Global Precipitation Climatology Project Research Articles

Using CloudSat to Advance the Global Precipitation Climatology Project (GPCP) over Antarctica

Remote sensing-based precipitation products face several challenges in high latitudes and specifically over frozen surfaces (i.e., snow and ice). Consequently, precipitation estimates tend to be lower in quality over these regions, including Antarctica, the coldest continent on Earth. In this study, we developed a method for adjusting precipitation estimates over Antarctica by leveraging CloudSat's ability to capture snowfall compared to other satellite products over snow and ice surfaces. We addressed limitations of CloudSat, such as poor spatiotemporal sampling, noise, and incomplete coverage near the poles. We utilized the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis 5th Generation (ERA5) to guide development, particularly during the period when the TIROS Operational Vertical Sounder (TOVS) was contributing. We adjusted monthly and seasonal TOVS and Atmospheric Infrared Sounder – Infrared (AIRS-IR) precipitation biases over Antarctica at the pixel level, as these two products are the primary inputs for the Global Precipitation Climatology Project (GPCP) in high latitudes. We assessed the adjusted TOVS and AIRS-IR through analyses of geographical maps and time series of monthly and seasonal mean precipitation rates. The results are encouraging, indicating that the proposed approach could replace the current approach used in the GPCP for adjusting precipitation estimates from AIRS-IR and TOVS over Antarctica. Adjusting precipitation estimates from TOVS and AIRS-IR using the proposed approach improves the Kling-Gupta efficiency (KGE) over their entire period by 162% and 147%, respectively. Moreover, the proposed approach can be applied to adjust other precipitation products over Antarctica at the pixel level, including satellites and reanalysis products.

Assessment of precipitation and near-surface temperature simulation by CMIP6 models in South America

This study evaluated the performance of 50 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the statistical features of precipitation and air temperature in five subdomains of South America during the historical period (1995–2014). Monthly precipitation and temperature simulations were validated with data from the Climate Prediction Center Merged Analysis of Precipitation, the Global Precipitation Climatology Project, and the ERA5 reanalysis. The models’ performance was evaluated using a ranking analysis with statistical metrics such as mean, standard deviation, Pearson’s spatial correlation, annual cycle amplitude, and linear trend. The analyses considered the representation of precipitation and air temperature separately for each subdomain, the representation for all five regions together, and the joint representation of precipitation and air temperature for all five subdomains. In the Brazilian Amazon, the best-performing models were EC-Earth3-Veg, INM-CM4-8, and INMCM5-0 (precipitation), and IPSL-CM6A-LR, MPI-ESM2-0, and IITM-ESM (temperature). In the La Plata Basin, KACE-1-0-G, ACCESS-CM2, and IPSL-CM6A-LR (precipitation), and GFDL-ESM4, TaiESM1, and EC-Earth3-Veg (temperature) yielded the best simulations. In Northeast Brazil, SAM0-UNICON, CESM2, and MCM-UA-1-0 (precipitation), BCC-CSM2-MR, KACE-1-0-G, and CESM2 (temperature) showed the best results. In Argentine Patagonia, the GCMs ACCESS-CM2, ACCESS-ESM1-5 and EC-Earth3-Veg-LR (precipitation), and CAMS-CSM1-0, CMCC-CM2-HR4, and GFDL-ESM4 (temperature) outperformed. Finally, for Southeast Brazil, the models ACCESS-CM2, ACCESS-ESM1-5, and EC-Earth3-Veg-LR (precipitation), and CAMS-CSM1-0, CMCC-CM2-HR4, and GFDL-ESM4 (temperature) yielded the best simulations. The joint evaluation of the regions and variables indicated that the best models are CESM2, TaiESM1, CMCC-CM2-HR4, FIO-ESM-2-0, and MRI-ESM2-0.

Global Precipitation for the Year 2023 and How It Relates to Longer Term Variations and Trends

Global Precipitation for the Year 2023 and How It Relates to Longer Term Variations and Trends

Monitoring the response of Saudi Arabia's largest fossil aquifer system to climate variability

The study utilizes an integrated approach that involves GRACE and Global Precipitation Climatology Project (GPCP) data to analyze temporal water mass variations in Saudi Arabia (KSA). Recently, KSA has witnessed a historic wet period between 2018 and 2020 (i.e., 120–150 mm/year) following a prolonged dry period between 2002 and 2017 (i.e., 80–100 mm/year). Inspection of GRACE data showed a decline in groundwater recharge of −4 to −6 mm during the dry period. The depletion was reduced by 1 to 6.8 mm in the Northeastern side of KSA (e.g. Hail, Al-Jouf, Al-Hudud Ash-Shamaliyah areas) during the wet period. This impressive recovery has led to a groundwater recharge increase of 360 km3 in Hail, 174 km3 in Al-Jouf and 97 km3 in Al-Hudud Ash-Shamaliyah. The findings emphasize the notion that climate change will bring more frequent extreme climatic events to the Kingdom of Saudi Arabia and call for continuous monitoring of aquifers every year.

The analysis on groundwater storage variations from GRACE/GRACE-FO in recent 20 years driven by influencing factors and prediction in Shandong Province, China

Monitoring and predicting the regional groundwater storage (GWS) fluctuation is an essential support for effectively managing water resources. Therefore, taking Shandong Province as an example, the data from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) is used to invert GWS fluctuation from January 2003 to December 2022 together with Watergap Global Hydrological Model (WGHM), in-situ groundwater volume and level data. The spatio-temporal characteristics are decomposed using Independent Components Analysis (ICA), and the impact factors, such as precipitation and human activities, which are also analyzed. To predict the short-time changes of GWS, the Support Vector Machines (SVM) is adopted together with three commonly used methods Long Short-Term Memory (LSTM), Singular Spectrum Analysis (SSA), Auto-Regressive Moving Average Model (ARMA), as the comparison. The results show that: (1) The loss intensity of western GWS is significantly greater than those in coastal areas. From 2003 to 2006, GWS increased sharply; during 2007 to 2014, there exists a loss rate − 5.80 ± 2.28 mm/a of GWS; the linear trend of GWS change is − 5.39 ± 3.65 mm/a from 2015 to 2022, may be mainly due to the effect of South-to-North Water Diversion Project. The correlation coefficient between GRACE and WGHM is 0.67, which is consistent with in-situ groundwater volume and level. (2) The GWS has higher positive correlation with monthly Global Precipitation Climatology Project (GPCP) considering time delay after moving average, which has the similar energy spectrum depending on Continuous Wavelet Transform (CWT) method. In addition, the influencing facotrs on annual GWS fluctuation are analyzed, the correlation coefficient between GWS and in-situ data including the consumption of groundwater mining, farmland irrigation is 0.80, 0.71, respectively. (3) For the GWS prediction, SVM method is adopted to analyze, three training samples with 180, 204 and 228 months are established with the goodness-of-fit all higher than 0.97. The correlation coefficients are 0.56, 0.75, 0.68; RMSE is 5.26, 4.42, 5.65 mm; NSE is 0.28, 0.43, 0.36, respectively. The performance of SVM model is better than the other methods for the short-term prediction.

Comparative Analysis of the Latest Global Oceanic Precipitation Estimates from GPM V07 and GPCP V3.2 Products

Abstract Satellites bring opportunities to quantify precipitation amount and distribution over the globe, critical to understanding how the Earth system works. The amount and spatial distribution of oceanic precipitation from the latest versions (V07 and the previous version) of the Global Precipitation Measurement (GPM) Core Observatory instruments and selected members of the constellation of passive microwave sensors are quantified and compared with other products such as the Global Precipitation Climatology Project (GPCP V3.2); the Merged CloudSat, TRMM, and GPM (MCTG) climatology; and ERA5. Results show that GPM V07 products have a higher precipitation rate than the previous version, except for the radar-only product. Within ∼65°S–65°N, covered by all of the instruments, this increase ranges from about 9% for the combined radar–radiometer product to about 16% for radiometer-only products. While GPM precipitation products still show lower mean precipitation rate than MCTG (except over the tropics and Arctic Ocean), the V07 products (except radar-only) are generally more consistent with MCTG and GPCP V3.2 than V05. Over the tropics (25°S–25°N), passive microwave sounders show the highest precipitation rate among all of the precipitation products studied and the highest increase (∼19%) compared to their previous version. Precipitation products are least consistent in midlatitude oceans in the Southern Hemisphere, displaying the largest spread in mean precipitation rate and location of latitudinal peak precipitation. Precipitation products tend to show larger spread over regions with low and high values of sea surface temperature and total precipitable water. The analysis highlights major discrepancies among the products and areas for future research.

Multiyear variations of soil moisture availability in the East European Plain

This study aims to examine the impact of climate change on the water storage across the East European Plain, utilizing archived digital materials from several remote sensing satellites, including the Terra/Aqua (MODIS), the Global Precipitation Climatology Project, GRACE, and GRACE FO satellites, as well as data from digital maps of Selyaninov hydrothermal coefficients. The spatial resolution of the analyzed data ranged from 1x1 km to 250x250 km. Aiming to enhance the spatial resolution of Selyaninov coefficient maps, a new version of the Selyaninov hydrothermal coefficient was suggested, leveraging satellite remote sensing data. Both visual and computer analyses of these materials reveal a consistent reduction in water storage in the southern regions of the East European Plain, accompanied by a slight increase in the Novgorod Oblast. This information suggests that the non-chernozem region of the East European Plain will play a crucial role in supplying agricultural products to the population in the next decades. The observed stable water storage in the northern part of the East European Plain, encompassing the Komi Republic and the Novgorod Oblast, hints at the potential of increased agricultural production in these areas. However, achieving sustainable growth in agricultural production in these regions necessitates a focused investment policy.

Spatio-temporal dynamics of methane concentration and its association to climatic and vegetation parameters: a case study of the Northern Cape Province, South Africa

Methane (CH4) is the second most abundant anthropogenic greenhouse gas after carbon dioxide (CO2), accounting for about 20% of global emissions. CH4 anthropogenic emission sources include landfills, oil and natural gas systems, agricultural activities, coal mining, stationary and mobile combustion, wastewater treatment and certain industrial processes. In this work, we examine the spatio-temporal dynamics of CH4 and its relationship to climatic and vegetation parameters in the Northern Cape province in South Africa. Various datasets from the TROPOspheric Monitoring Instrument, Moderate Resolution Imaging Spectroradiometer, Atmospheric Infrared Sounder and the Global Precipitation Climatology Project were used. The results show an increasing trend of CH4 concentration throughout the entire province. The greatest increase in CH4 concentration is observed in the western parts of the province during June–July–August (JJA) season. CH4 concentration shows negligible correlation with most climatic parameters, i.e. Temperature (Temp), Precipitation (Precip) and NDVI for both seasons. The Temp–NDVI relationship shows high correlation values of r   = –0.71 and r = 0.82 for the DJF and JJA seasons, respectively. Seasonality plays a critical role in the relationships of the CH4 to climatic and vegetation parameters. This study shows that we are in a crisis, and robust mitigation strategies are needed to combat this.

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.

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.

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.

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.

Assessment of CMIP6 model performance in simulating atmospheric aerosol and precipitation over Africa

Recent studies have shown that east African region experiences both extreme rainfall and droughts due to presence of atmospheric absorbing aerosols. As such, it is important to understand the Global Climate Models (GCMs) phase6 of Coupled Model Intercomparison Project (CMIP6) performance in capturing the climatological precipitation over the region. This study evaluates the ability of nineteen CMIP6 models in simulating the presence of aerosols and precipitation in both annual and seasonal variation over Africa and east Africa respectively from 1980 to 2014. The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) aerosol reanalysis data and Global Precipitation Climatology Project (GPCP) rainfall data have been used against CMIP6 datasets for validation. Model performance metrics were employed namely bias, root mean square error, spatial correlation coefficient, standard deviation and percentage bias (PBias) to assess the ability of individual models in simulating the two parameters over the study region. The quantitative analysis shows that most models are able to simulate both aerosols and precipitation over the study domain. Among the nineteen models evaluated, five (CMCC-CM2-SR5, E3SM-1-0, E3SM-1-1-ECA, NorESM2-LM and KACE-1-0-G) are showing good performance in reproducing aerosols over Africa. For precipitation, all the models are performing good except BCC-ESM1, CESM2-FV2, CMCC-CM2-SR5 and E3SM-1-1. In general, out of the nineteen models, no single model performs best in relation to observation. The performance of the models varies from season to season. Hence climate users are strongly advised to use projections of atmospheric aerosols and precipitation over the study area from CMIP6 models with caution for decision making on the mitigation or even adaptation of climate change.

Regional Characteristics of Summer Precipitation Anomalies in the Northeastern Maritime Continent

Based on the monthly mean reanalysis data from NCEP/NCAR (National Centers for Environmental Prediction/ National Center for Atmospheric Research) and GPCP (Global Precipitation Climatology Project) (1979–2020), the regional characteristics of precipitation in the warm pool side of the Maritime Continent (MC) and the relationships between different precipitation patterns and atmospheric circulations are studied. The results show that there are significant correlations as well as differences between the precipitation in the east of the Philippines (area A) and that in the Pacific Ocean near the Northern Mariana Islands (area B). Precipitation in area A is closely related to the eastern Pacific ENSO (El Nino-Southern Oscillation) and EAP/PJ (East Asia-Pacific/Pacific-Japan) teleconnection pattern, while precipitation in area B is linked to the Indian Ocean basin-wide and the South China Sea summer monsoon. When the precipitation anomaly in area A is positive, the East Asian summer monsoon is weak. A cyclone appears to the northwest of area A at 850 hPa with convergence airflow. After filtering out the effects of precipitation in area B, the cyclone retreats to the west, and an anticyclone appears to the southeast of area A. When the precipitation is above normal in area B, the circulation and water vapor transportation are similar to that in area A but more to the east. The updraft and downdrafts to both north and south sides of area B form two closed meridional vertical circulations. When the influence of area A is moved out, the circulation center in the warm pool area moves eastward. This research contributes to a better understanding of the regional characteristics of the Maritime Continent and the East Asian summer monsoon.

Performance of GPCP Daily Products Over Oceans: Evaluation Using Passive Aquatic Listeners

AbstractPassive Aquatic Listeners (PALs) have been increasingly deployed to collect minute‐scale surface oceanic rainfall and wind information, with a sampling area similar to the spaceborne sensor footprints. This provides an unprecedented opportunity to validate satellite precipitation products over oceans. This study evaluates the Global Precipitation Climatology Project (GPCP) daily products, including the widely used GPCP v1.3 and the newly released GPCP v3.2, over oceans using 58 PALs as references. The study shows that the GPCP performance depends on time scale, region, and rainfall intensity. The two versions of GPCP perform similarly at multi‐year and monthly scales, while GPCP v3.2 shows substantial improvements in representing rain occurrence and rain intensity at daily scale. The results also highlight the challenge of precipitation measurement over certain regions such as the tropical Southeastern Pacific and extratropical North Pacific, where both versions of the GPCP products perform similarly but exhibit noticeable differences compared to PAL observations.

Evaluation of CMIP6 Models Skill in Representing Annual Extreme Precipitation over Northern and Southern Nigeria

In this study, we evaluate the performance of 13 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating seven (7) extreme precipitation indices over Northern and southern Nigeria. The considered extreme indices designated in this study were, Total precipitation (PRCPTOT), maximum consecutive wet days (CWD), Heavy precipitation days (R10mm), Very heavy precipitation days (R20mm), Max-5 days Precipitation (RX5day), Extremely wet days (R99pTOT) and Very wet days (R95pTOT). The performance of these 13 climate models are assesed by comparing the model simulation to the observed dataset from the Global Precipitation Climatology Project (GPCP). The performance of CMIP6 models in capturing extreme precipitation characteristics is revealed through some selected multiple descriptive statistics: the normalized mean bias error, RMSE, NRMSE, and Taylor diagram. The descriptive statistics conclusively revealed the satisfactory performance of Cmip6 models in simulation of most extreme events over the north and southern region of Nigeria, as the selected 13 climate models showed a high statistical correlation of (~0.8) when compared with the observed GPCP data except for maximum consecutive wet days (CWD). Overall, majority of CMIP6 models were able to accurately represented only six (6) of the extreme indices, and a significant majority of CMIP6 models failed in simulating maximum consecutive wet days (CWD) in both northern and southern Nigeria.

Mass Variations in Terrestrial Water Storage over the Nile River Basin and Mega Aquifer System as Deduced from GRACE-FO Level-2 Products and Precipitation Patterns from GPCP Data

Changes in the terrestrial total water storage (TWS) have been estimated at both global and river basin scales from the Gravity Recovery and Climate Experiment (GRACE) mission and are still being detected from its GRACE Follow-On (GRACE-FO) mission. In this contribution, the sixth release of GRACE-FO (RL06) level-2 products applying DDK5 (decorrelation filter) were used to detect water mass variations for the Nile River Basin (NRB) in Africa and the Mega Aquifer System (MAS) in Asia. The following approach was implemented to detect the mass variation over the NRB and MAS: (1) TWS mass (June 2018–June 2021) was estimated by converting the spherical harmonic coefficients from the decorrelation filter DDK 5 of the GRACE-FO Level-2 RL06 products into equivalent water heights, where the TWS had been re-produced after removing the mean temporal signal (2) Precipitation data from Global Precipitation Climatology Project was used to investigate the pattern of change over the study area. Our findings include: (1) during the GRACE-FO period, the mass variations extracted from the RL06-DDK5 solutions from the three official centers—CSR, JPL, and GFZ—were found to be consistent with each other, (2) The NRB showed substantial temporal TWS variations, given a basin average of about 6 cm in 2019 and about 12 cm in 2020 between September and November and a lower basin average of about −9 cm in 2019 and −6 cm in 2020 in the wet seasons between March and May, while mass variations for the MAS had a relatively weaker temporal TWS magnitude, (3) the observed seasonal signal over the NRB was attributed to the high intensity of the precipitation events over the NRB (AAP: 1000–1800 mm yr−1), whereas the lack of the seasonal TWS signal over the MAS was due to the low intensity of the precipitation events over the MAS (AAP: 180–500 mm yr−1).

Bias and Trend Correction of Precipitation Datasets to Force Ocean Models

Abstract A novel method to adjust the precipitation produced by atmospheric reanalyses using observational constraints to force ocean models is described. The method allows the preservation of the qualities of the high-resolution and high-frequency output from the reanalyses while eliminating their bias and spurious trends. The method is shown to be robust to degradation in both space and time of the observation dataset. This method is applied to the ERA-Interim precipitation dataset using the Global Precipitation Climatology Project (GPCP) v2.3 as the observational reference in order to create a debiased dataset that can be used to force ocean models. The produced debiased dataset is then compared to ERA-Interim and GPCP in a suite of forced ice–ocean numerical experiments using the GFDL OM4 model. Ocean states obtained with the new precipitation dataset are consistent with results from GPCP-forced experiments with respect to global metrics but produces the extra sea surface salinity variability at the time scales unresolved by the observation-based dataset. Discrepancies between modeled and observed freshwater fluxes are discussed as well as the strategies to mitigate them and their impacts.

Interdecadal change in autumn rainfall over Southeast China and its association with tropical Pacific SST

In this paper, Japan Meteorological Agency (JRA-55) reanalysis and observational rainfall datasets from the National Climate Center (NCC) of China, as well as satellite datasets from the Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Climatology Project (GPCP), and International Satellite Cloud Climatology Project (ISCCP), are used. The correlation coefficient and fast Fourier transform (FFT) low-pass filter are also used, in order to reveal the interdecadal decrease in autumn rainfall in Southeast China (SEC) after 1990. The close and robust relationship between the interdecadal variation in autumn rainfall in SEC and sea surface temperature (SST) in the tropical Pacific is investigated. The most significant and stable region of correlation is located in 10° S–10° N, 160° E–160° W, in which there also exists interdecadal warming after 1990. Furthermore, the interdecadal warming of SST can induce Gill responses of the atmosphere: a cyclone anomaly is produced on each side of the equator in the lower troposphere, with a westerly anomaly to the west of the dateline, and an anticyclone anomaly is produced in the upper troposphere. In particular, the cyclone anomaly on the northern side of the equator is located in the Northwest Pacific (NWP), and its ambient northerly airflow weakens meridional water vapor transport, as well as the local descending motion and low-troposphere divergence, in favor of the interdecadal decrease in SEC rainfall after 1990. In addition, the sensitive experiments with ECHAM-5.4 model also confirm that the interdecadal warming in the region (10° S–10° N, 160° E–160° W) would motivate the atmospheric Gill response and thereby cause the sinking motion in SEC and support the interdecadal decrease in autumn rainfall in SEC.