To expand the spatial coverage of the conventional Basin Long Short‐Term Memory (LSTM) model for river discharge estimation beyond pre‐selected individual locations, we developed a discharge modeling scheme, Grid LSTM‐RAPID, to estimate discharge for every river reach worldwide. Grid LSTM‐RAPID applies LSTM runoff estimation to the grids (0.25°), small rectangular hydrological response units (HRUs) rather than basins (irregularly shaped HRUs of any size), and then routes the grid runoff over all reaches on a global river network using the RAPID routing model. It largely maintains the strong performance of Basin LSTM over gauged basins and achieves a median Kling‐Gupta Efficiency (KGE) of 0.653 for small basins out‐of‐sample both temporally and spatially (0.688 for out‐of‐sample temporally), and a median KGE of 0.592 for other basins with larger areas and less data quality. Compared to Basin LSTM, Grid LSTM‐RAPID loses about 0.03 in median KGE for basins out‐of‐sample in both time and space in exchange for global all‐reach coverage without heavy cost. Despite this tradeoff, it significantly outperforms a well‐calibrated process‐based benchmark model. Using the new scheme, we create an improved global reach‐level daily discharge data set from 1980 to near present named GRADES‐hydroDL, which is openly shared at https://www.reachhydro.org/home/records/grades‐hydrodl.

This study investigates the historical climatology and future projected change of atmospheric rivers (ARs) and precipitation for the Middle East and North Africa (MENA) region. We use a suite of models from the Coupled Model Intercomparison Project Phase 5 (CMIP5, historical and RCP8.5 scenarios) and other observations to estimate AR frequency and mean daily precipitation. Despite its arid-to-semi-arid climate, parts of the MENA region experience strong ARs, which contribute a large fraction of the annual precipitation, such as in the mountainous areas of Turkey and Iran. This study shows that by the end of this century, AR frequency is projected to increase (~20–40%) for the North Africa and Mediterranean areas (including any region with higher latitudes than 35 N). However, for these regions, mean daily precipitation (i.e., regardless of the presence of ARs) is projected to decrease (~15–30%). For the rest of the MENA region, including the Arabian Peninsula and the Horn of Africa, minor changes in AR frequency (±10%) are expected, yet mean precipitation is projected to increase (~50%) for these regions. Overall, the projected sign of change in AR frequency is opposite to the projected sign of change in mean daily precipitation for most areas within the MENA region.

The Atlantic and Pacific basin are found linked in the context of multidecadal SST variability from analyses of 118 years of observational data. Recurrent spatiotemporal variability, including multidecadal modes, was identified using the extended-EOF technique in a longitudinally global domain, allowing unfettered expression of interbasin interactions. The physicality of the obtained decadal modes was assessed using fishery records and analog counts.A three-mode structure with bi-directional interbasin links frames the new perspective on the cycling of multidecadal SST variability. The three modes are the Atlantic multidecadal oscillation (AMO), low-frequency North Atlantic Oscillation (LF-NAO), and North Pacific decadal variability [PDV-NP; resembling negative (–ve) PDO]. The two previously documented links AMO→PDV-NP (with ~12.5-yr lead) and LF-NAO→AMO (with 16-yr lead) are corroborated, while a third one, PDV-NP→(−LF-NAO) with ~6.5-yr lead, is uncovered. The interaction triad closes the loop on the cycling of multidecadal SST variability, generating AMO’s phase reversal in ~35 years, consistent with its widely noted ~70-yr time scale. The two previously noted links—one intrabasin and one interbasin—were unsuccessful in this regard.Other findings include the deeper subsurface extensions of Atlantic multidecadal SST variability, and the hitherto unrecognized similarity of Pan-Pacific decadal variability and North Pacific Gyre Oscillation. Instrumental records, albeit short in the context of multidecadal variability, must continue to be mined for insights into the functioning of the climate system as its model representations while improving, remain inadequate.

The northeast monsoon (NEM) brings the bulk of annual rainfall to southeastern peninsular India, Sri Lanka, and the neighboring Southeast Asian countries. This October–December monsoon is referred to as the winter monsoon in this region. In contrast, the southwest summer monsoon brings bountiful rainfall to the Indo-Gangetic Plain. The winter monsoon region is objectively demarcated from analysis of the timing of peak monthly rainfall. Because of the region’s complex terrain, in situ precipitation datasets are assessed using high-spatiotemporal-resolution Tropical Rainfall Measuring Mission (TRMM) rainfall estimates, prior to their use in monsoon evolution, variability, and trend analyses. The Global Precipitation Climatology Center’s in situ analysis showed the least bias from TRMM.El Niño–Southern Oscillation’s (ENSO) impact on NEM rainfall is shown to be significant, leading to stronger NEM rainfall over southeastern peninsular India and Sri Lanka but diminished rainfall over Thailand, Vietnam, and the Philippines. The impact varies subseasonally, being weak in October and strong in November. The positive anomalies over peninsular India are generated by anomalous anticyclonic flow centered over the Bay of Bengal, which is forced by an El Niño–related reduction in deep convection over the Maritime Continent.The historical twentieth-century climate simulations informing the Intergovernmental Panel on Climate Change’s Fifth Assessment (IPCC-AR5) show varied deficiencies in the NEM rainfall distribution and a markedly weaker (and often unrealistic) ENSO–NEM rainfall relationship.

Recently, assessments of global climate model (GCM) ensembles have transitioned from using unweighted means to weighted means designed to account for skill and interdependence among models. Although ensemble-weighting schemes are typically derived using a GCM ensemble, statistically downscaled projections are used in climate change assessments. This study applies four ensemble-weighting schemes for model averaging to precipitation projections in the south-central United States. The weighting schemes are applied to (1) a 26-member GCM ensemble and (2) those 26 members downscaled using Localized Canonical Analogs (LOCA). This study is distinct from prior research because it compares the interactions of ensemble-weighting schemes with GCMs and statistical downscaling to produce summarized climate projection products. The analysis indicates that statistical downscaling improves the ensemble accuracy (LOCA average root mean square error is 100 mm less than the CMIP5 average root mean square error) and reduces the uncertainty of the projected ensemble-mean change. Furthermore, averaging the LOCA ensemble using Bayesian Model Averaging reduces the uncertainty beyond any other combination of weighting schemes and ensemble (standard deviation of the mean projected change in the domain is reduced by 40–50 mm). The results also indicate that it is inappropriate to assume that a weighting scheme derived from a GCM ensemble matches the same weights derived using a downscaled ensemble.

"Advances in Sub-seasonal to Seasonal Prediction Relevant to Water Management in the Western United States" published on 21 Jul 2022 by American Meteorological Society.