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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS2020

Prediction skill of the 2012 U.S. Great Plains flash drought in Subseasonal Experiment (SubX) models

Rapid-onset droughts, known as flash droughts, can have devastating impacts on agriculture, water resources, and ecosystems. The ability to predict flash droughts in advance would greatly enhance our preparation for them and potentially mitigate their impacts. Here, we investigate the prediction skill of the extreme 2012 flash drought over the U.S. Great Plains at subseasonal lead times (3 weeks or more in advance) in global forecast systems participating in the Subseasonal Experiment (SubX). An additional comprehensive set of subseasonal hindcasts with NASA’s GEOS model, a SubX model with relatively high prediction skill, was performed to investigate the separate contributions of atmospheric and land initial conditions to flash drought prediction skill. The results show that the prediction skill of the SubX models is quite variable. While skillful predictions are restricted to within the first two forecast weeks in most models, skill is considerably better (3-4 weeks or more) for certain models and initialization dates. The enhanced prediction skill is found to originate from two robust sources: 1) accurate soil moisture initialization once dry soil conditions are established, and 2) the satisfactory representation of quasi-stationary cross-Pacific Rossby wave trains that lead to the rapid intensification of flash droughts. Evidence is provided that the importance of soil moisture initialization applies more generally to central U.S. summer flash droughts. Our results corroborate earlier findings that accurate soil moisture initialization is important for skillful subseasonal forecasts and highlight the need for additional research on the sources and predictability of drought-inducing quasi-stationary atmospheric circulation anomalies.

Anthony M DeAngelis↗
NASA NTRS2016

A Satellite Data-Driven, Client-Server Decision Support Application for Agricultural Water Resources Management

Water cycle extremes such as droughts and floods present a challenge for water managers and for policy makers responsible for the administration of water supplies in agricultural regions. In addition to the inherent uncertainties associated with forecasting extreme weather events, water planners need to anticipate water demands and water user behavior in a typical circumstances. This requires the use decision support systems capable of simulating agricultural water demand with the latest available data. Unfortunately, managers from local and regional agencies often use different datasets of variable quality, which complicates coordinated action. In previous work we have demonstrated novel methodologies to use satellite-based observational technologies, in conjunction with hydro-economic models and state of the art data assimilation methods, to enable robust regional assessment and prediction of drought impacts on agricultural production, water resources, and land allocation. These methods create an opportunity for new, cost-effective analysis tools to support policy and decision-making over large spatial extents. The methods can be driven with information from existing satellite-derived operational products, such as the Satellite Irrigation Management Support system (SIMS) operational over California, the Cropland Data Layer (CDL), and using a modified light-use efficiency algorithm to retrieve crop yield from the synergistic use of MODIS and Landsat imagery. Here we present an integration of this modeling framework in a client-server architecture based on the Hydra platform. Assimilation and processing of resource intensive remote sensing data, as well as hydrologic and other ancillary information occur on the server side. This information is processed and summarized as attributes in water demand nodes that are part of a vector description of the water distribution network. With this architecture, our decision support system becomes a light weight 'app' that connects to the server to retrieve the latest information regarding water demands, land use, yields and hydrologic information required to run different management scenarios. Furthermore, this architecture ensures all agencies and teams involved in water management use the same, up-to-date information in their simulations.

Agricultural↗
NASA NTRS2014

Comparing Evaporative Sources of Terrestrial Precipitation and Their Extremes in MERRA Using Relative Entropy

A quasi-isentropic back trajectory scheme is applied to output from the Modern Era Retrospective-analysis for Research and Applications and a land-only replay with corrected precipitation to estimate surface evaporative sources of moisture supplying precipitation over every ice-free land location for the period 1979-2005. The evaporative source patterns for any location and time period are effectively two dimensional probability distributions. As such, the evaporative sources for extreme situations like droughts or wet intervals can be compared to the corresponding climatological distributions using the method of relative entropy. Significant differences are found to be common and widespread for droughts, but not wet periods, when monthly data are examined. At pentad temporal resolution, which is more able to isolate floods and situations of atmospheric rivers, values of relative entropy over North America are typically 50-400 larger than at monthly time scales. Significant differences suggest that moisture transport may be the key to precipitation extremes. Where evaporative sources do not change significantly, it implies other local causes may underlie the extreme events.

Climatological Distributions↗
NASA NTRS2002

Effects of Interannual Climate Variability on Water Availability and Productivity in Capoeira and Crops Under Traditional and Alternative Shifting Cultivation

Regenerating forests play an important role in long-term carbon sequestration and sustainable landuse as they act as potentially important carbon and nutrient sinks during the shifting agriculture fallow period. The long-term functioning of capoeira. is increasingly threatened by a shortening fallow period during shifting cultivation due to demographic pressures and associated increased vulnerability to severe climatic events. Declining productivity and functioning of fallow forests of shifting cultivation combined with progressive loss of nutrients by successive burning and cropping activities has resulted in declining agricultural productivity. In addition to the effects of intense land use practices, droughts associated with El Nino events are becoming more frequent and severe in moist tropical forests and negative effects on capoeira productivity could be considerable. In Igarape-Acu (near Belem, Para), we hypothesize that experimental alternative landuse/clearing practices (mulching and fallow vegetation improvement by planting with fast-growing leguminous tree species) may make capoeira and agriculture more resilient to the effects of agricultural pressures and drought through (1) increased biomass, soil organic matter and associated increase in soil water storage, and nutrient retention and (2) greater rooting depth of trees planted for fallow improvement. This experimental practice (moto mechanized chop-and-mulch with fallow improvement) has resulted increased soil moisture during the cropping phase, reduced loss of nutrients and organic matter, and higher rates of secondary-forest biomass accumulation. We present preliminary data on water relations during the dry season of 2001 in capoeira and crops for both traditional slash-and-burn and alternative chop-and-mulch practices. These data will be used to test IKONOS data for the detection of moisture status differences. The principal goal of the research is to determine the extent to which capoeira and agricultural fields are susceptible to extreme climate events (drought) under contrasting landuse/clearing practices.

Guild, Liane S.↗
NASA NTRSDate not supplied

The Application of Remote Sensing using NASA Earth Observations paired with Sociodemographic Indicators to Identify Communities Most Susceptible to Urban Heat Exposure in Austin, Texas

In recent years, Austin, Texas has experienced an increase in population and urban development. Additionally, the City’s climate—already characterized by periods of extreme heat and drought—continues to change. As temperatures and demand for utilities and cooling resources rise, the number of heat-related deaths and illnesses in socially vulnerable populations (e.g., older, lower-income populations) is expected to increase. The City of Austin, The University of Texas at Austin (UT Austin), and The University of Texas Health Science Center at Houston (UT Health) partnered with NASA DEVELOP to examine the distribution of urban heat throughout the City. This project used land surface temperature, greenness, plant water content, and urban surface material analysis parameters derived from NASA Earth observations from Landsat 8 Operational Land Imager (OLI), Landsat 8 Thermal Infrared Sensor (TIRS), and Aqua and Terra Moderate Resolution Imaging Spectroradiometer (MODIS). The project team produced three different indices to create a spatial analysis for the study area including a social vulnerability index (SoVI), heat exposure index (HEI), and an overall heat priority index (HPI) score. This overall score was determined with a weighted analysis of heat-related environmental variables from NASA Earth observations and socioeconomic data from the 2019 American Community Survey. Based on the HPI score, the project team identified 121 census block groups out of 605 total that are designated as being most at risk of adverse impacts from extreme heat events. To test the sensitivity of the HPI, the team used a Monte Carlo analysis using different approaches for geographic scale, variable inclusion, census uncertainty, and index aggregation. Based on the sensitivity analysis, the resulting HPI score showed the metric was consistent with the baseline HPI. This provided increased confidence the score can be used as a tool to make informed infrastructure improvement plans in targeted areas (e.g., siting of cooling centers) and ensure equitable sustainable development.

Ryan Hammock↗
NASA NTRS2008

Remote Sensing for Food Security Monitoring in Afghanistan

Two decades of war have severely weakened Afghanistan s economy and infrastructure. Along with larger impacts on civil stability, education and health care, the current conflict in Afghanistan has resulted in widespread hunger and destitution. The 2005 National Risk and Vulnerability Assessment conducted by the United Nations found that 6.6 million Afghans do not meet their minimum food requirements and approximately 400,000 people each year are seriously affected by natural disasters, such as droughts, floods and extreme weather conditions. Given the poor security situation in the country, systems that will enable remote observations of variations of climate and their impacts on food production are critical for providing an appropriate and timely response. This chapter describes the remote sensing systems and food security analyses that the US Agency for International Development s Famine Early Warning Systems Network (FEWS NET) conducts in Afghanistan to monitor and provide information to international donors to ensure that adequate assistance is provided during this time of development and recovery.

Brown, Molly E.↗
NASA NTRS2011

NASA Water Resources Program

With increasing population pressure and water usage coupled with climate variability and change, water issues are being reported by numerous groups as the most critical environmental problems facing us in the 21st century. Competitive uses and the prevalence of river basins and aquifers that extend across boundaries engender political tensions between communities, stakeholders and countries. In addition to the numerous water availability issues, water quality related problems are seriously affecting human health and our environment. The potential crises and conflicts especially arise when water is competed among multiple uses. For example, urban areas, environmental and recreational uses, agriculture, and energy production compete for scarce resources, not only in the Western U.S. but throughout much of the U.S. and also in numerous parts of the world. Mitigating these conflicts and meeting water demands and needs requires using existing water resources more efficiently. The NASA Water Resources Program Element works to use NASA products and technology to address these critical water issues. The primary goal of the Water Resources is to facilitate application of NASA Earth science products as a routine use in integrated water resources management for the sustainable use of water. This also includes the extreme events of drought and floods and the adaptation to the impacts from climate change. NASA satellite and Earth system observations of water and related data provide a huge volume of valuable data in both near-real-time and extended back nearly 50 years about the Earth's land surface conditions such as precipitation, snow, soil moisture, water levels, land cover type, vegetation type, and health. NASA Water Resources Program works closely to use NASA and Earth science data with other U.S. government agencies, universities, and non-profit and private sector organizations both domestically and internationally. The NASA Water Resources Program organizes its projects under five functional themes. I) Streamflow and Flood Forecasting 2) Water Supply and Irrigation (includes evapotranspiration) 3) Drought 4) Water Quality 5) Climate and Water Resources. To maximize this activity NASA Water Resources Program works closely with other government agencies (e.g., the National Oceanic and Atmospheric Administration (NOAA); the U.S. Department of Agriculture (USDA); the U.S. Geological Survey (USGS); the Environmental Protection Agency (EPA), USAID, the Air Force Weather Agency (AFWA)), universities, non-profit national and international organizations, and the private sector. The NASA Water Resources program currently is funding 21 active projects under the functional themes (http://wmp.gsfc.nasa.gov & http://science.nasa.gov/earth-science/applied-sciences/).

Toll, David L.↗
NASA NTRS2011

Climate Teleconnections and Recent Patterns of Human and Animal Disease Outbreaks

Recent clusters of outbreaks of mosquito-borne diseases (Rift Valley fever and chikungunya) in Africa and parts of the Indian Ocean islands illustrate how interannual climate variability influences the changing risk patterns of disease outbreaks. Extremes in rainfall (drought and flood) during the period 2004 - 2009 have privileged different disease vectors. Chikungunya outbreaks occurred during the severe drought from late 2004 to 2006 over coastal East Africa and the western Indian Ocean islands and in the later years India and Southeast Asia. The chikungunya pandemic was caused by a Central/East African genotype that appears to have been precipitated and then enhanced by global-scale and regional climate conditions in these regions. Outbreaks of Rift Valley fever occurred following excessive rainfall period from late 2006 to late 2007 in East Africa and Sudan, and then in 2008 - 2009 in Southern Africa. The shift in the outbreak patterns of Rift Valley fever from East Africa to Southern Africa followed a transition of the El Nino/Southern Oscillation (ENSO) phenomena from the warm El Nino phase (2006-2007) to the cold La Nina phase (2007-2009) and associated patterns of variability in the greater Indian Ocean basin that result in the displacement of the centres of above normal rainfall from Eastern to Southern Africa. Understanding the background patterns of climate variability both at global and regional scale and their impacts on ecological drivers of vector borne-diseases is critical in long-range planning of appropriate response and mitigation measures.

Anyamba, Assaf↗
NASA NTRS2018

Recent Rainfall Decline in West Africa Due to Enhanced Biomass Burning and Dust Emission

Rainfall in West Africa during boreal summer is primarily controlled by the low-level southerly monsoon flow that transports moisture from the Gulf of Guinea to the region and the African Easterly Jet that modulates the convective systems. However, the role of aerosols in rainfall variability of this region, despite the large emissions of dust and black carbon in Africa, is not well-understood. Our study reveals a decline in precipitation over Southern West Africa (SWA) in the past two decades that its trend and interannual variability present an indirect relationship with the aerosols loadings. Examining the local and remote sources of aerosols, we found that the latter have a larger contribution. We have used MERRA-2 large scale atmospheric dynamics and physics data to determine the mechanisms responsible for aerosol-rainfall interactions in the region. Our results have important implications for the people and ecosystem as the increasing rates of land use change, deforestation and urbanization in the continent are expected to enhance the aerosols. This will result in an increase in the frequency and intensity of the extreme events including droughts.

Dezfuli, Amin↗
NASA NTRS2019

Evaluation Analysis of NASA SMAP L3 and L4 and SPoRT-LIS Soil Moisture Data in the United States

Soil moisture has a critical role in the development, frequency and persistence of climatic and hydrologic extremes such as drought, heat wave and flooding events. In situ soil moisture data are uneven and sparse in time and space. This highlights the need to utilize other soil moisture sources to fill this spatiotemporal gap. The goal of this study is to validate one satellite-based and two model-based soil moisture datasets with in situ data across the United States. Soil moisture information from the Soil Moisture Active Passive (SMAP) enhanced level 3 (L3) (SMAP L3) and modeled level 4 (L4) (SMAP L4) data at 9-km resolution and Short-term Prediction Research and Transition-Land Information System (SPoRT-LIS) modeled at 3-km resolution were selected for evaluation. SPoRT-LIS is a near real-time, high resolution operational land analysis data. Ground-based data were obtained from the North American Soil Moisture Database (NASMD) for 362 stations. Seven statistical indicators including anomaly, Spearman, and Pearson correlation coefficients, the systematic error (Bias), root mean square error (RMSE), unbiased root mean square error (ubRMSE), and normalized standard deviation (SDV) were used to evaluate the satellite- and model-derived soil moisture data. In addition, the triple collocation (TC) error model was used to measure the error among SMAP L4, SPoRT-LIS and ground-based data. This study assesses which satellite or modeled dataset is most appropriate for specific times and locations to use as a surrogate for in situ observations. Temporal and spatial analysis demonstrated that, overall, SMAP L4 performed better than SMAP L3 and SPoRT-LIS. Strong agreement was observed between SMAP L4 and in situ observations (ρ = 0.53, Bias = −0.006) in all seasons and most regions with various land covers, especially in winter and in the central regions of the United States. For croplands, SMAP L4 presented the best agreement with in situ data, analyzing all period (ρ = 0.60) and non-winter period (ρ = 0.61) separately.

Tavakol, Ameneh↗
NASA NTRS2017

The Past and Future Changes in Climate of the Rice-Wheat Cropping Zone in Punjab, Pakistan

Agriculture is ranked top among Pakistan economic sectors vulnerable to the potential impacts of climate change. The agricultural production system is directly affected by weather inputs (temperature, solar radiation, and rainfall) that are projected to change in future (following increases in carbon dioxide and other greenhouse gasses). Although Climatic extremes such as drought, floods and heat waves, are expected to increase with detrimental consequences for agriculture and livestock production, yet changes in mean climates also pose challenges to sustainable development. This study presents climate change results for five districts within the major rice-wheat productivity zone of Punjab province in Pakistan. The results are focused on RCP8.5 mid-century (2040-2069) scenarios derived from five global climate models (GCMs) output and the Kharif (June-October) and Rabi (November-April) seasons. Analysis of recent historical weather data of Sialkot and Sheikhupura districts shows an increase in minimum temperatures and maximum temperatures and a large variation in rainfall. This temperature change and variability in rainfall are expected to enhance further as we approach 2050s. The mean maximum temperature is projected to increase by 2-2.5 ºC during the rice growing (Kharif) season and 2.4-2.7ºC during the wheat growing (Rabi) season. Rainfall during the rice growing season is more uncertain, with projections indicating an increase of 25%-35 % in the study region, while a minimal change is expected during the Rabi season. Climax of the rice growing season corresponds with the projected increase in monsoon intensity, leaving no doubt about the crop water demand satisfaction. However, a simultaneous increase in the day and night temperature may affect the growth and development of some critical phenological stages and thus could impact final yield.

Climate Change↗
NASA NTRS2023

Flood Impacts on Net Ecosystem Exchange in the Midwestern and Southern United States in 2019

Climate extremes such as droughts, floods, heatwaves, frosts, and windstorms add considerable variability to the global year-to-year increase in atmospheric CO(2) through their influence on terrestrial ecosystems. While the impact of droughts on terrestrial ecosystems has received considerable attention, the response to flooding is not well understood. To improve upon this knowledge, the impact of the 2019 anomalously wet conditions over the Midwest and Southern US on CO(2) vegetation fluxes is examined in the context of 2017–2018 when such precipitation anomalies were not observed. CO(2) is simulated with NASA's Global Earth Observing System (GEOS) combined with the Low-order Flux Inversion, where fluxes of CO(2) are estimated using a suite of remote sensing measurements including greenness, night lights, and fire radiative power as well as with a bias correction based on insitu observations. Net ecosystem exchange CO(2) tracers are separated into the three regions covering the Midwest, South, and Eastern Texas and adjusted to match CO(2) observations from towers located in Iowa, Mississippi, and Texas. Results indicate that for the Midwestern region consisting primarily of corn and soybeans crops, flooding contributes to a 15%–25% reduction of annual net carbon uptake in 2019 in comparison to 2017 and 2018. These results are supported by independent reports of changes in agricultural activity. For the Southern region, comprised mainly of non-crop vegetation, annual net carbon uptake is enhanced in 2019 by about 10%–20% in comparison to 2017 and 2018. These outcomes show the heterogeneity in effects that excess wetness can bring to diverse ecosystems.

Nikolay Balashov↗
NASA NTRS2014

Abrupt Increases in Amazonian Tree Mortality Due to Drought-Fire Interactions

Interactions between climate and land-use change may drive widespread degradation of Amazonian forests. High-intensity fires associated with extreme weather events could accelerate this degradation by abruptly increasing tree mortality, but this process remains poorly understood. Here we present, to our knowledge, the first field-based evidence of a tipping point in Amazon forests due to altered fire regimes. Based on results of a large-scale, longterm experiment with annual and triennial burn regimes (B1yr and B3yr, respectively) in the Amazon, we found abrupt increases in fire-induced tree mortality (226 and 462%) during a severe drought event, when fuel loads and air temperatures were substantially higher and relative humidity was lower than long-term averages. This threshold mortality response had a cascading effect, causing sharp declines in canopy cover (23 and 31%) and aboveground live biomass (12 and 30%) and favoring widespread invasion by flammable grasses across the forest edge area (80 and 63%), where fires were most intense (e.g., 220 and 820 kW x m(exp −1)). During the droughts of 2007 and 2010, regional forest fires burned 12 and 5% of southeastern Amazon forests, respectively, compared with less than 1% in nondrought years. These results show that a few extreme drought events, coupled with forest fragmentation and anthropogenic ignition sources, are already causing widespread fire-induced tree mortality and forest degradation across southeastern Amazon forests. Future projections of vegetation responses to climate change across drier portions of the Amazon require more than simulation of global climate forcing alone and must also include interactions of extreme weather events, fire, and land-use change.

MODIS↗
NASA NTRS2016

Simulating US Agriculture in a Modern Dust Bowl Drought

Drought-induced agricultural loss is one of the most costly impacts of extreme weather, and without mitigation, climate change is likely to increase the severity and frequency of future droughts. The Dust Bowl of the 1930s was the driest and hottest for agriculture in modern US history. Improvements in farming practices have increased productivity, but yields today are still tightly linked to climate variation and the impacts of a 1930s-type drought on current and future agricultural systems remain unclear. Simulations of biophysical process and empirical models suggest that Dust-Bowl-type droughts today would have unprecedented consequences, with yield losses approx.50% larger than the severe drought of 2012. Damages at these extremes are highly sensitive to temperature, worsening by approx.25% with each degree centigrade of warming. We find that high temperatures can be more damaging than rainfall deficit, and, without adaptation, warmer mid-century temperatures with even average precipitation could lead to maize losses equivalent to the Dust Bowl drought. Warmer temperatures alongside consecutive droughts could make up to 85% of rain-fed maize at risk of changes that may persist for decades. Understanding the interactions of weather extremes and a changing agricultural system is therefore critical to effectively respond to, and minimize, the impacts of the next extreme drought event.

climate change impacts↗
NASA NTRS2022

Interactive Biogenic Emissions and Drought Stress Effects on Atmospheric Composition in NASA GISS ModelE

Drought is a hydroclimatic extreme that causes perturbations to the terrestrial biosphere and acts as a stressor on vegetation, affecting emissions patterns. During severe drought, isoprene emissions are reduced. In this paper, we focus on capturing this reduction signal by implementing a new percentile isoprene drought stress (y d ) algorithm in NASA GISS ModelE based on the MEGAN3 (Model of Emissions of Gases and Aerosols from Nature Version 3) approach as a function of a photosynthetic parameter (V c,max ) and water stress (β). Four global transient simulations from 2003–2013 are used to demonstrate the effect without y d (Default_ModelE) and with online y d (DroughtStress_ModelE). DroughtStress_ModelE is evaluated against the observed isoprene measurements at the Missouri Ozarks AmeriFlux (MOFLUX) site during the 2012 severe drought where improvements in the correlation coefficient indicate it is a suitable drought stress parameterization to capture the reduction signal during severe drought. The application of y d globally leads to a decadal average reduction of ∼ 2.7 %, which is equivalent to ∼14.6 Tg yr -1 of isoprene. The changes have larger impacts in regions such as the southeastern US. DroughtStress_ModelE is validated using the satellite ΩHCHO column from the Ozone Monitoring Instrument (OMI) and surface O 3 observations across regions of the US to examine the effect of drought on atmospheric composition. It was found that the inclusion of isoprene drought stress reduced the overestimation of ΩHCHO in Default_ModelE during the 2007 and 2011 southeastern US droughts and led to improvements in simulated O 3 during drought periods. We conclude that isoprene drought stress should be tuned on a model-by-model basis because the variables used in the parameterization responses are relative to the land surface model hydrology scheme (LSM) and the effects of y d application could be larger than seen here due to ModelE not having large biases of isoprene during severe drought.

drought stress effects↗
NASA NTRS2012

Climate Variability and Weather Extremes: Model-Simulated and Historical Data

Extremes in weather and climate encompass a wide array of phenomena including tropical storms, mesoscale convective systems, snowstorms, floods, heat waves, and drought. Understanding how such extremes might change in the future requires an understanding of their past behavior including their connections to large-scale climate variability and trends. Previous studies suggest that the most robust findings concerning changes in short-term extremes are those that can be most directly (though not completely) tied to the increase in the global mean temperatures. These include the findings that (IPCC 2007): There has been a widespread reduction in the number of frost days in mid-latitude regions in recent decades, an increase in the number of warm extremes, particularly warm nights, and a reduction in the number of cold extremes, particularly cold nights. For North America in particular (CCSP SAP 3.3, 2008): There are fewer unusually cold days during the last few decades. The last 10 years have seen a lower number of severe cold waves than for any other 10-year period in the historical record that dates back to 1895. There has been a decrease in the number of frost days and a lengthening of the frost-free season, particularly in the western part of North America. Other aspects of extremes such as the changes in storminess have a less clear signature of long term change, with considerable interannual, and decadal variability that can obscure any climate change signal. Nevertheless, regarding extratropical storms (CCSP SAP 3.3, 2008): The balance of evidence suggests that there has been a northward shift in the tracks of strong low pressure systems (storms) in both the North Atlantic and North Pacific basins. For North America: Regional analyses suggest that there has been a decrease in snowstorms in the South and lower Midwest of the United States, and an increase in snowstorms in the upper Midwest and Northeast. Despite the progress already made, our understanding of the basic mechanisms by which extremes vary is incomplete. As noted in IPCC (2007), Incomplete global data sets and remaining model uncertainties still restrict understanding of changes in extremes and attribution of changes to causes, although understanding of changes in the intensity, frequency and risk of extremes has improved. Separating decadal and other shorter-term variability from climate change impacts on extremes requires a better understanding of the processes responsible for the changes. In particular, the physical processes linking sea surface temperature changes to regional climate changes, and a basic understanding of the inherent variability in weather extremes and how that is impacted by atmospheric circulation changes at subseasonal to decadal and longer time scales, are still inadequately understood. Given the fundamental limitations in the time span and quality of global observations, substantial progress on these issues will rely increasingly on improvements in models, with observations continuing to play a critical role, though less as a detection tool, and more as a tool for addressing physical processes, and to insure the quality of the climate models and the verisimilitude of the simulations (CCSP SAP 1.3, 2008).

Schubert, Siegfried D.↗
NASA NTRS2018

Climate and the Global Famine of 1876-78

From 1875 to 1878, concurrent multiyear droughts in Asia, Brazil, and Africa, referred to as the Great Drought, caused widespread crop failures, catalyzing the so-called Global Famine, which had fatalities exceeding 50 million people and long-lasting societal consequences. Observations, paleoclimate reconstructions, and climate model simulations are used 1) to demonstrate the severity and characterize the evolution of drought across different regions, and 2) to investigate the underlying mechanisms driving its multiyear persistence. Severe or record-setting droughts occurred on continents in both hemispheres and in multiple seasons, with the "Monsoon Asia" region being the hardest hit, experiencing the single most intense and the second most expansive drought in the last 800 years. The extreme severity, duration, and extent of this global event is associated with an extraordinary combination of preceding cool tropical Pacific conditions (1870-76), a record-breaking El Nino (1877-78), a record strong Indian Ocean dipole (1877), and record warm North Atlantic Ocean (1878) conditions. Composites of historical analogs and two sets of ensemble simulations - one forced with global sea surface temperatures (SSTs) and another forced with tropical Pacific SST - were used to distinguish the role of the extreme conditions in different ocean basins. While the drought in most regions was largely driven by the tropical Pacific SST conditions, an extreme positive phase of the Indian Ocean dipole and warm North Atlantic SSTs, both likely aided by the strong El Nino in 1877-78, intensified and prolonged droughts in Australia and Brazil, respectively, and extended the impact to northern and southeastern Africa. Climatic conditions that caused the Great Drought and Global Famine arose from natural variability, and their recurrence, with hydrological impacts intensified by global warming, could again potentially undermine global food security.

multi-year drought↗
NASA NTRS2019

Negative extreme events in gross primary productivity and their drivers in China during the past three decades

Climate extremes have remarkable impacts on ecosystems and are expected to increase with future global warming. However, only few studies have focused on the ecological extreme events and their drivers in China. In this study, we carried out an analysis of negative extreme events in gross primary productivity (GPP) in China and the sub-regions during 1982–2015, using monthly GPP simulated by 12 process-based models (TRENDYv6) and an observation-based model (Yao-GPP). Extremes were defined as the negative 5th percentile of GPP anomalies, which were further merged into individual extreme events using a three-dimensional contiguous algorithm. Spatio-temporal patterns of negative GPP anomalies were analyzed by taking the 1000 largest extreme events into consideration. Results showed that the effects of extreme events decreased annual GPP by 2.8% (i.e. 208 TgC/year) in TRENDY models and 2.3% (i.e. 151 TgC/year) in Yao-GPP. Hotspots of extreme GPP deficits were mainly observed in North China (−53 gC/sq.m year) in TRENDY models and Northeast China (−42 gC/sq.m year) in Yao-GPP. For China as a whole, attribution analyses suggested that extreme low precipitation was associated with 40%–50% of extreme negative GPP events. Most events in northern and western China could be explained by meteorological droughts (i.e. low precipitation) while GPP extreme events in southern China were more associated with temperature extremes, in particular with cold spells. GPP was revealed to be much more sensitive to heat/drought than to cold/wet extreme events. Combined with projected changes in climate extremes in China, GPP negative anomalies caused by drought events in northern China and by temperature extremes in southern China might be more prominent in the future.

Climate change↗