Precipitation Climatology Research Articles

Introducing Flashiness‐Intensity‐Duration‐Frequency (F‐IDF): A New Metric to Quantify Flash Flood Intensity

AbstractFlash flooding is a damaging weather event, yet it remains challenging to quantify its severity. We propose a development—the Flashiness‐Intensity‐Duration‐Frequency (F‐IDF) curve—to quantify flash flood intensity based on the frequency and duration of the event. As a proof‐of‐concept, we mapped Contiguous US (CONUS)‐wide F‐IDF values at 3,722 stream gage locations and explored their relations with basin attributes. It is found that (a) The return periods of flash flood events are highly associated with the return peroids of rainfall events; (b) Climatological precipitation amounts exhibit the most positive correlation with flashiness while a basin's drainage area is the most negatively correlated; (c) Correlation of flashiness with basin attributes decreases with increasing F‐IDF return periods and shorter event durations. Both aspects are attributable to the rainfall signal overwhelming the underlying basin attributes as the intensities become more extreme. This new metric has implications for hydrology and emergency responders.

Observational Uncertainty for Global Drought‐Pluvial Volatility

AbstractDroughts and pluvials have occurred in most regions in the past. However, what calls growing attention is the additive effects of these two opposite extreme events occurring in spatial‐temporal proximity to one another, sometimes beyond either of the hazards individually. This study examines the likelihood of global drought‐pluvial volatility which involves both meteorological drought‐to‐pluvial (DTP) and pluvial‐to‐meteorological drought transitions; meanwhile, identifies discrepancies and agreements among the widely used observations for such events, an aspect that remains currently overlooked. Globally, we find that the observation‐based data sets including Global Precipitation Climatology Center (GPCC), Climate Research Unit (CRU) and ERA5 reach a good agreement in estimating the event transition rates, with an average 15.46% (15.49%) of all meteorological droughts (pluvials) being succeeded by pluvials (meteorological droughts) in the following season. At the regional scale, our results reveal that the spatial variability and frequency associated with meteorological DTP transitions are slightly larger than that with pluvial‐to‐ meteorological drought transitions, but the observational uncertainty is more pronounced in the latter case as a result of greater uncertainty in the univariate drought depiction and enhanced regional divergence among the observed data. In general, GPCC and CRU exhibit higher consistency, albeit with less agreement under pluvial‐to‐ meteorological drought transitions, while ERA5 yields underestimations and reduced spatial variability considering both transition scenarios. The study highlights a need of using multiple independent observation‐based data sets for compound/multivariate extreme analysis, particularly in the context of climate‐related decision‐making, water resources planning, and future model validation studies.

The Precipitation Characteristics of Mesoscale Convective Systems Over Europe

AbstractMesoscale convective systems (MCSs) are common over Europe and can produce severe weather, including extreme precipitation that leads to flash floods. The few studies analyzing the climatological characteristics of MCSs over Europe are either focusing on only few years of data or on limited subareas. Using the recent Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) satellite precipitation climatology, we identify and track MCSs for 16 years over Europe. The tracking algorithm relies on the overlap of precipitation features between consecutive time steps and, unlike previous studies, uses lightning data to distinguish convective from stratiform rain patches, which can reduce potential identification errors. We analyze this new European MCS climatology to characterize MCS precipitation properties and conclude the following results: MCSs overall occur most frequently over the Mediterranean and Atlantic during fall and winter, whereas during summer, they concentrate over the continent. Typically, more than a third of seasonal precipitation can be attributed to MCSs, and their contribution to extreme precipitation is even greater, often exceeding 60%. MCSs over the continent display a clear diurnal cycle peak during the afternoon, and some continental areas also show a second, nocturnal peak. The MCS diurnal cycle for coastal and oceanic regions is more variable. We find that the spatiotemporal distribution of MCS precipitation can be attributed to specific environmental variables, namely (sea) surface temperature, fronts occurrence and convective instability. While inland MCS precipitation is mostly constrained by thermodynamics, for the coastal MCSs the atmospheric dynamics plays an important role as well.

Climate Atlas of Bosnia and Herzegovina

The chapter presents the Climate Atlas of Bosnia and Herzegovina with 180 thematic maps. The digital interactive Climate Atlas was created for the first time for the needs of the Third National Communication of Bosnia and Herzegovina and was published in 2016. The publisher was the Faculty of Natural Sciences and Mathematics of the University of Banja Luka, and the climatological maps are displayed in the form of a WEB interactive climate atlas. The interactive climate atlas can be accessed via the link: http://www.unfccc.ba/klimatski_atlas/. By decision of the Teaching and Scientific Council of the Faculty of Natural Sciences and Mathematics of the University of Banja Luka number: 19/3.1370/16 from May 18, 2016 the Climate Atlas of Bosnia and Herzegovina by authors Davorin Bajić and Goran Trbić was approved to be published as a scientific publication. In this monograph, the Climate Atlas is published for the first time in a printed edition. The Climatic Atlas of Bosnia and Herzegovina consists of sets of digital climatological maps related to two climatological parameters, air temperature and precipitation, displayed by month, season, growing sesaon and year. Climate maps were made for three climatological periods, namely: the observed period 1961‒1990, and the periods 2001‒2030 and 2071‒2100, which refer to scenarios A1B and A2. Climate maps for the period 1961‒1990 were made based on the interpolation of climate parameters from 45 meteorological stations in Bosnia and Herzegovina.

Evaluation of global precipitation products for meteorological drought assessment with respect to IMD station datasets over India

In-situ precipitation data is the most reliable data for the drought assessment. However, gridded datasets are commonly used due to limited availability of station data. However, the gridded data may exhibit bias compared to station data. The present study has been undertaken to examine the performance of gridded datasets from global precipitation products (gauge-interpolated, merged from various sources, re-analysis and satellite products) for the assessment of meteorological drought with reference to station datasets of the India Meteorological Department (IMD) from 2000 to 2019 at six drought zones over India. Various statistical metrics were used to check the performance of the precipitation products at four seasons of India. The Standardised Precipitation Index (SPI) was used at a 1-month timescale to evaluate the meteorological drought characteristics. The gridded precipitation products, in regions of high rainfall, tended to underestimate precipitation during the monsoon season and overestimate it during the non-monsoon season. When precipitation was analysed spatially using statistical parameters, the product Integrated Multi-Satellite Retrievals (IMERG Final-Run) outperformed all the other precipitation products in the four seasons and six drought zones over India. For drought characteristics evaluation, the products Climate Prediction Center (CPC) Morphing Technique (CMORPH) and Global Precipitation Climatology Centre (GPCC) outperformed all the other products in the six drought zones over India. Further, in order to rank the precipitation products, the Spearman rank correlation coefficient (SCC) and critical success index (CSI) were used. The analysis results showed that IMERG Final-Run, GPCC and Multi-Source Weighted-Ensemble Precipitation (MSWEP-PastNoGauge) ranked best out of all the precipitation products when analysed for drought monitoring and assessment at six drought zones over India. Overall, IMERG Final-Run outperformed all the other precipitation products for drought monitoring, while TerraClimate performed poorly in most of the drought zones and in India as a whole. The study's findings will help choose suitable gridded precipitation dataset for meteorological drought assessment across six homogeneous drought zones and in India as a whole, aiding operational drought risk management and early warnings.

Drought variability, changes and hot spots across the African continent during the historical period (1928–2017)

AbstractThe spatiotemporal variability of meteorological droughts, its changes and hot spots location across Africa are analysed for the period spanning 1928–2017 using the Standardized Precipitation Index (SPI) applied to the precipitation products from the Climatic Research Unit (CRU), University of Delaware (UDEL) and Global Precipitation Climatology Centre (GPCC). Spatially, an analysis based on rotational empirical orthogonal function identifies five regions of similar drought variability, namely the Sahel, East Africa, East Southern Africa, West Southern Africa and the Gulf of Guinea. Temporally, the most common periods of drought occurrence are the 1970s, the 1980s and, to a lesser extent, the 1990s. Changes in drought characteristics for the intermediate past (1958–1987) and recent past (1988–2017) compared to the far past (1928–1957) indicate robust increases of drought duration, frequency and severity in the Sahel, and to a lower extent in the Gulf of Guinea, some areas of Central Africa, part of Southern Africa and over Madagascar. These changes are stronger (weaker) along the Sahel during the intermediate past (recent past) and stronger (weaker) over Central and Southern Africa and Madagascar during the recent past (intermediate past). As a consequence, drought hot spots, mostly driven by severity during the regions' wet season, are identified in areas confined in the Sahel during the intermediate past and in regions mainly over Central and Southern Africa and Madagascar during the recent past. Our results are useful for drought disaster risk management across Africa and provide a valuable reference for future drought analysis under global warming conditions.

The New Version 3.2 Global Precipitation Climatology Project (GPCP) Monthly and Daily Precipitation Products

Abstract The Global Precipitation Climatology Project (GPCP) Version 3.2 Precipitation Analysis provides globally complete analyses of surface precipitation on a 0.5° × 0.5° latitude–longitude grid at both monthly and daily time scales, covering from 1983 to the present and from June 2000 to the present, respectively. These merged products continue the GPCP heritage of incorporating precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, sounder-based estimates, and surface rain gauge observations emphasizing the strengths of various inputs and striving for time and space homogeneity. Furthermore, these analyses incorporate modern algorithms, refined intercalibrations among sensors, climatologies of recent high-quality satellite precipitation data, and fine-scale multisatellite estimates. New data fields have been introduced to better characterize the precipitation, including the fraction of the precipitation that is liquid (rain) in both the monthly and daily products, and a quality index for the monthly product. Compared to the operational GPCP Version 2.3 Monthly, the Version 3.2 Monthly product provides a more reasonable climatology in the Southern Ocean and increases the estimated global average precipitation by about 4.5%, which is similar to estimates from recent global water budget assessments. Global and regional trends for 1983–2020 with this new Monthly dataset are very similar to those computed from Version 2.3. Compared to the operational One-Degree Daily (Version 1.3) product, the new Version 3.2 Daily is designed to better represent the histogram of precipitation rates, particularly at high values and shifts the start of less-certain high-latitude estimates from 40° to 58° latitude in each hemisphere. Significance Statement Studies of Earth’s climate require long-term global datasets based on observations to show how the climate functions and to validate numerical climate models. This study describes an important upgrade to the monthly and daily precipitation (rain and snow) products computed by the Global Precipitation Climatology Project. We use modern analysis schemes, add new sources of data, and deliver results on a finer-scale 0.5° × 0.5° latitude–longitude grid [roughly 55 km (34 mi) on a side at the equator]. The new data show improved agreement with other studies and depict more reasonable behavior in the Southern Ocean. The daily product shows improved estimates of how often different intensities of precipitation occur around the world, particularly the high amounts that drive floods and landslides.

Seasonal Modulation of the Madden–Julian Oscillation’s Impact on Rainfall in Sri Lanka

Abstract For the tropical country of Sri Lanka, subseasonal variability in precipitation is both ecologically and societally relevant, influencing agricultural yields, natural hazard risk, energy production, and disease incidence. The primary driver of this subseasonal precipitation variability is the Madden–Julian oscillation (MJO). Here we investigate this influence on Sri Lankan precipitation across seasons, describing MJO-associated precipitation patterns and exploring the potential for MJO-informed subseasonal forecasts. We do so using 40-yr satellite-derived records of precipitation with high spatial resolution (from CHIRPS v2.0) and related meteorological and atmospheric fields (from ERA5 and MERRA-2). We find a direct MJO influence on precipitation corresponding to propagation of the MJO’s convectively active region and suppressed region near Sri Lanka, with the strength and spatial patterns of this influence differing across seasons. There are particularly strong impacts in the second intermonsoon (SIM; October–November) and southwest monsoon (SWM; May–September) seasons. During SIM the impacts are island-wide, but strongest in the northeast. During the SWM the absolute impacts are localized to the southwest, but the relative impacts (i.e., relative to precipitation climatology) are fairly uniform across the island. Moreover, we find significant associations between MJO phase and Sri Lankan precipitation at time scales of up to several weeks. Notably, these associations are stronger when using the OLR-based MJO index (OMI) rather than the more commonly used real-time multivariate MJO index (RMM). While the MJO associations we describe here arise from a highly simplified forecasting scheme, they provide a foundation and impetus for developing a more complete, MJO-informed precipitation forecast method. Significance Statement Rainfall variability at the subseasonal (weeks–months) time scale is critical to societal well-being, given its fundamental importance for agriculture, flood risk, hydropower generation, and disease incidence. Our work describes how such rainfall variability in Sri Lanka is impacted by the Madden–Julian oscillation, in which a region of enhanced rainfall and cloudiness, paired with a region of decreased rainfall and cloudiness, circles the globe every 30–60 days. Our results suggest that its influence on Sri Lankan rainfall may be strong enough that incorporating knowledge of the Madden–Julian oscillation into forecasts can improve the accuracy of rainfall prediction for Sri Lanka. Future work should develop a more comprehensive forecast method to assess viability in real-world forecasting scenarios.

Extreme Drought Assessment in Sumatra-Indonesia Using SPI and EDI

Even though Sumatra is very vulnerable to the effects of drought, research identifying drought on this island is very limited, especially extreme drought. This research was conducted to identify extreme drought in Sumatra using the Standardized Precipitation Index (SPI) and the Effective Drought Index (EDI). This study uses precipitation data from the Indonesian Meteorology, Climatology and Geophysics Agency (BMKG) and Global Precipitation Climatology Center (GPCC) reanalysis data. The composite indices were conducted to discover some phenomena that cause the drought based on El Niño and positive Indian Ocean Dipole (IOD) events. The results showed that the El Niño and positive IOD phenomena were more likely to influence extreme droughts. However, the droughts in 2014 and 2008 tended to be influenced by the negative Sea Surface Temperature anomaly (SSTA). The spatial analysis results show that the areas that experience extreme drought more often are the west coast of Sumatra (except Aceh province), especially based on SPI12 and EDI. The composite indices results show that the drought that occurred in Aceh province was more influenced by the El Niño phenomenon in the December-January-February (DJF) period and the positive IOD in the June-July-August (JJA) – September-October-November (SON) period. In addition, Aceh province is an area that is not affected by El Niño-positive IOD, especially during the SON-DJF period. These results can be used to mitigate drought, especially when El Niño-positive IOD phenomena cause it.

IMERG Precipitation Improves the SMAP Level-4 Soil Moisture Product

Abstract The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6. Significance Statement Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia.

Assessment of NEX-GDDP-CMIP6 Downscale Data in Simulating Extreme Precipitation over the Huai River Basin

This study aimed to assess the performance of 35 global climate models included in NEX-GDDP-CMIP6, derived from downscaling CMIP6 data to high spatial (25 km) and temporal (daily) resolutions, in reproducing extreme precipitation events over the Huai River Basin. Eight widely used extreme precipitation indices were employed to quantitatively describe the models’ capability of simulation. Results indicate that the majority of models can reasonably capture trends, with UKESM1-0-LL performing the best among all considered models. All models demonstrate high accuracy in simulating climatological means, especially for the total precipitation (PRCPTOT), displaying a spatial correlation coefficient exceeding 0.8 when compared to the observed data. NorESM2-MM and MRI-ESM2-0 can accurately simulate the frequency and intensity of extreme precipitation, respectively. In general, UKESM1-0-LL, CESM2, MIROC6, MRI-ESM2-0, CMCC-CM2-SR5, and MPI-ESM-2-LR exhibit superior simulation capabilities in terms of capturing both the trends and climatology of extreme precipitation. The aforementioned findings provide guidance for future studies on the regional impacts of climate change using NEX model data, and therefore hold great importance in comprehending the regional impacts of, and the adaptability to, climate change, as well as the development of adaptation strategies.

Evaluation of the Performance of CMIP6 Climate Models in Simulating Rainfall over the Philippines

The Philippines is highly vulnerable to multiple climate-related hazards due to its geographical location and weak adaptation measures. Floods are the most catastrophic hazards that impact lives, livelihoods, and, consequently, the economy at large. Understanding the ability of the general circulation models to simulate the observed rainfall using the latest state-of-the-art model is essential for reliable forecasting. Based on this background, this paper objectively aims at assessing and ranking the capabilities of the recent Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating the observed rainfall over the Philippines. The Global Precipitation Climatology Project (GPCP) v2.3 was used as a proxy to gauge the performance of 11 CMIP6 models in simulating the annual and rainy-season rainfall during 1980–2014. Several statistical metrics (mean, standard deviation, normalized root means square error, percentage bias, Pearson correlation coefficient, Mann–Kendall test, Theil–Sen slope estimator, and skill score) and geospatial measures were assessed. The results show that that CMIP6 historical simulations exhibit satisfactory effectiveness in simulating the annual cycle, though some models display wet/dry biases. The CMIP6 models generally underestimate rainfall on the land but overestimate it over the ocean. The trend analysis shows that rainfall over the country is insignificantly increasing both annually and during the rainy seasons. Notably, most of the models could correctly simulate the trend sign but over/underestimate the magnitude. The CMIP6 historical rainfall simulating models significantly agree on simulating the mean annual cycle but diverge in temporal ability simulation. The performance of the models remarkably differs from one metric to another and among different time scales. Nevertheless, the models may be ranked from the best to the least best at simulating the Philippines’ rainfall in the order GFDL, NOR, ACCESS, ENS, MRI, CMCC, NESM, FIO, MIROC, CESM, TAI, and CAN. The findings of this study form a good basis for the selection of models to be used in robust future climate projection and impact studies regarding the Philippines. The climate model developers may use the documented shortcoming of these models and improve their physical parametrization for better performance in the future.

Statistical Evaluation of the Performance of Gridded Daily Precipitation Products from Reanalysis Data, Satellite Estimates, and Merged Analyses over Global Land

The Beijing Climate Center of the China Meteorological Administration (BCC/CMA) has developed a gauge-satellite-model merged gridded daily precipitation dataset with complete global coverage, called BCC Merged Estimation of Precipitation (BMEP). Using the unified rain gauge dataset from the CPC (CPC-U) as the independent benchmark, BMEP and the four most widely used global daily precipitation products, including the Global Precipitation Climatology Project one-degree daily (GPCP-1DD), the NCEP Climate Forecast System Reanalysis (CFSR), the Interim ECMWF Re-analysis (ERA-interim), and the 55 year Japanese Reanalysis Project (JRA-55), are evaluated over the global land area from January 2003 to December 2016. The results show that all gridded datasets capture the overall spatiotemporal variation of global daily precipitation. All gridded datasets can basically capture the overall spatiotemporal variation of global daily precipitation. However, CFSR data tend to overestimate precipitation intensity and exhibit a spurious positive trend after 2010, attributed to the transition from CFSR to NCEP’s Climate Forecast System Version 2 (CFSv2). On the other hand, JRA-55 and ERA-interim data demonstrate higher skill in characterizing spatial and temporal variations, bias, correlation, and RMSE. GPCP-1DD data perform well in terms of bias but show limitations in detecting the interannual variability and RMSE of daily precipitation. Among these evaluated products, BMEP data exhibit the best agreement with CPC-U data in terms of the spatiotemporal variation, pattern, magnitude of variability, and occurrence of rainfall events across different thresholds. These findings indicate that BMEP gridded precipitation data effectively capture the actual characteristics of daily precipitation over global land areas.

Increasing multiscale variability in extreme precipitation under global warming in the Tienshan Mountains, Central Asia

Changes in precipitation variability not only have a significant impact on water cycle processes, but also pose an additional challenge to society's climate resilience. Extreme precipitation is more severe, more abrupt, and more sensitive to temperature changes than mean-state precipitation. However, our understanding of the features of extreme precipitation variability over a broad range of temporal scales, as well as the differences between wet and dry seasons, is limited. In this study, we perform filtering on a daily (2–5 days) to an interannual (2–8 years) scale, using daily precipitation data from detrended APHRODITE and bias-corrected CMIP6 based on zero-phase Butterworth filters. We then analyze the changes in climatological extreme precipitation variability at different time scales, looking at the features along geographical gradients and in wet and dry seasons. At various warming levels, extreme precipitation variability is projected. The findings reveal that the longer the time scale, the higher the overall variability of both extreme and mean-state precipitation, with extreme precipitation showing greater variability. From daily to interannual scales, the variability of R95p increases from 142.79 mm to 875.05 mm, representing a 6.13-fold increase in volatility, but the variability in mean precipitation increases just 1.43-fold, from 2.27 mm to 3.24 mm. The variability in the eastern Tienshan Mountains (80°E ∼ 95°E) is greater than that in the western Tienshan Mountains (66°E ∼ 80°E). Furthermore, along longitudinal and latitudinal gradients, extreme precipitation variability exhibits considerable time-scale differences, with the more extreme the event, the greater the variability. R10mm variability increases from 15.28 d to 76.74 d on a daily to interannual scale. The variability increase (61.46 d) is roughly twice that for R5mm. Seasonally, the wet season is more variable than the dry. The total variability of R10mm was 4.57 days in the wet season and 1.78 days in the dry season. Compared with the reference period (1976–2005), there is an overall increase in extreme precipitation variability at different warming levels, along with an increasing sensitivity to temperature. With the exception of consecutive dry days, the degree of response of filtered extreme precipitation variability to temperature (absolute value of response rate) shows obvious increases with time scale and accounts for a greater proportion of the total variability response rate.

Implications for Validation of IMERG Satellite Precipitation in a Complex Mountainous Region

Satellite-based precipitation retrievals such as the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG), provide alternative data in mountainous regions. In this study, we evaluated IMERG in the Yarlung Tsangbo Grand Canyon (YGC) using ground observations. It was found that IMERG underestimated the total rainfall primarily due to under-detection of rainfall events, with misses being more prevalent than false alarms. We analyzed the relationships between the probability of detection (POD), false alarm ratio (FAR), bias in detection (BID), and Heidke skill score (HSS) and terrain factors. It was found that the POD decreased with elevation, leading to increased underestimation of rainfall events at higher elevations, and the FAR was higher in valley sites. In terms of the hit events, IMERG overestimated the light rainfall events and underestimated the heavy rainfall events and the negative bias in the hit events decreased with elevation. IMERG could capture the early morning peak precipitation in the YGC region but underestimated the amplitude of the diurnal variation. This bias was inherent at the sensor level, and the Global Precipitation Climatology Center (GPCC) calibration partially improved the underestimation. However, this improvement was not sufficient for the YGC region. This study fills the gap in IMERG validation in a complex mountainous region and has implications for users and developers.

Climatology of Tropical Cyclone Precipitation in the S2S Models

Abstract This study evaluates the representation of tropical cyclone precipitation (TCP) in reforecasts from the Subseasonal to Seasonal (S2S) Prediction Project. The global distribution of precipitation in S2S models shows relevant biases in the multimodel mean ensemble that are characterized by wet biases in total precipitation and TCP, except for the Atlantic. The TCP biases can contribute more than 50% of the total precipitation biases in basins such as the southern Indian Ocean and South Pacific. The magnitude and spatial pattern of these biases exhibit little variation with lead time. The origins of TCP biases can be attributed to biases in the frequency of tropical cyclone occurrence. The S2S models simulate too few TCs in the Atlantic and western North Pacific and too many TCs in the Southern Hemisphere and eastern North Pacific. At the storm scale, the average peak precipitation near the storm center is lower in the models than observations due to a too high proportion of weak TCs. However, this bias is offset in some models by higher than observed precipitation rates at larger radii (300–500 km). An analysis of the mean TCP for each TC at each grid point reveals an overestimation of TCP rates, particularly in the near-equatorial Indian and western Pacific Oceans. These findings suggest that the simulation of TC occurrence and the storm-scale precipitation require better representation in order to reduce TCP biases and enhance the subseasonal prediction skill of mean and extreme total precipitation.

Behavior of IMERG precipitation extremes with temperature at different spatial resolution

The Clausius-Clapeyron equation determines how saturation vapor pressure increases with temperature, which is important in determining variations in extreme precipitation. Regionally, the scaling of C–C does not vary significantly, but the relationship with extreme precipitation does. In this study, the precipitation from Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) is tested for its accuracy in scaling extreme precipitation rates with temperature (termed scaling factor). We utilized the IMERG precipitation data across the Indian Sub-continent at 0.1° × 0.1°, 0.25° × 0.25° and 0.5° × 0.5° spatial resolution from 2001 to 2020 datasets. Our findings show that, there is a transition in Global Precipitation Measurement’s precipitation extremes estimations (95th percentile) around 30 °C over spatial resolution of 0.25° and 0.1° from C–C to sub C–C. This study also evaluates the sensitivity of C–C scaling in different regimes of India having homogeneous precipitation climatology. It is found that southeast India is highly sensitive to the spatial resolution, as it shows steep slopes in extreme precipitation rates at high dew point temperatures. This is the first study to evaluate the sensitivity of spatial resolution on C–C analysis as most of the previous studies have considered temporal variations.

Simulations of the North Tropical Atlantic Mode–ENSO Connection in CMIP5 and CMIP6 Models

AbstractThe North Tropical Atlantic (NTA) mode is a dominant component of sea surface temperature (SST) variability over the northern tropical Atlantic, which is crucial to the occurrence of El Niño‐Southern Oscillation (ENSO). This study utilizes the historical simulation of the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) to evaluate the simulations of the NTA mode and NTA–ENSO connection. We found that the CMIP5 and CMIP6 could reproduce the spatial pattern of the NTA mode similarly. Most of them could reasonably reproduce the spatial pattern of the NTA mode similar to the observation. Still, there are some differences in the central values and position of the NTA mode. In addition, the analysis further suggests that the CMIP5/CMIP6 models have a large diversity in the NTA–ENSO connection, which may be due to their differences in simulating the spring climatological mean Atlantic Intertropical Convergence Zone (ITCZ) precipitation and spring‐to‐summer NTA SST persistence. Models with stronger spring climatological mean Atlantic ITCZ precipitation in favor of the spring NTA‐related SST anomalies stimulate local precipitation anomalies, resulting in a more powerful atmospheric circulation reaction over the tropical Pacific. Additionally, the stronger spring‐to‐summer NTA SST persistence for sustaining the teleconnection, which constantly transmits impacts from the tropical Atlantic into the tropical Pacific. The CMIP6 models show improvements to the CMIP5 models regarding simulations of the spring climatological mean Atlantic ITCZ precipitation and spring‐to‐summer NTA SST persistence. Therefore, the CMIP6 models simulate the NTA–ENSO connection better than the CMIP5 models.

Spatio-Temporal Characteristics and Differences in Snow Density between the Tibet Plateau and the Arctic

The Tibet Plateau (TP) and the Arctic are typically cold regions with abundant snow cover, which plays a key role in land surface processes. Knowledge of variations in snow density is essential for understanding hydrology, ecology, and snow cover feedback. Here, we utilized extensive measurements recorded by 697 ground-based snow sites during 1950–2019 to identify the spatio-temporal characteristics of snow density in these two regions. We examined the spatial heterogeneity of snow density for different snow classes, which are from a global seasonal snow cover classification system, with each class determined from air temperature, precipitation, and wind speed climatologies. We also investigated possible mechanisms driving observed snow density differences. The long-term mean snow density in the Arctic was 1.6 times that of the TP. Slight differences were noted in the monthly TP snow densities, with values ranging from 122 ± 29 to 158 ± 52 kg/m3. In the Arctic, however, a clear increasing trend was shown from October to June, particularly with a rate of 30.3 kg/m3 per month from March to June. For the same snow class, the average snow density in the Arctic was higher than that in the TP. The Arctic was characterized mainly by a longer snowfall duration and deeper snow cover, with some areas showing perennial snow cover. In contrast, the TP was dominated by seasonal snow cover that was shallower and warmer, with less (more) snowfall in winter (spring). The results will be helpful for future simulations of snow cover changes and land interactions at high latitudes and altitudes.

The Impact of ENSO on the Precipitation in Iraq Regions

Abstract Precipitation is considered one of the important weather variables in determining the climate of any region in the world, and perhaps the precipitation in Iraq is witnessing an incomprehensible seasonal fluctuation. The work tries to identify the reasons for this fluctuation, from one season to another. The monthly average precipitation data in Iraq was obtained by Global Precipitation Climatology Project GPCP. Also, data for sea surface temperature anomalies (SST) for the four El Nino regions, as well as relative humidity and zonal wind speed were obtained from the National Centres for Environmental Prediction NCEP and National Centre for Atmospheric Research NCAR. The study period extended from 1980 to 2022, where the correlation of monthly precipitation average in Iraq with El Nino and La Niña events was tested, and it was found that there was a positive proportional effect of the El Nino event on the increase in rainfall rates in Iraq, the correlation coefficient R about 0.8 in North and Northern-East of Iraq. To explain this, the correlation between El Niño events, relative humidity, and zonal wind speed was tested. It was found that there is a proportional effect of El Nino on the relative humidity and an inverse effect on the zonal wind speed. Central and southern Iraq is less affected by El Niño and La Niña events, as the correlation coefficient of monthly precipitation average with El Niño phenomenon is approximately 0.5 to 0.6.