks observed in GLV4 exhibit substantially greater spatial variability than in TOK (0.75 versus 0.28 spatial coefficient of variation). In addition, modeling results indicate that the net turbulent energy flux contribution to snowmelt in GLV4 is, on average, 3 times greater in magnitude (mean 29% versus 10%) and interannual variability (standard deviation 17% versus 6%) than in TOK. These energy flux values exhibit strong seasonality, increasing as the melt season progresses to times later in the year (R2 = 0.54-0.77). This seasonality of energy flux appears to be associated with snowmelt rates that generally increase with onset date of melt (0.02 cm d-2). This seasonality in snowmelt rate, coupled to differences in hydrogeology, may account for the observed differences in correspondence between the timing of snowmelt and timing of streamflow in these watersheds.

Interannual variability of snowmelt in the Sierra Nevada and Rocky Mountains, United States: examples from two alpine watersheds

USGS Publications Warehouse

Jepsen, Steven M.; Molotch, Noah P.; Williams, Mark W.; Rittger, Karl E.; Sickman, James O.

2012-01-01

The distribution of snow and the energy flux components of snowmelt are intrinsic characteristics of the alpine water cycle controlling the location of source waters and the effect of climate on streamflow. Interannual variability of these characteristics is relevant to the effect of climate change on alpine hydrology. Our objective is to characterize the interannual variability in the spatial distribution of snow and energy fluxes of snowmelt in watersheds of a maritime setting, Tokopah Basin (TOK) in California's southern Sierra Nevada, and a continental setting, Green Lake 4 Valley (GLV4) in Colorado's Front Range, using a 12 year database (1996–2007) of hydrometeorological observations and satellite-derived snow cover. Snowpacks observed in GLV4 exhibit substantially greater spatial variability than in TOK (0.75 versus 0.28 spatial coefficient of variation). In addition, modeling results indicate that the net turbulent energy flux contribution to snowmelt in GLV4 is, on average, 3 times greater in magnitude (mean 29% versus 10%) and interannual variability (standard deviation 17% versus 6%) than in TOK. These energy flux values exhibit strong seasonality, increasing as the melt season progresses to times later in the year (R2 = 0.54–0.77). This seasonality of energy flux appears to be associated with snowmelt rates that generally increase with onset date of melt (0.02 cm d-2). This seasonality in snowmelt rate, coupled to differences in hydrogeology, may account for the observed differences in correspondence between the timing of snowmelt and timing of streamflow in these watersheds.

Trends, interannual variability, and seasonal cycle of atmospheric methane over the western Pacific observed using voluntary observing ships

NASA Astrophysics Data System (ADS)

Terao, Y.; Kim, H.; Mukai, H.; Nojiri, Y.; Machida, T.; Tohjima, Y.; Saeki, T.; Maksyutov, S.

2012-12-01

We present an analysis of trends, interannual variability (IAV), and seasonal cycle of atmospheric methane (CH4) over the western Pacific between 55N and 35S from 1994 to 2011. Observations were made by the National Institute for Environmental Studies (NIES), Center for Global Environmental Research (CGER), using voluntary observation ships sailing between Japan and Australia/New Zealand and between Japan and North America, sampling background maritime air quasi-monthly with high resolution in latitude. We found remarkable phenomena in IAV of CH4 in the northern tropics over the western Pacific: 1) the high growth rate of 20 ppb/yr in mid-1997 ahead of the global increase in 1998, 2) the suppression of CH4 growth in 2007, 3) significantly smaller amplitude of seasonal cycle in 1999-2000 and in 2008. Results from the simulation and meteorological analysis indicated that the IAV in atmospheric circulation associated with the El Nino and La Nina significantly contributed to these events. Our observations were made at sites located relatively close to the large CH4 sources of East and Southeast Asia, which resulted in the high sensitivity of measured CH4 mixing ratios in the northern tropics to changes in atmospheric transport and emissions from East and Southeast Asia. We will show the results from inverse analysis using our ship measurements as well as other global dataset. The CH4 data set we presented here would be valuable in accurately and quantitatively estimating the global CH4 budget.

Interannual and spatial variability of maple syrup yield as related to climatic factors

PubMed Central

Houle, Daniel

2014-01-01

Sugar maple syrup production is an important economic activity for eastern Canada and the northeastern United States. Since annual variations in syrup yield have been related to climate, there are concerns about the impacts of climatic change on the industry in the upcoming decades. Although the temporal variability of syrup yield has been studied for specific sites on different time scales or for large regions, a model capable of accounting for both temporal and regional differences in yield is still lacking. In the present study, we studied the factors responsible for interregional and interannual variability in maple syrup yield over the 2001–2012 period, by combining the data from 8 Quebec regions (Canada) and 10 U.S. states. The resulting model explained 44.5% of the variability in yield. It includes the effect of climatic conditions that precede the sapflow season (variables from the previous growing season and winter), the effect of climatic conditions during the current sapflow season, and terms accounting for intercountry and temporal variability. Optimal conditions for maple syrup production appear to be spatially restricted by less favourable climate conditions occurring during the growing season in the north, and in the south, by the warmer winter and earlier spring conditions. This suggests that climate change may favor maple syrup production northwards, while southern regions are more likely to be negatively affected by adverse spring conditions. PMID:24949244

Predicting Regional Drought on Sub-Seasonal to Decadal Time Scales

NASA Technical Reports Server (NTRS)

Schubert, Siegfried; Wang, Hailan; Suarez, Max; Koster, Randal

2011-01-01

Drought occurs on a wide range of time scales, and within a variety of different types of regional climates. It is driven foremost by an extended period of reduced precipitation, but it is the impacts on such quantities as soil moisture, streamflow and crop yields that are often most important from a users perspective. While recognizing that different users have different needs for drought information, it is nevertheless important to understand that progress in predicting drought and satisfying such user needs, largely hinges on our ability to improve predictions of precipitation. This talk reviews our current understanding of the physical mechanisms that drive precipitation variations on subseasonal to decadal time scales, and the implications for predictability and prediction skill. Examples are given highlighting the phenomena and mechanisms controlling precipitation on monthly (e.g., stationary Rossby waves, soil moisture), seasonal (ENSO) and decadal time scales (PD and AMO).

Seasonal climate variation and caribou availability: Modeling sequential movement using satellite-relocation data

USGS Publications Warehouse

Nicolson, Craig; Berman, Matthew; West, Colin Thor; Kofinas, Gary P.; Griffith, Brad; Russell, Don; Dugan, Darcy

2013-01-01

Livelihood systems that depend on mobile resources must constantly adapt to change. For people living in permanent settlements, environmental changes that affect the distribution of a migratory species may reduce the availability of a primary food source, with the potential to destabilize the regional social-ecological system. Food security for Arctic indigenous peoples harvesting barren ground caribou (Rangifer tarandus granti) depends on movement patterns of migratory herds. Quantitative assessments of physical, ecological, and social effects on caribou distribution have proven difficult because of the significant interannual variability in seasonal caribou movement patterns. We developed and evaluated a modeling approach for simulating the distribution of a migratory herd throughout its annual cycle over a multiyear period. Beginning with spatial and temporal scales developed in previous studies of the Porcupine Caribou Herd of Canada and Alaska, we used satellite collar locations to compute and analyze season-by-season probabilities of movement of animals between habitat zones under two alternative weather conditions for each season. We then built a set of transition matrices from these movement probabilities, and simulated the sequence of movements across the landscape as a Markov process driven by externally imposed seasonal weather states. Statistical tests showed that the predicted distributions of caribou were consistent with observed distributions, and significantly correlated with subsistence harvest levels for three user communities. Our approach could be applied to other caribou herds and could be adapted for simulating the distribution of other ungulates and species with similarly large interannual variability in the use of their range.

Interannual Variability in Soil Trace Gas (CO2, N2O, NO) Fluxes and Analysis of Controllers

NASA Technical Reports Server (NTRS)

Potter, C.; Klooster, S.; Peterson, David L. (Technical Monitor)

1997-01-01

Interannual variability in flux rates of biogenic trace gases must be quantified in order to understand the differences between short-term trends and actual long-term change in biosphere-atmosphere interactions. We simulated interannual patterns (1983-1988) of global trace gas fluxes from soils using the NASA Ames model version of CASA (Carnegie-Ames-Stanford Approach) in a transient simulation mode. This ecosystem model has been recalibrated for simulations driven by satellite vegetation index data from the NOAA Advanced Very High Resolution Radiometer (AVHRR) over the mid-1980s. The predicted interannual pattern of soil heterotropic CO2 emissions indicates that relatively large increases in global carbon flux from soils occurred about three years following the strong El Nino Southern Oscillation (ENSO) event of 1983. Results for the years 1986 and 1987 showed an annual increment of +1 Pg (1015 g) C-CO2 emitted from soils, which tended to dampen the estimated global increase in net ecosystem production with about a two year lag period relative to plant carbon fixation. Zonal discrimination of model results implies that 80-90 percent of the yearly positive increments in soil CO2 emission during 1986-87 were attributable to soil organic matter decomposition in the low-latitudes (between 30 N and 30 S). Soils of the northern middle-latitude zone (between 30 N and 60 N) accounted for the residual of these annual increments. Total annual emissions of nitrogen trace gases (N2O and NO) from soils were estimated to vary from 2-4 percent over the time period modeled, a level of variability which is consistent with predicted interannual fluctuations in global soil CO2 fluxes. Interannual variability of precipitation in tropical and subtropical zones (30 N to 20 S appeared to drive the dynamic inverse relationship between higher annual emissions of NO versus emissions of N2O. Global mean emission rates from natural (heterotrophic) soil sources over the period modeled (1983

Prediction of rainfall anomalies during the dry to wet transition season over the Southern Amazonia using machine learning tools

NASA Astrophysics Data System (ADS)

Shan, X.; Zhang, K.; Zhuang, Y.; Fu, R.; Hong, Y.

2017-12-01

Seasonal prediction of rainfall during the dry-to-wet transition season in austral spring (September-November) over southern Amazonia is central for improving planting crops and fire mitigation in that region. Previous studies have identified the key large-scale atmospheric dynamic and thermodynamics pre-conditions during the dry season (June-August) that influence the rainfall anomalies during the dry to wet transition season over Southern Amazonia. Based on these key pre-conditions during dry season, we have evaluated several statistical models and developed a Neural Network based statistical prediction system to predict rainfall during the dry to wet transition for Southern Amazonia (5-15°S, 50-70°W). Multivariate Empirical Orthogonal Function (EOF) Analysis is applied to the following four fields during JJA from the ECMWF Reanalysis (ERA-Interim) spanning from year 1979 to 2015: geopotential height at 200 hPa, surface relative humidity, convective inhibition energy (CIN) index and convective available potential energy (CAPE), to filter out noise and highlight the most coherent spatial and temporal variations. The first 10 EOF modes are retained for inputs to the statistical models, accounting for at least 70% of the total variance in the predictor fields. We have tested several linear and non-linear statistical methods. While the regularized Ridge Regression and Lasso Regression can generally capture the spatial pattern and magnitude of rainfall anomalies, we found that that Neural Network performs best with an accuracy greater than 80%, as expected from the non-linear dependence of the rainfall on the large-scale atmospheric thermodynamic conditions and circulation. Further tests of various prediction skill metrics and hindcasts also suggest this Neural Network prediction approach can significantly improve seasonal prediction skill than the dynamic predictions and regression based statistical predictions. Thus, this statistical prediction system could have

Seasonal Prediction of Taiwan's Streamflow Using Teleconnection Patterns

NASA Astrophysics Data System (ADS)

Chen, Chia-Jeng; Lee, Tsung-Yu

2017-04-01

Seasonal streamflow as an integrated response to complex hydro-climatic processes can be subject to activity of prevailing weather systems potentially modulated by large-scale climate oscillations (e.g., El Niño-Southern Oscillation, ENSO). To develop a seamless seasonal forecasting system in Taiwan, this study assesses how significant Taiwan's precipitation and streamflow in different seasons correlate with selected teleconnection patterns. Long-term precipitation and streamflow data in three major precipitation seasons, namely the spring rains (February to April), Mei-Yu (May and June), and typhoon (July to September) seasons, are derived at 28 upstream and 13 downstream catchments in Taiwan. The three seasons depict a complete wet period of Taiwan as well as many regions bearing similar climatic conditions in East Asia. Lagged correlation analysis is then performed to investigate how the precipitation and streamflow data correlate with predominant teleconnection indices at varied lead times. Teleconnection indices are selected only if they show certain linkage with weather systems and activity in the three seasons based on previous literature. For instance, the ENSO and Quasi-Biennial Oscillation, proven to influence East Asian climate across seasons and summer typhoon activity, respectively, are included in the list of climate indices for correlation analysis. Significant correlations found between Taiwan's precipitation and streamflow and teleconnection indices are further examined by a climate regime shift (CRS) test to identify any abrupt changes in the correlations. The understanding of existing CRS is useful for informing the forecasting system of the changes in the predictor-predictand relationship. To evaluate prediction skill in the three seasons and skill differences between precipitation and streamflow, hindcasting experiments of precipitation and streamflow are conducted using stepwise linear regression models. Discussion and suggestions for coping

Regional Arctic sea-ice prediction: potential versus operational seasonal forecast skill

NASA Astrophysics Data System (ADS)

Bushuk, Mitchell; Msadek, Rym; Winton, Michael; Vecchi, Gabriel; Yang, Xiaosong; Rosati, Anthony; Gudgel, Rich

2018-06-01

Seasonal predictions of Arctic sea ice on regional spatial scales are a pressing need for a broad group of stakeholders, however, most assessments of predictability and forecast skill to date have focused on pan-Arctic sea-ice extent (SIE). In this work, we present the first direct comparison of perfect model (PM) and operational (OP) seasonal prediction skill for regional Arctic SIE within a common dynamical prediction system. This assessment is based on two complementary suites of seasonal prediction ensemble experiments performed with a global coupled climate model. First, we present a suite of PM predictability experiments with start dates spanning the calendar year, which are used to quantify the potential regional SIE prediction skill of this system. Second, we assess the system's OP prediction skill for detrended regional SIE using a suite of retrospective initialized seasonal forecasts spanning 1981-2016. In nearly all Arctic regions and for all target months, we find a substantial skill gap between PM and OP predictions of regional SIE. The PM experiments reveal that regional winter SIE is potentially predictable at lead times beyond 12 months, substantially longer than the skill of their OP counterparts. Both the OP and PM predictions display a spring prediction skill barrier for regional summer SIE forecasts, indicating a fundamental predictability limit for summer regional predictions. We find that a similar barrier exists for pan-Arctic sea-ice volume predictions, but is not present for predictions of pan-Arctic SIE. The skill gap identified in this work indicates a promising potential for future improvements in regional SIE predictions.

Has the prediction of the South China Sea summer monsoon improved since the late 1970s?

NASA Astrophysics Data System (ADS)

Fan, Yi; Fan, Ke; Tian, Baoqiang

2016-12-01

Based on the evaluation of state-of-the-art coupled ocean-atmosphere general circulation models (CGCMs) from the ENSEMBLES (Ensemble-based Predictions of Climate Changes and Their Impacts) and DEMETER (Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction) projects, it is found that the prediction of the South China Sea summer monsoon (SCSSM) has improved since the late 1970s. These CGCMs show better skills in prediction of the atmospheric circulation and precipitation within the SCSSM domain during 1979-2005 than that during 1960-1978. Possible reasons for this improvement are investigated. First, the relationship between the SSTs over the tropical Pacific, North Pacific and tropical Indian Ocean, and SCSSM has intensified since the late 1970s. Meanwhile, the SCSSM-related SSTs, with their larger amplitude of interannual variability, have been better predicted. Moreover, the larger amplitude of the interannual variability of the SCSSM and improved initializations for CGCMs after the late 1970s contribute to the better prediction of the SCSSM. In addition, considering that the CGCMs have certain limitations in SCSSM rainfall prediction, we applied the year-to-year increment approach to these CGCMs from the DEMETER and ENSEMBLES projects to improve the prediction of SCSSM rainfall before and after the late 1970s.

Interannual Variability of Water Ice Clouds at Gale Crater

NASA Astrophysics Data System (ADS)

Martinez, G.; Giuranna, M.; McConnochie, T. H.; Tamppari, L.; Smith, M. D.; Vicente-Retortillo, Á.; Renno, N. O.; Kloos, J. L.; Moores, J. E.; Guzewich, S.

2017-12-01

The Aphelion Cloud Belt (ACB) is a water ice cloud band that encircles the planet longitudinally at latitudes ranging from about 10°S to 30°N during the northern spring and summer (aphelion season). The ACB has been studied extensively using satellite observations over the last two decades [1], showing little interannual variability from MY 24 to 34. The Mars Science Laboratory (MSL) mission has completed more than 1750 sols of measurements at Gale crater (4.5°S), from Ls 155° in MY 31 to Ls 33° in MY 34. Interestingly, MSL results from various instruments indicate that the ACB produces significant interannual variability at Gale crater during the aphelion season. In particular, near-noon retrievals of water ice opacity by the ChemCam instrument indicate an increase in water ice opacity up to 50% from MY 32 to 33 [2], further supported by analysis of UV [3] and ground temperature [4] data taken by the Rover Environmental Monitoring Station during MY 32 and 33. A weaker ( 5%) increase in water ice opacity in MY 33 relative to MY 32 was also observed from images taken during afternoon hours by the rover's Navigation Cameras [5]. We are analyzing simultaneous and noncontemporary satellite observations at the location of Gale made by the Planetary Fourier Spectrometer [6], Mars Climate Sounder, Thermal Emission Imaging System and Thermal Emission Spectrometer to shed light on the nature of the interannual variability of the ACB at Gale, and to locally understand the relation between the ACB and the water cycle. References:[1] Smith, M.D. (2008), Spacecraft observations of the martian atmosphere, Annu. Rev. Earth Planet. Sci. 36. [2] McConnochie, T. H., et al. (2017), Retrieval of Water Vapor Column Abundance and Aerosol Properties from ChemCam Passive Sky Spectroscopy, Icarus (submitted). [3] Vicente-Retortillo, Á., et al. (2017), Determination of dust aerosol particle size at Gale Crater using REMS UVS and Mastcam measurements, GRL, 44. [4] Vasavada, A.R. et al

Explaining and forecasting interannual variability in the flow of the Nile River

NASA Astrophysics Data System (ADS)

Siam, M. S.; Eltahir, E. A. B.

2014-05-01

The natural interannual variability in the flow of Nile River had a significant impact on the ancient civilizations and cultures that flourished on the banks of the river. This is evident from stories in the Bible and Koran, and from the numerous Nilometers discovered near ancient temples. Here, we analyze extensive data sets collected during the 20th century and define four modes of natural variability in the flow of Nile River, identifying a new significant potential for improving predictability of floods and droughts. Previous studies have identified a significant teleconnection between the Nile flow and the Eastern Pacific Ocean. El Niño-Southern Oscillation (ENSO) explains about 25% of the interannual variability in the Nile flow. Here, we identify, for the first time, a region in the southern Indian Ocean with similarly strong teleconnection to the Nile flow. Sea Surface Temperature (SST) in the region (50-80° E and 25-35° S) explains 28% of the interannual variability in the Nile flow. During those years with anomalous SST conditions in both Oceans, we estimate that indices of the SSTs in the Pacific and Indian Oceans can collectively explain up to 84% of the interannual variability in the flow of Nile. Building on these findings, we use classical Bayesian theorem to develop a new hybrid forecasting algorithm that predicts the Nile flow based on global models predictions of indices of the SST in the Eastern Pacific and Southern Indian Oceans.

The prediction of surface temperature in the new seasonal prediction system based on the MPI-ESM coupled climate model

NASA Astrophysics Data System (ADS)

Baehr, J.; Fröhlich, K.; Botzet, M.; Domeisen, D. I. V.; Kornblueh, L.; Notz, D.; Piontek, R.; Pohlmann, H.; Tietsche, S.; Müller, W. A.

2015-05-01

A seasonal forecast system is presented, based on the global coupled climate model MPI-ESM as used for CMIP5 simulations. We describe the initialisation of the system and analyse its predictive skill for surface temperature. The presented system is initialised in the atmospheric, oceanic, and sea ice component of the model from reanalysis/observations with full field nudging in all three components. For the initialisation of the ensemble, bred vectors with a vertically varying norm are implemented in the ocean component to generate initial perturbations. In a set of ensemble hindcast simulations, starting each May and November between 1982 and 2010, we analyse the predictive skill. Bias-corrected ensemble forecasts for each start date reproduce the observed surface temperature anomalies at 2-4 months lead time, particularly in the tropics. Niño3.4 sea surface temperature anomalies show a small root-mean-square error and predictive skill up to 6 months. Away from the tropics, predictive skill is mostly limited to the ocean, and to regions which are strongly influenced by ENSO teleconnections. In summary, the presented seasonal prediction system based on a coupled climate model shows predictive skill for surface temperature at seasonal time scales comparable to other seasonal prediction systems using different underlying models and initialisation strategies. As the same model underlying our seasonal prediction system—with a different initialisation—is presently also used for decadal predictions, this is an important step towards seamless seasonal-to-decadal climate predictions.