Spatial Pattern Of Precipitation Anomalies Research Articles

Two deep learning-based bias-correction pathways improve summer precipitation prediction over China

As most global climate models (GCM) suffer from large biases in simulating/predicting summer precipitation over China, it is of great importance to develop suitable bias-correction methods. This study proposes two pathways of bias-correction with deep learning (DL) models incorporated. One is the deterministic pathway (DP), in which the bias correction is directly applied to the precipitation forecasts. The other one, namely the probability pathway (PP), corrects the forecasted precipitation anomalies using a conditional probability method before being added to the observational climatology. These two pathways have been applied to correct the precipitation forecasts based on a GCM prediction system Nanjing University of Information Science and Technology Climate Forecast System version 1.0 (NUIST-CFS1.0). The applications of DL models in the both pathways yield higher resolution of corrected predictions than the uncorrected ones. Both pathways improve summer precipitation predictions at 4-month lead. Moreover, the DP correction shows a better performance in predicting extreme precipitation, while the PP is proficient in correcting the spatial pattern of precipitation anomalies over China. The present results highlight the importance of the application of appropriate correction strategy for different prediction purposes.

The role of ENSO flavours and TNA on recent droughts over Amazon forests and the Northeast Brazil region

AbstractAmazon tropical forests and the semiarid Northeast Brazil (NEB) region have registered very severe droughts during the last two decades, with a frequency that may have exceeded natural climate variability. Severe droughts impact the physiological response of Amazon forests, decreasing the availability to absorb atmospheric CO2, as well as biodiversity and increasing risk of fires. Droughts on this region also affect population by isolating them due to anomalous low river levels. Impacts of droughts over NEB region are related to water and energy security and subsistence agriculture. Most drought episodes over Amazonia and NEB are associated with El Niño (EN) events, anomalous warming over the Tropical North Atlantic (TNA), and even an overlapping among them. However, not all the dry episodes showed a large‐scale pattern linked to a canonical EN event or warm TNA episodes. For instance, dry episodes linked to EN events present distinct spatial patterns of precipitation anomalies depending on EN type (Central‐Pacific vs. Eastern‐Pacific EN), and NEB region experienced a severe drought in 2012 that is not attributed to EN or warm TNA events. Even in the case of the strong EN in 2015/16, some regional impacts have not been explained by EN contribution. This paper discusses the effects of CP and EP EN events, and the role of warm TNA events on tropical Walker and Hadley circulation leading to drought over Amazonia and NEB regions.

Sensitivity of Dynamical Downscaling Seasonal Precipitation Forecasts to Convection and Land Surface Parameterization in a High-Resolution Regional Climate Model

Both convection and land surface parameterization influence seasonal precipitation forecasts. In this study, the sensitivity of dynamical downscaling seasonal precipitation forecasts to convection and land surface parameterization was investigated by nesting the Weather Research and Forecasting (WRF) model into the NCEP’s Climate Forecast System version 2 (CFSv2) retrospective forecasts with four convective schemes: Kain–Fritsch (KF), Betts–Miller–Janjic (BMJ), Grell–Freitas (GF), and new simplified Arakawa–Schubert (NSAS) schemes, and two land surface schemes: Noah and simplified Simple Biosphere (SSiB) schemes over the Han River basin. The CFSv2 model biases are reduced when the KF convective scheme is used in the wet summer season. However, negative biases still exist especially when the combination of BMJ and SSiB schemes is used. Compared with CFSv2 reforecasts and other combinations of schemes, the forecast skills of spatial patterns of precipitation anomalies are highest when the combination of KF and Noah schemes is used in summer. In contrast, the combination of BMJ and SSiB schemes shows lowest forecast skills in summer. To understand the causes of the differences in precipitation forecasts using different parameterization schemes, the simulated moisture flux convergence, thermodynamic parameters at different pressure levels, convective available potential energy (CAPE), convective inhibition (CIN), and heat fluxes are compared with the data in the ERA-5 reanalysis dataset. The WRF model-simulated moisture flux convergence is closer to that of the ERA-5 reanalysis compared with that of the CFSv2 reforecasts in summer. The vertical thermodynamic profiles also suggest that the combination of the KF and Noah schemes has caused a more unstable atmosphere, which is crucial for precipitation. In contrast, the combination of BMJ and SSiB schemes shows a less unstable atmospheric environment in summer, which explains the lower forecast skills compared with other schemes. The spatial patterns of CAPE are also improved when using the WRF model, which further enhances the precipitation forecast skills over the Han River basin.

The Influence of the Regional Hadley and Walker Circulations on Precipitation Patterns over Africa in El Niño, La Niña, and Neutral Years

This study focuses on the differential impacts of the positive (El Nino), negative (La Nina), and neutral phases of the El Nino Southern Oscillation (ENSO) on precipitation over Africa during DJF and JJA, evaluated through changes in the regional Hadley and Walker Circulations. Identification of the Hadley and Walker Cells was done using stream function mass transport calculations of ERA-Interim reanalysis data from 1979 to 2014. Analysis of the spatial pattern of precipitation anomalies shows that during DJF, El Nino (La Nina) negatively (positively) impacts precipitation over the African continent. During JJA, El Nino (La Nina) influences precipitation variability over the Sahel region, producing positive (negative) anomalies. Negative precipitation anomalies associated with El Nino (DJF) over southern Africa are linked to a strengthening in subsidence of the descending branch of the regional Hadley Cell, and during JJA the negative precipitation anomalies over the Sahel are associated with a weakening of the ascending branch of the regional Hadley Cell. During La Nina events in DJF, there is a tendency toward increased convection in southern Africa, associated with a stronger ascending branch and weaker descending branch of the regional Hadley Cell. During La Nina events in JJA, positive precipitation anomalies over the Sahel are associated with an intensification of the ascending branch of the regional Hadley Cell north of the equator.

Climatic and anthropogenic drivers of northern Amazon fires during the 2015-2016 El Niño event.

The strong El Niño Southern Oscillation (ENSO) event that occurred in 2015-2016 caused extreme drought in the northern Brazilian Amazon, especially in the state of Roraima, increasing fire occurrence. Here we map the extent of precipitation and fire anomalies and quantify the effects of climatic and anthropogenic drivers on fire occurrence during the 2015-2016 dry season (from December 2015 to March 2016) in the state of Roraima. To achieve these objectives we first estimated the spatial pattern of precipitation anomalies, based on long-term data from the TRMM (Tropical Rainfall Measuring Mission), and the fire anomaly, based on MODIS (Moderate Resolution Imaging Spectroradiometer) active fire detections during the referred period. Then, we integrated climatic and anthropogenic drivers in a Maximum Entropy (MaxEnt) model to quantify fire probability, assessing (1) the model accuracy during the 2015-2016 and the 2016-2017 dry seasons; (2) the relative importance of each predictor variable on the model predictive performance; and (3) the response curves, showing how each environmental variable affects the fire probability. Approximately 59% (132,900km2 ) of the study area was exposed to precipitation anomalies ≤-1 standard deviation (SD) in January and ~48% (~106,800km2 ) in March. About 38% (86,200km2 ) of the study area experienced fire anomalies ≥1SD in at least one month between December 2015 and March 2016. The distance to roads and the direct ENSO effect on fire occurrence were the two most influential variables on model predictive performance. Despite the improvement of governmental actions of fire prevention and firefighting in Roraima since the last intense ENSO event (1997-1998), we show that fire still gets out of control in the state during extreme drought events. Our results indicate that if no prevention actions are undertaken, future road network expansion and a climate-induced increase in water stress will amplify fire occurrence in the northern Amazon, even in its humid dense forests. As an additional outcome of our analysis, we conclude that the model and the data we used may help to guide on-the-ground fire-prevention actions and firefighting planning and therefore minimize fire-related ecosystems degradation, economic losses and carbon emissions in Roraima.

On the spatial and temporal variability of ENSO precipitation and drought teleconnection in mainland Southeast Asia

Abstract. The variability of the hydroclimate over mainland Southeast Asia is strongly influenced by the El Niño–Southern Oscillation (ENSO), which has been linked to severe droughts and floods that profoundly influence human societies and ecosystems alike. Although the significance of ENSO is well understood, there are still limitations in the understanding of its effects on hydroclimate, particularly with regard to understanding the spatio-temporal characteristics and the long-term variation of its effects. Therefore we analysed the seasonal evolution and spatial variations in the effect of ENSO on precipitation over the period of 1980–2013 and the long-term variation in the ENSO teleconnection using tree-ring-derived Palmer drought severity indices (PDSIs) for the March–May season that span over the time period 1650–2004. The analyses provided an improved understanding of the seasonal evolution of the precipitation anomalies during ENSO events. The effects of ENSO were found to be most consistent and expressed over the largest areal extents during March–May of the year when the ENSO events decay. On a longer timescale, we found that ENSO has affected the region's March–May hydroclimate over the majority (95 %) of the 355-year study period and that during half (52 %) of the time ENSO caused a significant increase in hydroclimatic variability. The majority of the extremely wet and dry March–May seasons also occurred during ENSO events. However, considerable variability in ENSO's influence was revealed: the spatial pattern of precipitation anomalies varied between individual ENSO events, and the strength of ENSO's influence was found to vary through time. Given the high variability in ENSO teleconnection that we described and the limitations of the current understanding of the effects of ENSO, we suggest that the adaptation to ENSO-related extremes in hydroclimate over mainland Southeast Asia needs to recognise uncertainty as an inherent part of adaptation, must go beyond "predict and control", and should seek adaptation opportunities widely within society.

Spatial patterns of precipitation anomalies in eastern China during centennial cold and warm periods of the past 2000 years

The spatial patterns of precipitation anomalies in eastern China during both warm and cold periods over the past 2000 years were studied. Four warm periods (AD 650-750, AD 1000-1100, AD 1190-1290, and AD 1900-2000) and five cold periods (AD 440-540, AD 780-920, AD 1390-1460, AD 1600-1700, and AD 1800-1900) were selected on a centennial timescale, referring to the synthesized temperature changes over China and Northern Hemisphere. The regional differences in precipitation were analysed and compared using a data set consisting of the grades in severity of floods and droughts as derived from Chinese historical documents. The results showed that there has been no fixed spatial pattern of precipitation anomalies during either cold or warm periods in eastern China over the past 2000 years. For the most of warm periods, there existed coherence of spatial pattern with dry condition only occurred over north of the Yangtze River, and the consistent spatial patterns were found between the periods 650-750 and 1190-1290, 650-750, and 1000-1100. For the cold periods, the precipitation showed various spatial patterns, and similarities were only presented in the two periods of 1600-1700 and 1800-1900 featured by meridional distribution. Comparing the spatial patterns between cold and warm periods, inverse pattern of 440-540 and 1900-2000 over the most study area can be detected, but similar pattern was also found between the periods 1800-1900 and 1900-2000, and 1600-1700 and 1900-2000. Our results implicated that the relationship between temperature and spatial pattern of precipitation anomaly has high complexity, which would be of significance to understanding the spatial pattern of precipitation in the future climate change study.

Influence of Arctic sea ice on European summer precipitation

The six summers from 2007 to 2012 were all wetter than average over northern Europe. Although none of these individual events are unprecedented in historical records, the sequence of six consecutive wet summers is extraordinary. Composite analysis reveals that observed wet summer months in northern Europe tend to occur when the jet stream is displaced to the south of its climatological position, whereas dry summer months tend to occur when the jet stream is located further north. Highly similar mechanisms are shown to drive simulated precipitation anomalies in an atmospheric model. The model is used to explore the influence of Arctic sea ice on European summer climate, by prescribing different sea ice conditions, but holding other forcings constant. In the simulations, Arctic sea ice loss induces a southward shift of the summer jet stream over Europe and increased northern European precipitation. The simulated precipitation response is relatively small compared to year-to-year variability, but is statistically significant and closely resembles the spatial pattern of precipitation anomalies in recent summers. The results suggest a causal link between observed sea ice anomalies, large-scale atmospheric circulation and increased summer rainfall over northern Europe. Thus, diminished Arctic sea ice may have been a contributing driver of recent wet summers.

Spatial patterns of precipitation anomalies for 30-yr warm periods in China during the past 2000 years

The spatial patterns of precipitation anomalies during five 30-yr warm periods of 691–720, 1231–1260, 1741–1770, 1921–1950, and 1981–2000 were investigated using a dryness/wetness grading dataset covering 48 stations from Chinese historical documents and 22 precipitation proxy series from natural archives. It was found that the North China Plain (approximately 35°–40°N, east of 105°E) was dry in four warm periods within the centennial warm epochs of 600–750, the Medieval Warm Period (about 900–1300) and after 1900. A wet condition prevailed over most of China during 1741–1770, a 30-yr warm peak that occurred during the Little Ice Age (about 1650–1850). The spatial pattern of the precipitation anomaly in 1981–2000 over East China (25°-40°N, east of 105°E) is roughly consistent with that in 1231–1260, but a difference in the precipitation anomaly appeared over the Tibetan Plateau. The spatial patterns of the precipitation anomalies over China varied between all five 30-yr warm periods, which implies that the matching pattern between temperature and precipitation change is multiform, and the precipitation anomaly could be positive or negative when a decadal warm climate occurs in different climate epochs. This result may provide a primary reference for the mechanism detection and climate simulation of the precipitation anomaly of the future warm climate.

Precipitation division and climate shift in China from 1960 to 2000

Precipitation division and climate shift in China were analysed using daily precipitation data from 486 stations for the period 1960–2000. A modified hierarchical clustering method was used to divide China into sub-regions that have a coherent annual cycle, interannual evolutions and long-term trends. A division with 20 grouping areas can well describe the anomalous regional features seasonally, and a division with 40 grouping areas can reach a relatively stable level, theoretically for the annual and summer precipitation in mainland China. Rapid changes or interdecadal transition points of the regional precipitation series are detected by a multiple timescale t-test method. For mean annual precipitation, the interdecadal shift from a low to a high level is found to occur around 1987 in the Xinjiang region and around 1983 in Northeast China. The transition points of increasing summer rainfall in the eastern Tibetan Plateau and the lower Yangtze River are centred closely about 1979. Changes in the double-decadal precipitation are also detected for the two periods of 1961–1980 and 1981–2000. An increasing trend in annual precipitation is mainly found in the lower Yangtze River valley, the Xinjiang region, and Northeast China. Decreasing annual precipitation is mainly situated from the middle Yangtze River to the Yellow River. In summer and winter, the spatial pattern of precipitation anomalies shows two pairs of contrasting shifts with negative departures in North China and southern coastal China, while positive departures are found in the middle and lower Yangtze River valley and Northeast China. This pattern of regional precipitation shift in China is associated with southerly monsoon flow in eastern China and westerly flow in northwestern China.

Effects of El Niño–Southern Oscillation on the Climate, Water Balance, and Streamflow of the Mississippi River Basin

Abstract Climatic and hydrologic observations and results from a terrestrial ecosystem model coupled to a regional-scale river-routing algorithm are used to document the associations between the El Niño–Southern Oscillation (ENSO) phenomenon and anomalies in climate, surface water balance, and river hydrology within the Mississippi River basin. While no ENSO signal is found in streamflow at the outlet of the basin in Vicksburg, Mississippi, significant anomalies in all water balance components are found in certain regions within the basin. ENSO is mainly associated with positive winter temperature anomalies, but hydrologic patterns vary with season, location, and ENSO phase. El Niño precipitation anomalies tend to affect evapotranspiration (ET) in the western half of the basin and runoff in the eastern half. La Niña events are associated with ET anomalies in the central portion of the basin and runoff anomalies in the southern and eastern portions of the basin. Both ENSO phases are associated with decreased snow depth. Anomalous soil moisture patterns occur at seasonal time scales and filter noisier spatial patterns of precipitation anomalies into coherent patterns with larger field significance; however, for all water budget components, there is a large amount of variability in response within a particular ENSO phase. With anomalies that are up to 4 times those of a typical event, it is clear that improved predictability of the onset and strength of an upcoming ENSO event is important for both water resource management and disaster mitigation.

The influence of the tropical and subtropical Atlantic and Pacific Oceans on precipitation variability over Southern Central South America on seasonal time scales

AbstractThis paper studies the temporal and spatial patterns of precipitation anomalies over southern central South America (SCSA; 22–40°S and 54–70°W), and their relationship with the sea‐surface temperature (SST) variability over the surrounding tropical and subtropical Atlantic and Pacific Oceans. The data include monthly precipitation from 68 weather stations in central–northern Argentina and neighbouring Brazil, Paraguay and Uruguay, and monthly SSTs from the NOAA dataset with a 2° resolution for the period 1961–93. We use the method of canonical correlation analysis (CCA) to study the simultaneous relationship between bi‐monthly precipitation and SST variability. Before applying the CCA procedure, standardized anomalies are calculated and a prefiltering is applied by means of an empirical orthogonal function (EOF) analysis. Thus, the CCA input consists of 10 EOF modes of SST and between 9 and 11 modes for precipitation and their corresponding principal components, which are the minimum number of modes necessary to explain at least 80% of the variance of the corresponding field. The results show that November–December presents the most robust association between the SST and SCSA precipitation variability, especially in northeastern Argentina and southern Brazil, followed by March–April and May–June. The period January–February, in contrast, displays a weak relationship with the oceans and represents a temporal minimum of oceanic influence during the summer semester. Based on the CCA maps, we identify the different oceanic and SCSA regions, the regional averages of SST and precipitation are calculated, and linear correlation analysis are conducted. The periods with greater association between the oceans and SCSA precipitation are November–December and May–June. During November–December, every selected region over SCSA reflects the influence of several oceanic regions, whereas during May–June only a few regions show a direct association with the oceans. The Pacific Ocean regions have a greater influence and are more widespread over SCSA; the Atlantic Ocean regions have an influence only over the northwestern and the southeastern parts of SCSA. In general, the relationship with the equatorial and tropical Atlantic and Pacific Oceans is of the type warm–wet/cold–dry, whereas the subtropical regions of both oceans show the opposite relationship, i.e. warm–dry/cold–wet. Copyright © 2004 Royal Meteorological Society