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The sources of predictability for the February 2021 cold air outbreak (CAO) over the central United States, which led to power grid failures and water delivery shortages in Texas, are diagnosed using a machine learning–based prediction model called a linear inverse model (LIM). The flexibility and low computational cost of the LIM allows its forecasts to be used for identifying and assessing the predictability of key physical processes. The LIM may also be run as a climate model for sensitivity and risk analysis for the same reasons. The February 2021 CAO was a subseasonal forecast of opportunity, as the LIM confidently predicted the CAO’s onset and duration four weeks in advance, up to two weeks earlier than other initialized numerical forecast models. The LIM shows that the February 2021 CAO was principally caused by unpredictable internal atmospheric variability and predictable La Niña teleconnections, with nominally predictable contributions from the previous month’s sudden stratospheric warming and the Madden–Julian oscillation. When run as a climate model, the LIM estimates that the February 2021 CAO was in the top 1% of CAO severity and suggests that similarly extreme CAOs could be expected to occur approximately every 20–30 years.

Anthropogenically forced-warming and La Niña forced-precipitation deficits caused at least a sixfold risk increase for compound extreme low precipitation and high temperature in California–Nevada from October 2020 to September 2021.

The skill of NOAA’s official monthly U.S. precipitation forecasts (issued in the middle of the prior month) has historically been low, having shown modest skill over the southern United States, but little or no skill over large portions of the central United States. The goal of this study is to explain the seasonal and regional variations of the North American subseasonal (weeks 3–6) precipitation skill, specifically the reasons for its successes and its limitations. The performances of multiple recent-generation model reforecasts over 1999–2015 in predicting precipitation are compared to uninitialized simulation skill using the atmospheric component of the forecast systems. This parallel analysis permits attribution of precipitation skill to two distinct sources: one due to slowly evolving ocean surface boundary states and the other to faster time-scale initial atmospheric weather states. A strong regionality and seasonality in precipitation forecast performance is shown to be analogous to skill patterns dictated by boundary forcing constraints alone. The correspondence is found to be especially high for the North American pattern of the maximum monthly skill that is achieved in the reforecast. The boundary forcing of most importance originates from tropical Pacific SST influences, especially those related to El Niño–Southern Oscillation. We discuss physical constraints that may limit monthly precipitation skill and interpret the performance of existing models in the context of plausible upper limits. Significance Statement Skillful subseasonal precipitation predictions have societal benefits. Over the United States, however, NOAA’s official U.S. monthly precipitation forecast skill has been historically low. Here we explore origins for skill of North American week-3 to week-6 precipitation predictions. Skill arising from initial weather states is compared to that arising from ocean surface boundary states alone. The monthly and seasonally varying pattern of U.S. monthly precipitation skill is appreciably derived from boundary constraints, linked especially with El Niño–Southern Oscillation. Forecasts of opportunity are identified, despite the low skill of monthly precipitation forecasts on average. Potential limits of monthly precipitation skill are explored that provide insight on the juxtaposition of “skill deserts” over the central United States with high skill regions over western North America.

Flash droughts, characterized by their unusually rapid intensification, have garnered increasing attention within the weather, climate, agriculture, and ecological communities in recent years due to their large environmental and socioeconomic impacts. Because flash droughts intensify quickly, they require different early warning capabilities and management approaches than are typically used for slower-developing “conventional” droughts. In this essay, we describe an integrated research-and-applications agenda that emphasizes the need to reconceptualize our understanding of flash drought within existing drought early warning systems by focusing on opportunities to improve monitoring and prediction. We illustrate the need for engagement among physical scientists, social scientists, operational monitoring and forecast centers, practitioners, and policy-makers to inform how they view, monitor, predict, plan for, and respond to flash drought. We discuss five related topics that together constitute the pillars of a robust flash drought early warning system, including the development of 1) a physically based identification framework, 2) comprehensive drought monitoring capabilities, and 3) improved prediction over various time scales that together 4) aid impact assessments and 5) guide decision-making and policy. We provide specific recommendations to illustrate how this fivefold approach could be used to enhance decision-making capabilities of practitioners, develop new areas of research, and provide guidance to policy-makers attempting to account for flash drought in drought preparedness and response plans.

Data-sparse hydroclimates across the globe are often the most vulnerable to climate shocks and their populations to food insecurity. Drought monitoring and famine early warning in these regions have for too long relied on poor parameterizations of atmospheric evaporative demand (E0)—no less than the demand side of drought and of consumptive use by agriculture—either relying on physically poor process representations of E0 or on climatological mean estimates. However, by exploiting the advent of long-term, spatially distributed, and accurate reanalyses of the land-atmosphere system and its drivers we can put new data to use to save livelihoods and lives by improving drought monitoring, famine early warning, and multi-scale agricultural risk assessment.Here we describe one such effort—to create a daily, long-term, accurate, global operational dataset of E0. Funded by the Famine Early Warning Systems Network (FEWS NET) and its partners, we have developed a nearly 42-year long, daily, 0.125-degree, global dataset of Penman-Monteith reference evapotranspiration as a fully physical metric of E0. This new E0 dataset is driven by the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2)—an accurate, fine-resolution land-surface/atmosphere reanalysis. We verified the accuracy of the dataset against (i) point-estimates of E0 derived by Southern African Science Service Centre for Climate Change and Adaptive Land Management (SASSCAL) initiative in Southern Africa, a region with sparse ground-truth data and significant humanitarian need, and (ii) on a spatially distributed basis against E0 derived from other reanalyses (Global Data Assimilation System and Princeton Global Forcing) that, although global, are otherwise unsuitable for operational food-security decision-making.We summarize the various uses to which the new E0 dataset is already being put in support of food-security monitoring and decision-making in food-insecure countries within the FEWS NET framework: to provide input data for a global implementation of the Evaporative Demand Drought Index (EDDI), which examines anomalies in E0 to permit early warning and ongoing monitoring of agricultural flash drought and hydrologic drought, both crucial drivers of food insecurity; and to diagnose the anomalies in E0 that lead to or signal drought into the relative contributions from its drivers, examining canonical droughts across Africa (e.g., the 2015 drought in Malawi, and the 2016 Horn of Africa drought, and the current multi-year East African drought). We also present use-cases that verify the operational applicability of the new E0 dataset in long-established drought, famine, crop- and pastoral-stress metrics, and in predictability assessments of drought forecasts. Driven by this new dataset, these analyses should significantly contribute to a more holistic understanding of drought and food-security across the African continent and the rest of the world.

October-September runoff increased 6% and 17% in the Upper (UMRB) and Lower (LMRB) Missouri River Basin, respectively, in a recent (1990-2019) compared to past (1960-1989) climate. The runoff increases were unanticipated, given various projections for semi-permanent drought and/or aridification in the North American Great Plains. Here, five transient coupled climate model ensembles are used to diagnose the effects of natural internal variability and anthropogenic climate change on the observed runoff increases, and to project UMRB and LMRB runoff to the mid-21st century. The runoff increases observed in the recent compared to the past climate were not due to anthropogenic climate change, but resulted mostly from an extreme occurrence of internal multi-decadal variability. High runoff resulted from large, mostly internally-generated, precipitation increases (6% in the UMRB and 5% in the LMRB) that exceeded simulated increases due to climate change forcing alone (0-2% inter-model range). The precipitation elasticity of runoff, which relates runoff sensitivity to precipitation differences in the recent compared to the past climate, led to 1-3-fold and 2-4-fold amplifications of runoff versus precipitation in the UMRB and LMRB, respectively. Without the observed precipitation increases in the recent compared to the past climate, effects of human-induced warming of about 1°C would alone have most likely induced runoff declines of 7% and 13% in the UMRB and LMRB, respectively. Ensemble model simulations overwhelmingly project lower UMRB and LRMB runoff by 2050 compared to 1990-2019, a change found to be insensitive to whether individual realizations experienced high flows in the recent climate.

Understanding the nature of precipitation over central southwest Asia, a data-sparse, semi-arid region, is crucial for anticipating future agricultural productivity and the likelihood of hazards such as flooding. However, the month-to-month evolution of daily precipitation characteristics, such as duration and amplitude, have not been extensively considered. Here we compare how daily precipitation and local vertical motion forcing – represented by warm conveyor belts (WCBs) – evolve from November to April over Afghanistan. Given the low amount of in-situ precipitation observations in the area, we first compare several precipitation estimates, indicating that the seasonal evolution of daily precipitation is consistent across estimates that incorporate satellite information. While these datasets agree on the timing of peak precipitation in February and March, total accumulation amounts vary substantially. Still, a common feature is that the majority of precipitation occurs on the few days when accumulation exceeds 4 mm, which are most frequent in February and March. Precipitation intensity, duration, and the associated circulation patterns evolve as winter progresses into spring, with notable differences within the months from January to April. El Niño conditions are generally associated with more heavy precipitation days than La Niña, consistent with past research, with both enhanced WCB frequency and moisture transport from lower latitudes observed during El Niño conditions, except for in January when neither precipitation nor WCB change. As such, our results support prior connections made between ENSO and seasonal-to-interannual circulation changes and extend this connection to one between the slowly-evolving ENSO influence and transient, synoptic-scale vertical motion represented by WCBs.

Diagnosis of rapidly developing springtime droughts in the central United States has mostly been made via numerous individual case studies rather than in an aggregate sense. This study investigates common aspects of subseasonal “meteorological drought” evolution, here defined as persistent precipitation minus evapotranspiration (P − ET) deficits, revealed in early (1 April–15 May) and late (16 May–30 June) spring composites of 5-day running mean JRA-55 reanalysis data for three different central U.S. regions during 1958–2018. On average, these droughts are initiated by a quasi-stationary Rossby wave packet (RWP), propagating from the western North Pacific, which arises about a week prior to drought onset. The RWP is related to a persistent ridge west of the incipient drought region and strong subsidence over it. This subsidence is associated with low-level divergent flow that dries the atmosphere and suppresses precipitation for roughly 1–2 weeks, and generally has a greater impact on the local moisture budget than does reduced poleward moisture transport. The resulting “dynamically driven” evaporative demand corresponds to a rapid drying of the root-zone soil moisture, which decreases around 40 percentiles within about 10 days. Anomalous near-surface warmth develops only after the P − ET deficit onset, as does anomalously low soil moisture that then lingers a month or more, especially in late spring. The horizontal scale of the RWPs, and of the related drought anomalies, decreases from early to late spring, consistent with the climatological change in the Pacific Rossby waveguide. Finally, while this composite analysis is based upon strong, persistent P − ET deficits, it still appears to capture much of the springtime development of “flash droughts” as well.

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2020 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

Diagnosing El Niño-Southern Oscillation (ENSO) predictability within operational forecast models is hindered by computational expense, resulting in hindcasts limited in their period of record, initialization frequency, and/or forecast leads. Here, we examine the multi-year predictability of ENSO since the late 1800s based on the subsequent evolution from anomalous states that most closely match observed sea surface temperature and height anomalies in 25 pre-industrial control simulations from the Coupled Model Intercomparison Project Phase 6. We found our ENSO forecast skill is comparable to twice-yearly 20th century hindcasts generated by a European operational forecasting system. However, our monthly initialization indicates that the so-called spring predictability barrier, presents throughout the century, does not impede second-year (~9-18 month leads) ENSO skill, which was notably high both in recent decades and towards the end of the 19th century. Overall, ENSO has exhibited a roughly U-shaped evolution in both amplitude and skill since the late 1800s.

Subseasonal forecasts of opportunity (SFOs) for precipitation over southwest Asia during January–March at lead times of 3–6 weeks are identified using elevated expected forecast skill from a linear inverse model (LIM), an empirical dynamical model that uses statistical relationships to infer the predictable dynamics of a system. The expected forecast skill from this LIM, which is based on the atmospheric circulation, tropical outgoing longwave radiation, and sea surface temperatures, captures the predictability associated with many relevant signals as opposed to just one. Two modes of variability, El Niño–Southern Oscillation (ENSO) and the Madden–Julian Oscillation (MJO), which themselves are predictable because of their slow variations, are related to southwest Asia precipitation SFOs. Strong El Niño events, as observed in 1983, 1998, and 2016, significantly increase the likelihood by up to 3-fold of an SFO 3–4 and 5–6 weeks in advance. Strong La Niña events, as observed in 1989, 1999, 2000, also significantly increase the likelihood of an SFO at those same lead times. High-amplitude MJO events in phases 2–4 and 6–8 of greater than one standardized departure also significantly increase the likelihood of an SFO 3–4 weeks in advance. Predictable atmospheric circulation patterns preceding anomalously wet periods indicate a role for enhanced tropical convection in the South Pacific convergence zone (SPCZ) region, while suppressed convection is observed preceding predictable dry periods. Anomalous heating in this region is found to distinguish wet and dry periods during both El Niño and La Niña conditions, although the atmospheric circulation response to the heating differs between each ENSO phase.

Subseasonal forecasts of opportunity (SFOs) for precipitation over southwest Asia during January–March at lead times of 3–6 weeks are identified using elevated expected forecast skill from a Linear Inverse Model (LIM), an empirical dynamical model that uses statistical relationships to infer the predictable dynamics of a system. The expected forecast skill from this LIM, which is based on the atmospheric circulation, tropical outgoing longwave radiation and sea surface temperatures, captures the predictability associated with many relevant signals as opposed to just one. Two modes of variability, El Niño-Southern Oscillation (ENSO) and the Madden Julian Oscillation (MJO), which themselves are predictable because of their slow variations, are related to southwest Asia precipitation SFOs. Strong El Nino events, as observed in 1983, 1998, and 2016, significantly increase the likelihood by up to threefold of an SFO 3–4 and 5–6 weeks in advance. Strong La Nina events, as observed in 1989, 1999, 2000, also significantly increased the likelihood of an SFO at those same lead times. High amplitude MJO events in phases 2–4 and 6–8 of greater than one standardized departure also significantly increases the likelihood of an SFO 3–4 weeks in advance. Predictable atmospheric circulation patterns preceding anomalously wet periods indicate a role for anomalous tropical convection in the SPCZ region, while suppressed convection is observed preceding predictable dry periods. Anomalous heating in this region is found to distinguish wet and dry periods during both El Niño and La Niña conditions, although the atmospheric circulation response to the heating differs between each ENSO phase.

The ability of six CMIP6 models to reproduce the observed cold season teleconnection between tropical Indo-Pacific sea surface temperatures (SSTs) and precipitation in Southwest Asia, the coastal Middle East (CME), and Northern Pakistan and India (NPI) is examined. The 1979-2014 period is analyzed, to maximize observations over both the tropical ocean and the regions. Nine historical simulations for the same period are examined for each model, to account for the internal variability of the coupled system. The teleconnection is examined in terms of SSTs, precipitation, 200 hPa geopotential heights, and derived quantities.All the models capture some of the broadest features of the teleconnections, but there is a wide range in the ability of the models to reproduce the magnitude and details. The differences appear related to both the models’ ability to capture the details of the tropical variability, including the position and strength of the precipitation anomalies in the Indo-west Pacific, as well as the models’ ability to accurately propagate the tropically-forced response into the region. The teleconnections to the CME and NPI regions on the eastern and western margins, respectively, of the strongest signal are very similar in structure and have similar results, except that the models’ ability to reproduce the strength and details of the teleconnection is even more limited, consistent with their marginal locations and known influence of other modes of variability. For all three areas, the wide range in model ability to capture the leading teleconnection suggests caution in interpreting climate regional projections.

The spatiotemporal evolution of daily southern Africa precipitation characteristics, and associated atmospheric circulation, related to El Niño and La Niña is examined across the region’s November–April wet season. Precipitation characteristics are examined in terms of monthly changes in daily average precipitation, the number of precipitation days, and the number of heavy precipitation days in three independently constructed estimates of observed precipitation during 1983–2018. Mechanisms related to precipitation changes, including contributions from mass divergence, water vapor transports, and transient eddies, are diagnosed using the atmospheric moisture budget based on the ERA5 reanalysis. El Niño is related to precipitation anomalies that build during December–March, the core of the rainy season, culminating in significantly below average values stretching across a semiarid region from central Mozambique to southeastern Angola. A broad anticyclone centered over Botswana drives these precipitation anomalies primarily through anomalous mass divergence, with moisture advection and transient eddies playing secondary roles. La Niña is related to significantly above average daily precipitation characteristics over all Africa south of 20°S in February and much less so during the other five months. February precipitation anomalies are primarily driven through mass divergence due to a strong anomalous cyclonic circulation, whereas a similar circulation is more diffuse during the other months. The spatiotemporal evolutions of anomalies in daily precipitation characteristics across southern Africa related to El Niño and La Niña are not equal and opposite. The robustness of an asymmetric evolution, which could have implications for subseasonal forecasts, needs to be confirmed with analysis of additional empirical data and established with climate model experimentation.

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2019 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

The 2017 flash drought arrived without early warning and devastated the U.S. northern Great Plains region comprising Montana, North Dakota, and South Dakota and the adjacent Canadian Prairies. The drought led to agricultural production losses exceeding $2.6 billion in the United States, widespread wildfires, poor air quality, damaged ecosystems, and degraded mental health. These effects motivated a multiagency collaboration among academic, tribal, state, and federal partners to evaluate drought early warning systems, coordination efforts, communication, and management practices with the goal of improving resilience and response to future droughts. This essay provides an overview on the causes, predictability, and historical context of the drought, the impacts of the drought, opportunities for drought early warning, and an inventory of lessons learned. Key lessons learned include the following: 1) building partnerships during nondrought periods helps ensure that proper relationships are in place for a coordinated and effective drought response; 2) drought information providers must improve their understanding of the annual decision cycles of all relevant sectors, including, and beyond, direct impacts in agricultural sectors; and 3) ongoing monitoring of environmental conditions is vital to drought early warning, given that seasonal forecasts lack skill over the northern Great Plains.

Year-to-year extreme alterations in California (CA) precipitation, denoted here as flips, present significant challenges to resource managers, emergency management officials, and the state’s economy and ecosystems generally. We evaluate regional (north, central, and south) and statewide flip behavior since 1571 CE utilizing instrumental data and paleoclimate reconstructions. Flips, defined as dry-to-wet and wet-to-dry consecutive alterations between the tailward 30th percentiles of the precipitation distribution, have occurred throughout this period without indication of systematic change through the recent time of modern anthropogenic forcing. Statewide “grand flips” are notably absent between 1892 and 1957; bootstrap Monte Carlo analysis indicates that this feature is consistent with random behavior. Composites for northeastern Pacific Ocean winter sea level pressure and jet-stream winds associated with flip events indicate anomalous high or low pressure during the core precipitation delivery season for dry or wet flip years, respectively, and jet-stream conditions that are also like those associated with individual dry or wet years. Equatorial Pacific sea surface temperatures play a partial role in both dry-to-wet and wet-to-dry events in central and southern CA in the longer-period reconstruction data, with response restricted primarily to southern CA in the smaller sample-size instrumental data. Knowledge of a prior year extreme, potentially representing initiation of a flip, provides no enhancement of prediction quality for the second year beyond that achievable from skillful seasonal prediction of equatorial Pacific sea surface temperatures. Overall, results indicate that the first-order nature of flip behavior from the later 1500s reflects the quasi–white noise nature of precipitation variability in CA, influenced secondarily by equatorial Pacific sea surface conditions, particularly in southern CA.

Seasonal to interannual forecasts made by coupled general circulation models (CGCMs) undergo strong climate drift and initialization shock, driving the model state away from its long-term attractor. Here we explore initializing directly on a model’s own attractor, using an analog approach in which model states close to the observed initial state are drawn from a “library” obtained from prior uninitialized CGCM simulations. The subsequent evolution of those “model-analogs” yields an ensemble forecast, without additional model integration. This technique is applied to CGCMs either used operationally by NCEP or as part of the CMIP6 dataset. By selecting from these long control runs those model states whose monthly SST and SSH anomalies best resemble the observations at initialization time, hindcasts are then made for leads of 1-36 months during 1958-2019. Deterministic and probabilistic skill measures of these model-analog hindcasts are comparable to, and in some regions better than, traditionally assimilation-initialized CGCM hindcasts after 1982, for both the individual models and the multi-model ensemble.On average, ENSO skill of AC>0.5 exists for forecast leads of 18 months for forecasts initialized in summer. More important, we find that not only were some notable ENSO events predictable two years (or more) ahead of time, but that we can both identify forecast “hits” and avoid “false alarms” -- at the time of forecast -- by using a simple forecast signal-to-noise metric (SNR; root-mean-squared ensemble mean divided by ensemble spread), determined from the large (O(100) member) model-analog ensemble. That is, our analog ensemble approach can be used to make actionable ocean predictions, where forecasts of opportunity can be identified well in advance.Since these long-lead hindcasts do not require full-field initialization, they have also been extended back prior to 1900. We find that while there has been considerable multi-decadal variation in seasonal ENSO skill, there has been no long-term trend for leads up to about 6-9 months. However, while multi-year ENSO skill appears to have also occurred in the past for a few large ENSO events, in the past thirty years it has occurred with considerably greater frequency, raising the possibility that it is a more recent phenomenon.

The socioeconomic stability of the Central Asian Republics in the Amu Darya watershed is sensitive to drought. Activities related to agriculture employ a large fraction of the population and are responsible for at least one-fifth of the gross domestic products of Afghanistan, Tajikistan, and Turkmenistan. Toward building a predictive understanding that may be applied to drought early warning practices, the characteristics, precursors and potential predictability of agricultural drought in the Amu Darya watershed are examined in a large ensemble of Community Earth System Model version 1 simulations during 1920–2019. Agricultural drought is examined over Upper and Lower regions of the Amu Darya watershed, which have different mean hydroclimates, and is defined by 1-m soil moisture deficits lasting three or more months. The likelihood of drought onset and demise is phase-locked with the seasonal cycle of precipitation of each region, but with some notable differences. For the Upper region, drought onset and demise are three times more likely to occur during Autumn and Spring than other seasons. For the Lower region, drought onset and demise are three times more likely to occur during November–April than during Summer. Precipitation anomalies drive drought onset and demise during the climatological wet periods of both regions while temperatures play a smaller role. The probability of drought onset and demise is modulated by La Nina and El Nino, which control the interannual variability of precipitation over the Central Asian Republics during their wet seasons, indicating that the state of the El Nino Southern Oscillation serves as a key predictor of agricultural drought phase changes.

Whether distinct wintertime U.S. climate conditions exist for central-Pacific (CP) versus eastern-Pacific (EP) El Niño events is explored using atmospheric and coupled ocean–atmospheric models. Results using the former agree with most prior studies indicating different U.S. temperature and precipitation patterns associated with El Niño flavors. Causes are traced to equatorial rainfall sensitivity to both magnitudes and spatial patterns of sea surface temperatures (SSTs) distinguishing CP and EP cases. Warmer east equatorial Pacific Ocean SSTs during EP than CP events, specifically for strong EP cases, are responsible for greater east equatorial Pacific rainfall, which displaces tropospheric circulation anomalies eastward over the Pacific–North American region. Weak-amplitude EP cases and all CP events since 1980 fail to excite east equatorial Pacific rainfall, thus not initiating the dynamical chain of effects characterizing strong EP cases. Over the contiguous United States, the difference in tropospheric circulations between strong EP and CP events describes a cyclonic pattern that renders the former colder and wetter. Regional signals include notably colder western and warmer eastern U.S. surface temperatures during EP versus CP events, and higher southwestern and southeastern U.S. precipitation during EP events. We demonstrate the important result—new to studies of observed El Niño flavor impacts—that coupled models largely reproduce the sensitivities of atmospheric models. Confirmed hereby is the realism of prior estimates of El Niño flavor impacts that relied on atmospheric models alone. We further examine predictability of El Niño flavors using coupled forecasts, demonstrating that SST distinctions between CP and EP events and their diverse U.S. wintertime impacts are predictable at least a season in advance.

The Facility for Weather and Climate Assessments (FACTS) developed at the NOAA Physical Sciences Laboratory is a freely available resource that provides the science community with analysis tools; multimodel, multiforcing climate model ensembles; and observational/reanalysis datasets for addressing a wide class of problems on weather and climate variability and its causes. In this paper, an overview of the datasets, the visualization capabilities, and data dissemination techniques of FACTS is presented. In addition, two examples are given that show the use of the interactive analysis and visualization feature of FACTS to explore questions related to climate variability and trends. Furthermore, we provide examples from published studies that have used data downloaded from FACTS to illustrate the types of research that can be pursued with its unique collection of datasets.

Flash droughts are a recently recognized type of extreme event distinguished by sudden onset and rapid intensification of drought conditions with severe impacts. They unfold on subseasonal-to-seasonal timescales (weeks to months), presenting a new challenge for the surge of interest in improving subseasonal-to-seasonal prediction. Here we discuss existing prediction capability for flash droughts and what is needed to establish their predictability. We place them in the context of synoptic to centennial phenomena, consider how they could be incorporated into early warning systems and risk management, and propose two definitions. The growing awareness that flash droughts involve particular processes and severe impacts, and probably a climate change dimension, makes them a compelling frontier for research, monitoring and prediction. Flash droughts, which develop over the course of weeks, are difficult to forecast given the current state of subseasonal-to-seasonal prediction. This Perspective offers operational and research definitions, places them in the broader context of climate and suggests avenues for future research.

Editors note: For easy download the posted pdf of the Explaining Extreme Events of 2018 is a very low-resolution file. A high-resolution copy of the report is available by clicking here. Please be patient as it may take a few minutes for the high-resolution file to download.

We present an offline paleo-data assimilation methodology that formally combines the analog assimilation method (AA) and the Kalman filter (KF), utilizing the KF as a postprocessor of the AA output. This methodology can be applied to reconstruct climate fields that are spatially separated from proxy-based reconstructions by using the spatial covariability generated by a climate model. Our method is applied to a set of spatially resolved and spatially consistent climate reconstructions of several variables reflecting different seasons, incorporating the application of methodological variants that have undergone rigorous testing in terms of both improving statistical methodology and physical interpretation. This contrasts with applications primarily based on transfer relationships of annual means of local, single variable or bivariate, climate model priors into paleo proxy states. The gains from adding the KF postprocessor are modest in our test case of reconstructing sea level pressure (SLP) geopotential height fields in the northeast Pacific, utilizing paleoclimatic temperature and moisture reconstructions in western North America. Notably, SLP reconstruction skill is enhanced in the oceanic region south of Alaska that is strongly associated with wet winters in western North America. The results suggest that the AA method is approaching optimality in this test case, driven by the quality of the paleoreconstruction information used to drive the AA process, along with the realism of the climate model employed, to which the KF postprocessing step is added. The derived reconstructions are then used for evaluation of the relationship between winter SLP and precipitation in California over the past ∼450 years.

From the Hindu Kush Mountains to the Registan desert, Afghanistan is a diverse landscape where droughts, floods, conflict, and economic market accessibility pose challenges for agricultural livelihoods and food security. The ability to remotely monitor environmental conditions is critical to support decision making for humanitarian assistance. The FEWS NET Land Data Assimilation System (FLDAS) global and Central Asia data streams described here combine meteorological reanalysis datasets and land surface models to generate routine estimates of snow-covered fraction, snow water equivalent, soil moisture, runoff and other variables representing the water and energy balance. This approach allows us to fill the gap created by the lack of in situ hydrologic data in the region. First, we describe the configuration of the FLDAS and the two resultant data streams: one, global, at ~1 month latency, provides monthly average outputs on a 10 km2 grid from 1982–present. The second data stream, Central Asia, at ~1 day latency, provides daily average outputs on a 1 km2 grid from 2001–present. We describe our verification of these data that are compared to other remotely sensed estimates as well as qualitative field reports. These data and value-added products (e.g., anomalies and interactive time series) are hosted by NASA and USGS data portals for public use. The global data stream with a longer record, is useful for exploring interannual variability, relationships with atmospheric-oceanic teleconnections (e.g., ENSO), trends over time, and monitoring drought. Meanwhile, the higher spatial resolution Central Asia data stream, with lower latency, is useful for simulating snow-hydrologic dynamics in complex topography for monitoring snowpack and flood risk.

Small changes in Pacific temperature gradients connected with the El Niño Southern Oscillation (ENSO) influence the Walker Circulation and are related to global climate anomalies. Therefore, it is of paramount importance to develop robust indices of their past behavior. Here, we reconstruct the difference in sea surface temperature between the west and central Pacific during ENSO, coined the West Pacific Gradient (WPG), based on the Last Millennium Paleo Hydrodynamics Data Assimilation. We show that the WPG tracks ENSO variability and strongly co-varies with the zonal gradient in Pacific sea surface temperature. We demonstrate that the WPG strength is related to significant atmospheric circulation and precipitation anomalies during historical El Niño and La Niña events by magnifying or weakening droughts and pluvials across the Indo-Pacific. We show that an extreme negative WPG coupled to a strong zonal Pacific temperature gradient is associated with enhanced megadroughts in North America between 1400 CE and the late sixteenth century. The twentieth century stands out in showing the most extreme swings between positive and negative WPG conditions over the past Millennium. We conclude that the WPG is a robust index together with ENSO indices to reveal past changes in Pacific zonal sea surface temperature gradient variability.

Characteristics and predictability of drought in the Midwestern United States, spanning the Great Plains to the Ohio Valley, at local and regional scales are examined during 1916-2015. Given vast differences in hydroclimatic variability across the Midwest, drought is evaluated in four regions identified using a hierarchical clustering algorithm applied to an integrated drought index based on soil moisture, snow water equivalent, and three-month runoff from land surface models forced by observed analyses. Highlighting the regions containing the Ohio Valley (OV) and Northern Great Plains (NGP), the OV demonstrates a preference for sub-annual droughts, the timing of which can lead to prevalent dry epochs, while the NGP demonstrates a preference for annual-to-multi-annual droughts. Regional drought variations are closely related to precipitation, resulting in a higher likelihood of drought onset or demise during wet seasons: March-November in the NGP and all year in the OV, with a preference for March-May and September-November. Due to the distinct dry season in the NGP, there is a higher likelihood of longer drought persistence, as the NGP is four times more likely to experience drought lasting at least one year compared to the OV. While drought variability in all regions and seasons are related to atmospheric wave trains spanning the Pacific-North American sector, longer-lead predictability is limited to the OV in December-February because it is the only region/season related to slow-varying sea surface temperatures consistent with El Niño-Southern Oscillation. The wave trains in all other regions appear to be generated in the atmosphere, highlighting the importance of internal atmospheric variability in shaping Midwestern drought.

Sharing simple ideas across a broad community of practitioners helps them to work together more effectively. For this reason, drought early warning systems spend a considerable effort on describing how hazards are detected and defined. Well-articulated definitions of drought provide a shared basis for collaboration, response planning, and impact mitigation. One very useful measure of agricultural drought stress has been the “Water Requirement Satisfaction Index” (WRSI). In this study, we develop a new, simpler metric of water requirement satisfaction, the Phenological Water Balance. We describe this metric, compare it to WRSI and yield statistics in a food-insecure region (east Africa), and show how it can be easily combined with analog-based rainfall forecasts to produce end-of-season estimates of growing season water deficits. In dry areas, the simpler Phenological Water Balance metric is very similar to the WRSI. In these regions, we show that the coupling between rainfall deficits and increased reference evapotranspiration amplifies the impacts of droughts. In wet areas, on the other hand, our new metric provides useful information about water excess—seasons that are so wet that they may not be conducive to good agricultural outcomes. Finally, we present a Phenological Water Balance-based forecast example, demonstrating how this framework can be easily used to translate assumptions about seasonal rainfall outcomes into predictions of growing season water deficits. Effective humanitarian relief efforts rely on early projections of these deficits to design and deploy appropriate targeted responses. At present, it is difficult to combine gridded satellite-gauge precipitation forecasts with climate forecasts. Our new metric helps overcome this obstacle. Future extensions could use the water requirement framework to contextualize other water supply indicators, like Actual evapotranspiration (AET) values derived from satellite observations or hydrologic models.