Land Precipitation Research Articles

Combining Temperature and Precipitation to Constrain the Aerosol Contribution to Observed Climate Change

Abstract Using the past to improve future predictions requires an understanding and quantification of the individual climate contributions to the observed climate change by aerosols and greenhouse gases (GHGs), which is hindered by large uncertainties in aerosol forcings and responses across climate models. To estimate historical aerosol responses, we apply detection and attribution methods to attribute a joint change in temperature and precipitation to forcings by combining signals of observed changes in tropical wet and dry regions, the interhemispheric temperature asymmetry, global mean temperature (GMT), and global mean land precipitation (GMLP). Fingerprints representing the climate response to aerosols (AERs) and the remaining external forcings (noAER; mostly GHG) are derived from large ensembles of historical single- and ALL-forcing simulations from three models in phase 6 of the Coupled Model Intercomparison Project and selected using a perfect model study. Results from an imperfect model study and a hydrological sensitivity analysis support combining our choice of temperature and precipitation fingerprints into a joint study. We find that diagnostics including temperature and precipitation slightly better constrain the noAER signal than diagnostics based purely on temperature or GMT-only and allow for the attribution of AER cooling (even when GMT is not included in the fingerprint). These results are robust across fingerprints from different climate models. Estimated contributions for AER and noAER agree with other published estimates including those from the most recent IPCC report. Finally, we attribute the best estimate of 0.46 K ([−0.86, −0.05] K) of aerosol-induced cooling and 1.63 K ([1.26, 2.00] K) of noAER warming in 2010–19 relative to 1850–1900 using the combined signals of GMT and GMLP. Significance Statement Aerosols are small liquid or solid airborne particles. They are predominantly the secondary result of emissions of aerosol precursor gases emitted via industrial or natural processes. While greenhouse gases warm the climate, aerosols can have a cooling effect on the climate system, thus offsetting some of the greenhouse gas–related warming. We expect greenhouse gas concentrations in the atmosphere to continue to increase, while aerosol concentrations are likely going to decline due to their impacts on human health. Our study uses observed temperature and precipitation changes to quantify how much aerosols have offset warming from past greenhouse gas emissions. This can help constrain future predictions of global warming.

Correction: Changes in extreme integrated water vapor transport on the U.S. west coast in NA-CORDEX, and relationship to mountain and inland precipitation

Correction: Changes in extreme integrated water vapor transport on the U.S. west coast in NA-CORDEX, and relationship to mountain and inland precipitation

Improving inland precipitation forecast in China through data assimilation of microwave temperature sounding data from a three‐orbit constellation

Improving inland precipitation forecast in China through data assimilation of microwave temperature sounding data from a three‐orbit constellation

Performance of CMIP5 and CMIP6 models in reproducing the Interdecadal Pacific Oscillation and its global impacts

AbstractThis study assessed the capability of the historical simulations of phase 5 and 6 of the Coupled Model Intercomparison Project (CMIP5/6) in reproducing the temporal and spatial characteristics of the Interdecadal Pacific Oscillation (IPO) and its impact on global surface air temperature (SAT), surface equivalent potential temperature (Thetae_sfc) and precipitation. The IPO index time series simulated by CMIP5/6 models deviated from observations and struggled to capture the phase evolution characteristics of the IPO. Nevertheless, CMIP5/6 models successfully captured the horseshoe‐shaped sea surface temperature anomaly in the Pacific. Additionally, the CMIP5/6 models were able to simulate the IPO's 10–30‐year period. Notably, the simulated IPO index exhibited a statistically significant upward trend, which was absent in observations. Additionally, the IPO‐related global land SAT, Thetae_sfc and precipitation simulated by CMIP5/6 models performed differently in boreal winter and boreal summer. Furthermore, the IPO‐related global land SAT performed better in CMIP5/6 models during boreal winter than that in boreal summer. In CMIP6 models, it improved during both boreal winter and summer compared to CMIP5 models. In terms of the IPO‐related global land Thetae_sfc, CMIP5/6 models also performed better during boreal winter than in boreal summer. However, CMIP5 models outperformed CMIP6 models during the boreal summer. In terms of the IPO‐related global land precipitation, CMIP5/6 models performed better during boreal summer compared to boreal winter. Moreover, the IPO‐related global land precipitation in CMIP6 models improved significantly in boreal winter, but almost the same in boreal summer, compared to CMIP5 models. Further studies showed that the enhancements in simulating IPO's spatial pattern did not correspond to improvements in the model's ability to simulate IPO's global teleconnections.

Earth system model’s capability of predicting drought-induced crop failure

We used the Meteorological Research Institute Earth System Model to investigate land precipitation and net primary production (NPP, proxy for agricultural production here) in terms of food and water security. In the preindustrial state, the largest decrease in Southeast Asian precipitation (> 20%) occurs along with the largest El Niño and positive phase of the Atlantic multidecadal oscillation, consistent with the 1876-78 Great Drought, leading to the largest decrease in NPP (~ 20%); >2 standard deviations, extreme dryness and crop failure. In relatively arid regions (e.g., India and northeastern Brazil), the largest decreases in precipitation and NPP reach ≥ 50%, particularly 80 ~ 90% in Deccan, India. In the 21st century warming projection (Shared Socioeconomic Pathways 5-8.5, the largest CO2 emission scenario), the interannual variability of precipitation and NPP in Monsoon Asia increase under the influence of enhanced El Niño-Southern Oscillation. Although the increased precipitation variability indicates more frequent severer droughts than in the preindustrial state, this severity does not influence NPP so much: the increased NPP variability is negligible compared to the significant increase in normal NPP by CO2 fertilization effect, which indicates usually rich harvests. Compared to a recent severe crop failure (− 25% in Japan in 1993), the simulated preindustrial drought-induced NPP decreases of ≥ 50% in parts of low latitudes are extraordinarily large enough to correspond to devastating crop failures. The model indicates that great famines before the 20th century were natural in origin, and that manmade factors, such as harsh taxes and hoarding and export of grain, made the famines more devastating.

The disproportionate impact of enhanced evaporation from melting arctic sea ice on cold-season land precipitation trends

Diminishing Arctic sea ice has led to enhanced evaporation from the Arctic marginal seas (AMS), which is expected to alter precipitation over land. In this work, AMS evaporation is numerically tracked to quantify its contribution to cold-season (October–March) precipitation over land in the Northern Hemisphere during 1980–2021. Results show a significant 32% increase in AMS moisture contribution to land precipitation, corresponding to a 16% increase per million square km loss of sea ice area. Especially over the high-latitude land, despite the fractional contribution of AMS to precipitation being relatively low (8%), the augmented AMS evaporation contributed disproportionately (42%) to the observed upward trend in precipitation. Notably, northern East Siberia exhibited a substantial rise in both the amount and fraction of extreme snowfall sourced from the AMS. Our findings underscore the importance of the progressively ice-free Arctic as an important contributor to the escalating levels of cold-season precipitation and snowfall over northern high-latitude land.

The Role of Atmospheric Stabilities and Moisture Convergence in the Enhanced Dry Season Precipitation over Land from 1979 to 2021

Abstract Between 1979 and 2021, global ocean regions experienced a decrease in dry season precipitation, while the trend over land areas varied considerably. Some regions, such as southeastern China, the Maritime Continent, eastern Europe, and eastern North America, showed a slight increasing trend in dry season precipitation. This study analyzes the potential mechanisms behind this trend by using the fifth major global reanalysis produced by ECMWF (ERA5) data. The analysis shows that the weakening of downward atmospheric motions played a critical role in enhancing dry season precipitation over land. An atmospheric moisture budget analysis revealed that larger convergent moisture fluxes lead to an increase in water vapor content below 400 hPa. This, in turn, induced an unstable tendency in the moist static energy profile, leading to a more unstable atmosphere and weakening downward motions, which drove the trend toward increasing dry season precipitation over land. More water vapor in the low troposphere is because of higher moisture convergence and moisture transport from ocean to land regions. In summary, this study demonstrates the intricate elements involved in altering dry season rainfall trends over land, which also emphasizes the importance of comprehending the spatial distribution of the wet-get-wetter and dry-get-drier paradigm. Significance Statement This study found that global land precipitation during the dry season slightly increased from 1979 to 2021, while precipitation over oceans declined. Moist static energy analysis showed a trend toward less stability in areas with increased dry season precipitation and the opposite trend in regions with declining precipitation. Water vapor content trends and dynamic components were the primary controlling mechanism for precipitation trends. Furthermore, the hotspots with pronounced increases or decreases in dry season precipitation reflect local circulation changes influenced by anthropogenic or natural factors.

Tropical cyclone-driven rainfall in the northeast Indian Ocean and adjoining land regions during the period 2000–2020

Cyclone-induced rainfall analysis plays a significant role in cyclone-related disaster mitigation and management. Here, we present a comprehensive analysis of tropical cyclone (TC)-induced rainfall in Bay of Bengal (BoB) and its adjacent land regions by the land falling TCs for the past two decades (2000–2020). An average of 12.6 mm/day of land precipitation and 28.75 mm/day of oceanic precipitation are found within 300 km radius of the cyclones, as analysed from the 54 cyclones occurred during the study period. It is observed that the highest TC rainfall contribution to the total rainfall is in the winter months of January and February (40%), whereas the smallest is in the monsoon months of June, July, August and September (0.85%). The TC rainfall contributes about 10% to the annual rainfall over the land regions. The quadrant analysis shows that the TC rainfall depends on the season of TC formation and its direction. About 40.47% of BoB cyclones has the highest rainfall in the right-top quadrant, 35.15% in the left-top quadrant and 14.18% in the left-bottom quadrant. Our study provides key information on land falling TCs and their potential to inflict disasters and thus, this information can be used to provide better safety measures for TC-related hazards such as heavy rainfall and floods. This is particularly important, as the TCs can be more intense and frequent in the context of climate change.

How does climate change affect potential yields of four staple grain crops worldwide by 2030?

Global food security basically depends on potential yields of staple grain crops worldwide, especially under climate change. However, most scholars use various models of production function in which climatic factors are often considered to estimate crop yield mostly at local or regional level. Therefore, in this paper: Potential yields of rice, wheat, maize and soybean worldwide by 2030 are projected creatively using Auto-regressive Integrated Moving Average and Trend Regressed (ARIMA-TR) model in which actual yields in recent two years are used for testing the reliability of projection and Gray System (GS) model for validating the test; Especially individual impacts of climate change on the productions of rice, wheat, maize and soybean worldwide since 1961 are analyzed by using unary regression model in which global mean temperature and land precipitation are independent variable while the yield of crop being dependent one, respectively. Results show that: by 2030, the ratio between average and top yields of world rice is projected to be 50.6% increasing, while those of world wheat, world maize and world soybean are projected to be 38.0% increasing, 14.7% decreasing and 72.5% increasing, respectively. Since 1961 global warming has exerted a negative impact on average yield of world rice less than on its top, a positive effect on average yield of world wheat while a negative impact on its top, a positive effect on average yield of world maize less than on its top, and a positive influence on average yield of world soybean while a negative one on its top, which might be slightly mitigated by 'Carbon Peak' target. The fluctuation of global rainfall contributes to the productions of these crops much less than global warming during same period. Our findings indicate that: to improve global production of four staple grain crops by 2030, the priorities of input should be given to either rice or wheat in both high and low yield countries, whereas to maize in high yield countries and to soybean in low yield countries. These insights highlight some difference from previous studies, and provide academia with innovative comprehension and policy-decision makers with supportive information on sustainable production of these four staple grain crops for global food security under climate change in the future.

Identifying Robust Changes of Extreme Precipitation in Japan From Large Ensemble 5‐km‐Grid Regional Experiments for 4K Warming Scenario

AbstractTo evaluate the impacts of global warming on local‐scale extreme precipitation in Japan, 720‐year ensemble dynamical downscaling is conducted by a regional climate model with a 5 km grid spacing. Our dynamical downscaling is based on the large ensemble data set called the database for Policy Decision making for Future climate change (d4PDF). Comparing the historical and 4K warming climates, the annual maximum daily and hourly precipitation are enhanced over Japan due to global warming. The increasing rate of annual maximum daily precipitation is larger over the coast of the Pacific Ocean in eastern and western Japan and the northern parts of Japan. The periods in which the extreme daily precipitation increases in each region depend on the movement of the Baiu front and the number of typhoons from June through September. The 50‐year return value of the maximum 24‐hr land precipitation within 500 km of the typhoon center is 960 mm/24hr in the 5 km experiment. This value is larger by 1.87 times than that of the 20 km experiment. The rate of future increase in the 50‐year return value related to typhoons is 1.30 relative to the present values for the 5 km experiments. The local‐scale quasi‐stationary band‐shaped precipitation system, which is called senjo‐kousuitai in Japanese, sometimes brings local‐scale heavy precipitation to Japan. The senjo‐kousuitai is well reproduced in the 5 km experiments and shows increases in frequency and intensity under the 4K warming condition. This study also discusses the application of our 5 km experiments to hydrological fields.

The Long-Term Trends of Global Land Precipitation in GFDL’s CM4 and ESM4 Climate Models

Abstract Historical precipitation and temperature trends and variations over global land regions are compared with simulations of two climate models focusing on grid points with substantial observational coverage from the early twentieth century. Potential mechanisms for the differences between modeled and observed trends are investigated using subsets of historical forcings, including ones using only anthropogenic greenhouse gases or aerosols, and simulations forced with the observed sea surface temperature and sea ice distribution. For century-scale (1915–2014) precipitation trends, underestimated increasing or unrealistic decreasing trends are found in the models over the extratropical Northern Hemisphere. The temporal evolution of key discrepancies between the observations and simulations indicates that 1) for averages over 15°–45°N, while there is not a significant trend in observations, both models simulate reduced precipitation from 1940 to 2014, and 2) for 45°–80°N observations suggest sizable precipitation increases while models do not show a significant increase, particularly during ∼1950–80. The timing of differences between models and observations suggests a key role for aerosols in these dry trend biases over the extratropical Northern Hemisphere. Additionally, 3) for 15°S–15°N the observed multidecadal decrease over tropical west Africa (1950–80) is only roughly captured by simulations forced with observed sea surface temperature; additionally, 4) in the all-forcing runs, the model with higher global climate sensitivity simulates increasing trends of temperature and precipitation over lands north of 45°N that are significantly stronger than the lower-sensitivity model and more consistent with the observed increases. Thus, underestimated greenhouse gas–induced warming—particularly in the lower sensitivity model—may be another important factor, besides aerosols, contributing to the modeled biases in precipitation trends.

Stable isotope compositions and vapor sources of precipitation in the Yellow River Delta, China.

To uncover the vapor source, formation mechanism, and the influence of meteorological factors on precipitation in the saline land of the Yellow River Delta, I employed stable isotopes of precipitation, especially for δ17O and 17O-excess, along with the Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT), to analyze the isotopic variation characteristics of precipitation and water vapor sources at different temporal scales and precipitation intensities [(<5, 5-10, 10-25, 25-50, >50 mm·d-1)] from May to October in Dongying, located in the Yellow River Delta. There were wide ranges of isotopes in the daily precipitation data between May and October, with smaller ranges and enriched average values during the dry season. The slope between δ'18O and δ'17O was the minimum of 0.5211 when precipitation intensity was below 5 mm·d-1, indicating the potential influence of evaporation from the moisture source site on precipitation. The maximum value was 0.5268 when precipitation intensity was between 10 mm·d-1 and 25 mm·d-1. For precipitation intensities below 50 mm·d-1 with four different intensities, δ2H, δ18O and δ17O decreased with the increase of precipitation. During the dry season, 17O-excess exhibited a positive relationship with temperature, suggesting the influence of continental circulating water vapor on precipitation. Conversely, in the wet season, 17O-excess displayed a negative relationship with relative humidity (RH), indicating less influence of evaporation. Analysis of air mass back trajectories using the HYSPLIT model indicated that precipitation during the dry season was primarily influenced by the continental monsoon, while precipitation during the wet season was affected by both oceanic and continental monsoons. In conclusion, precipitation in the Yellow River Delta is influenced by the evaporation of various water vapor sources, local meteorological factors, and atmospheric water vapor sources, resulting in different isotopic signatures across different scales. The fin-dings would provide a scientific basis for the allocation of scarce water resources in the Yellow River Delta.

Increased precipitation over land due to climate feedback of large-scale bioenergy cultivation

Bioenergy with carbon capture and storage (BECCS) is considered to be a key technology for removing carbon dioxide from the atmosphere. However, large-scale bioenergy crop cultivation results in land cover changes and activates biophysical effects on climate, with earth’s water recycling altered and energy budget re-adjusted. Here, we use a coupled atmosphere-land model with explicit representations of high-transpiration woody (i.e., eucalypt) and low-transpiration herbaceous (i.e., switchgrass) bioenergy crops to investigate the range of impact of large-scale rainfed bioenergy crop cultivation on the global water cycle and atmospheric water recycling. We find that global land precipitation increases under BECCS scenarios, due to enhanced evapotranspiration and inland moisture advection. Despite enhanced evapotranspiration, soil moisture decreases only slightly, due to increased precipitation and reduced runoff. Our results indicate that, at the global scale, the water consumption by bioenergy crop growth would be partially compensated by atmospheric feedbacks. Thus, to support more effective climate mitigation policies, a more comprehensive assessment, including the biophysical effects of bioenergy cultivation, is highly recommended.

Different uncertainty in tropical oceanic and land precipitation sensitivities under global warming

The change in precipitation (P) under global warming has far-reaching implications for society. However, the high degree of intermodel uncertainty in future projections undermines the reliability of the multi-model mean. The present study investigates the sources of intermodel uncertainty in tropical-mean oceanic and land P sensitivities under global warming, based on an analysis of 28 models from CMIP6. Results show that the uncertainty in tropical-mean P sensitivity is greater over ocean compared to that over land, and both are closely tied to uncertainties in tropical-mean circulation sensitivity. Nevertheless, the underlying drivers of the uncertainty in tropical-mean circulation sensitivity vary between ocean and land. For the tropical-mean oceanic part, the tropical sea surface temperature (SST) warming pattern, particularly the SST warming over the western equatorial Pacific, plays a crucial role by modifying the atmospheric stability over the tropical Pacific. On the other hand, for the tropical-mean land part, the combined impacts of changes in land sea level pressure, relative humidity and SST warming over the equatorial Pacific contribute to the intermodel uncertainty in circulation sensitivity. The results highlight the importance of improving the simulation of these factors to reduce the discrepancies in tropical-mean oceanic and land circulation sensitivities among climate models, ultimately leading to a more accurate depiction of P sensitivities under global warming.

A comparative analysis of the sea breeze on the Texas Gulf Coast and its impact on precipitation

An analysis of the sea breeze, its mechanisms and correlation with precipitation was performed in two cases for an urban city on the Gulf Coast where the first case examined pure sea breezes and the second investigated those associated with a likelihood for appreciable inland precipitation. Adjacent study areas to the east and west were used as background conditions to determine if changes in Houston's sea breeze are controlled by urbanization or climate change. Sea breeze days were selected by a three-filter algorithm using the difference in land and sea surface temperatures, ΔTSB, as well as geostrophic and surface winds. Results showed a decrease of 3–21% in sea breeze days between cases in the Houston area. Case 2 exhibited the most (e.g., 524 sea breeze days) and greatest increase (0.63 day yr−1) in sea breeze days in Beaumont, driven by an increase in land surface temperature which is statistically significant (p-value = 0.02) and decrease in sea surface temperature leading to an increase in ΔTSB, again with statistical significance (p-value = 0.01). Precipitation has increased by at least 1.07 × 10−8 mm day−1, with the greatest increase of 2.59 × 10−8 mm day−1 in Beaumont. The only increase in precipitation that was not found to be statistically significant existed for the most inland station in Houston. Based on an areal analysis of land cover, Houston is significantly more urban than the adjacent study areas: 65.8% of its area falls into the four developed classes, and these classes have increased by 11.4% between 2001 and 2019. Comparatively, Beaumont and Victoria are 16.8% and 7.16% developed, and urban development increased by 1.20% and 0.61%, respectively, over the same period. This indicates the possibility that the roughness elements in Houston's built environment interfere with the sea breeze, while climate change has driven its increase in Beaumont and decrease in Victoria as evidenced by our reported changes in meteorological variables.

Limited Skill of Projected Land Precipitation by IPCC Models During 2002−2020

AbstractClimate models are widely used to project future climate changes and analyze underlying mechanisms. However, it remains unknown whether the earlier precipitation projections match the subsequent observations in recent decades, which is the scientific basis for the confidence of precipitation projections and associated impacts. Here, we find models in the Third (TAR), Fourth (AR4), and Fifth (AR5) Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC) skillfully project the subsequent climatological changes in global mean land precipitation for 2002–2020, 2008–2020, and 2014–2020 (relative to 1980–1999 climatology) from several to 10 years ahead, respectively. Skillful regional projections are mainly found in the northern mid to high latitudes. IPCC models are less skillful in projecting subsequent changes in land precipitation at regional scales than at global scales, which is likely to be at least partly explained by the lower signal‐to‐noise ratio.

The reconstruction for the monthly surface air temperature over the Tibetan Plateau during 1901–2020 by deep learning

The Tibetan Plateau (TP) has experienced a prominent warming in recent decades, and there are many datasets to help people to understand the variations and distributions of climate change over the TP. However, the observed stations, satellite images, simulation models, and reanalysis products have weaknesses of sparse spatial distribution, short-term span, inconvenient access, and coarse resolution, respectively. Therefore, it is scarce for the high-resolution and long-term temperature dataset over the TP. In this study, based on the Climatic Research Unit (CRU) gridded data, the monthly surface air temperature dataset with a spatial resolution of 0.1° over the TP during 1901–2020 was reconstructed by a deep learning method called the Generative adversarial networks (GAN). Compared with the observation, the accuracy of temperature from the GAN (with Root Mean Square Error and Correlation Coefficients of 1.35 °C and 0.97) is improved compared to the CRU (with Root Mean Square Error and Correlation Coefficients of 3.30 °C and 0.82). In comparison with the CRU, Global Land Precipitation and Temperature (GLPT), ERA5, and NCEP/NCAR, the temperature from the GAN have the best robustness, especially over the western TP, suggesting the generated dataset has the best performance. Besides, compared to CRU, the temperature of GAN has an improved resolution and can present a more detailed texture structure of the temperature. Compared to Downscaling CRU with the same resolution, the temperature of GAN inherits the spatial and temporal distribution characteristics of CMFD and therefore better reflects the variation of temperature with longitude. Overall, the reconstruction for the monthly surface air temperature over the TP during 1901–2020 has a long-term temporal span and high spatial resolution, which provides an alternative basis for the temperature change over the TP.

Change of the wintertime multidecadal land precipitation variability in the mid‐1970s in the observation and CMIP6 simulations

AbstractThe wintertime multidecadal land precipitation (WMLP) variability is obtained using the ensemble empirical mode decomposition, and then the leading spatial pattern and temporal evolution of the WMLP in the northern hemisphere during the period of 1891–2014 are investigated. The time series shows a pronounced enhancement of the variability since the mid‐1970s, coincident with the 1976/1977 climate transition, with the variance increasing from 0.33 in the former period (P1) to 2.57 in the latter period (P2). The spatial structure in P2 resembles that in the entire period. The precipitation anomaly is positive in the North America between 30° and 50°N, South America and southern Europe; while it is negative in the southern North America, majority of Russia, Arabian Peninsula, Iran and South China. However, the spatial pattern in P1 differs from the entire period. An Interdecadal Pacific Oscillation (IPO)‐like sea surface temperature (SST) variability in cold phase and an Atlantic Multidecadal Oscillation (AMO)‐like SST variability in warm phase are associated with the leading mode of WMLP in P2 and the entire period. Yet, the relationship of IPO and AMO on the WMLP is barely found in P1. The in‐phase change of IPO and AMO in P1 may contribute to the smaller amplitude of WMLP variability in P1. In the end, the coupled models from CMIP6 historical runs are evaluated. The good‐models average is superior to any single good model in capturing the observed spatial features of WMLP in both P1 and P2. The poor‐models average performs better than any single poor model. Both good‐ and poor‐models averages are able to reproduce the IPO‐ and AMO‐like SST variabilities in P2.

Lagged effect of the Pacific Decadal Oscillation on decadal variation in global land precipitation

&lt;p&gt;The Pacific Decadal Oscillation (PDO), as the leading mode of sea surface temperature (SST) in the North Pacific, modulates the global temperature and precipitation. While previous studies have shown a negative relationship between the global precipitation and the PDO, the time-delayed feature of this relationship remains underexplored. Here we investigate the lagged effect of the decadal variations in the PDO on the global land precipitation using cross-correlation at multiple scales. We find that there is a delayed response of precipitation to the PDO, with the regional correlation peaking at a certain delay and gradually decreasing with increasing lag time. We note the asymmetric impacts of the positive and negative PDO phases on precipitation. Seasonal analysis reveals that the PDO is related to land precipitation during the subsequent season relative to the rest of the year, with the highest correlation occurring in the boreal winter. The delay in response is likely due to the constant SST forcing of the PDO to the atmospheric circulation. This study highlights the lag duration of the PDO�Cprecipitation relationship, potentially enriching our understanding of this relationship and enhancing climate predictions on decadal timescales.&lt;/p&gt;

Disentangling the Contribution of Moisture Source Change to Isotopic Proxy Signatures: Deuterium Tracing with WRF-Hydro-Iso-Tag and Application to Southern African Holocene Sediment Archives

Abstract It is well accepted that global circulation models equipped with stable water isotopologues help us better understand the relationships between atmospheric circulation changes and isotope records in paleoclimate archives. Still, isotope-enabled models do not disentangle the different processes affecting precipitation isotopic compositions. Furthermore, the relevance of this model-oriented approach relies on the realism of the modeled isotope results, which would support the interpretation of the proxy records in terms of modeled climate changes. To alleviate these limitations, the newly developed WRF-Hydro-iso-tag, a version of the isotope-enabled regional coupled model WRF-Hydro-iso enhanced with an isotope-tracing procedure, is presented. Physics-based WRF-Hydro-iso-tag ensembles are used to regionally downscale the isotope-enabled Community Earth System Model for southern Africa, for two 10-yr slices of mid-Holocene and preindustrial times. The isotope-tracing procedure is tailored to assess the origin of the hydrogen isotope deuterium contained in southern African precipitation, between the Atlantic and Indian Oceans. In comparison to the global model, WRF-Hydro-iso-tag simulates lower precipitation amounts with more regional details, as well as mid-Holocene-to-preindustrial changes in precipitation isotopic compositions that better match plant-wax deuterium records from two marine sediment cores off the Orange and Limpopo River basins. Linear relationships between mid-Holocene-to-preindustrial changes in temperature, precipitation amount, moisture source, and precipitation deuterium compositions are derived from the ensemble results. A deuterium enrichment in the Orange River-related sediment core may not be related to an aridification but rather indicate a summer circulation change enabling a larger contribution of more isotopically enriched moisture from the Atlantic Ocean. Significance Statement The knowledge of past climates is crucial for understanding our Earth system and apprehending future climate change. Plant materials in sediment archives contain atoms of hydrogen from past precipitation that allow paleoclimate reconstructions, using compositions of the hydrogen isotope deuterium. However, in the tropics, deuterium-depleted plant remains can either denote a wetter climate phase or a change in atmospheric circulation patterns with longer distances between ocean evaporation and land precipitation. This work provides an innovative dynamical downscaling method of global paleoclimate models to disentangle the effects of precipitation amount change and moisture source change on deuterium records, and ultimately to improve paleoclimate reconstructions. The interpretation of a deuterium enrichment in a marine sediment core as a marker for aridification is revised. The enrichment caused by an atmospheric circulation change bringing a larger amount of more isotopically enriched moisture flow from the Atlantic Ocean to southern African precipitation would be a more physically sound explanation.