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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS2012

On the Influence of North Pacific Sea Surface Temperature on the Arctic Winter Climate

Differences between two ensembles of Goddard Earth Observing System Chemistry-Climate Model simulations isolate the impact of North Pacific sea surface temperatures (SSTs) on the Arctic winter climate. One ensemble of extended winter season forecasts is forced by unusually high SSTs in the North Pacific, while in the second ensemble SSTs in the North Pacific are unusually low. High Low differences are consistent with a weakened Western Pacific atmospheric teleconnection pattern, and in particular, a weakening of the Aleutian low. This relative change in tropospheric circulation inhibits planetary wave propagation into the stratosphere, in turn reducing polar stratospheric temperature in mid- and late winter. The number of winters with sudden stratospheric warmings is approximately tripled in the Low ensemble as compared with the High ensemble. Enhanced North Pacific SSTs, and thus a more stable and persistent Arctic vortex, lead to a relative decrease in lower stratospheric ozone in late winter, affecting the April clear-sky UV index at Northern Hemisphere mid-latitudes.

Hurwitz, Margaret M.↗
NASA NTRS2013

Impacts of Atmosphere-Ocean Coupling on Southern Hemisphere Climate Change

Climate in the Southern Hemisphere (SH) has undergone significant changes in recent decades. These changes are closely linked to the shift of the Southern Annular Mode (SAM) towards its positive polarity, which is driven primarily by Antarctic ozone depletion. There is growing evidence that Antarctic ozone depletion has significant impacts on Southern Ocean circulation change. However, it is poorly understood whether and how ocean feedback might impact the SAM and climate change in the SH atmosphere. This outstanding science question is investigated using the Goddard Earth Observing System Coupled Atmosphere-Ocean-Chemistry Climate Model(GEOS-AOCCM).We perform ensemble simulations of the recent past (1960-2010) with and without the interactive ocean. For simulations without the interactive ocean, we use sea surface temperatures and sea ice concentrations produced by the interactive ocean simulations. The differences between these two ensemble simulations quantify the effects of atmosphere-ocean coupling. We will investigate the impacts of atmosphere-ocean coupling on stratospheric processes such as Antarctic ozone depletion and Antarctic polar vortex breakup. We will address whether ocean feedback affects Rossby wave generation in the troposphere and wave propagation into the stratosphere. Another focuson this study is to assess how ocean feedback might affect the tropospheric SAM response to Antarctic ozone depletion

Chemistry-Climate Modeling↗
NASA NTRS2022

The Importance of Internal Climate Variability in Climate Impact Projections

Uncertainty in climate projections is driven by three components: scenario uncertainty, intermodel uncertainty, and internal variability. Although socioeconomic climate impact studies increasingly take into account the first two components, little attention has been paid to the role of internal variability, although underestimating this uncertainty may lead to underestimating the socioeconomic costs of climate change. Using large ensembles from seven coupled general circulation models with a total of 414 model runs, we partition the climate uncertainty in classic dose–response models relating county-level corn yield, mortality, and per-capita gross domestic product to temperature in the continental United States. The partitioning of uncertainty depends on the time frame of projection, the impact model, and the geographic region. Internal variability represents more than 50% of the total climate uncertainty in certain projections, including mortality projections for the early 21st century, although its relative influence decreases over time. We recommend including uncertainty due to internal variability for many projections of temperature-driven impacts, including early-century and midcentury projections, projections in regions with high internal variability such as the Upper Midwest United States, and impacts driven by nonlinear relationships.

climate impacts↗
NASA NTRS2015

Projections of Rapidly Rising Temperatures over Africa Under Low Mitigation

An analysis of observed trends in African annual-average near-surface temperatures over the last five decades reveals drastic increases, particularly over parts of the subtropics and central tropical Africa. Over these regions, temperatures have been rising at more than twice the global rate of temperature increase. An ensemble of high-resolution downscalings, obtained using a single regional climate model forced with the sea-surface temperatures and sea-ice fields of an ensemble of global circulation model (GCM) simulations, is shown to realistically represent the relatively strong temperature increases observed in subtropical southern and northern Africa. The amplitudes of warming are generally underestimated, however. Further warming is projected to occur during the 21st century, with plausible increases of 4-6 C over the subtropics and 3-5 C over the tropics by the end of the century relative to present-day climate under the A2 (a low mitigation) scenario of the Special Report on Emission Scenarios. High impact climate events such as heat-wave days and high fire-danger days are consistently projected to increase drastically in their frequency of occurrence. General decreases in soil-moisture availability are projected, even for regions where increases in rainfall are plausible, due to enhanced levels of evaporation. The regional downscalings presented here, and recent GCM projections obtained for Africa, indicate that African annual-averaged temperatures may plausibly rise at about 1.5 times the global rate of temperature increase in the subtropics, and at a somewhat lower rate in the tropics. These projected increases although drastic, may be conservative given the model underestimations of observed temperature trends. The relatively strong rate of warming over Africa, in combination with the associated increases in extreme temperature events, may be key factors to consider when interpreting the suitability of global mitigation targets in terms of African climate change and climate change adaptation in Africa.

climate change↗
NASA NTRS2014

A Practical Philosophy of Complex Climate Modelling

We give an overview of the practice of developing and using complex climate models, as seen from experiences in a major climate modelling center and through participation in the Coupled Model Intercomparison Project (CMIP).We discuss the construction and calibration of models; their evaluation, especially through use of out-of-sample tests; and their exploitation in multi-model ensembles to identify biases and make predictions. We stress that adequacy or utility of climate models is best assessed via their skill against more naive predictions. The framework we use for making inferences about reality using simulations is naturally Bayesian (in an informal sense), and has many points of contact with more familiar examples of scientific epistemology. While the use of complex simulations in science is a development that changes much in how science is done in practice, we argue that the concepts being applied fit very much into traditional practices of the scientific method, albeit those more often associated with laboratory work.

complex simulation↗
NASA NTRS2013

Strengthening of Ocean Heat Uptake Efficiency Associated with the Recent Climate Hiatus

The rate of increase of global-mean surface air temperature (SAT(sub g)) has apparently slowed during the last decade. We investigated the extent to which state-of-the-art general circulation models (GCMs) can capture this hiatus period by using multimodel ensembles of historical climate simulations. While the SAT(sub g) linear trend for the last decade is not captured by their ensemble means regardless of differences in model generation and external forcing, it is barely represented by an 11-member ensemble of a GCM, suggesting an internal origin of the hiatus associated with active heat uptake by the oceans. Besides, we found opposite changes in ocean heat uptake efficiency (k), weakening in models and strengthening in nature, which explain why the models tend to overestimate the SAT(sub g) trend. The weakening of k commonly found in GCMs seems to be an inevitable response of the climate system to global warming, suggesting the recovery from hiatus in coming decades.

Climate hiatus↗
NASA NTRS2010

Quantifying Uncertainty in Projections of Stratospheric Ozone Over the 21st Century

Future stratospheric ozone concentrations will be determined both by changes in the concentration of ozone depleting substances (ODSs) and by changes in stratospheric and tropospheric climate, including those caused by changes in anthropogenic greenhouse gases (GHGs). Since future economic development pathways and resultant emissions of GHGs are uncertain, anthropogenic climate change could be a significant source of uncertainty for future projections of stratospheric ozone. In this pilot study, using an ensemble of opportunity of chemistry-climate model (CCM) simulations, the contribution of scenario uncertainty from different plausible emissions pathways for 10 ODSs and GHGs to future ozone projections is quantified relative to the contribution from model uncertainty and internal variability of the chemistry-climate system. For both the global, annual mean ozone concentration and for ozone in specific geographical regions, differences between CCMs are the dominant source of uncertainty for the first two-thirds of the 21 st century, up-to and after the time when ozone concentrations 15 return to 1980 values. In the last third of the 21st century, dependent upon the set of greenhouse gas scenarios used, scenario uncertainty can be the dominant contributor. This result suggests that investment in chemistry-climate modelling is likely to continue to refine projections of stratospheric ozone and estimates of the return of stratospheric ozone concentrations to pre-1980 levels.

Charlton-Perez, A. J.↗
NASA NTRS2013

Prediction of Seasonal Climate-induced Variations in Global Food Production

Consumers, including the poor in many countries, are increasingly dependent on food imports and are therefore exposed to variations in yields, production, and export prices in the major food-producing regions of the world. National governments and commercial entities are paying increased attention to the cropping forecasts of major food-exporting countries as well as to their own domestic food production. Given the increased volatility of food markets and the rising incidence of climatic extremes affecting food production, food price spikes may increase in prevalence in future years. Here we present a global assessment of the reliability of crop failure hindcasts for major crops at two lead times derived by linking ensemble seasonal climatic forecasts with statistical crop models. We assessed the reliability of hindcasts (i.e., retrospective forecasts for the past) of crop yield loss relative to the previous year for two lead times. Pre-season yield predictions employ climatic forecasts and have lead times of approximately 3 to 5 months for providing information regarding variations in yields for the coming cropping season. Within-season yield predictions use climatic forecasts with lead times of 1 to 3 months. Pre-season predictions can be of value to national governments and commercial concerns, complemented by subsequent updates from within-season predictions. The latter incorporate information on the most recent climatic data for the upcoming period of reproductive growth. In addition to such predictions, hindcasts using observations from satellites were performed to demonstrate the upper limit of the reliability of crop forecasting.

variations↗
NASA NTRSDate not supplied

Estimating Future Changes of Energy Demand for Heating and Cooling Buildings at NASA Centers GC23J-1198

With its unique and trusted earth observations, NASA is a critical source in informing decisions that will help achieve the U.S. goal of Net-Zero Greenhouse Gas (GHG) Emissions by 2050. NASA’s Prediction of Worldwide Energy Resource (POWER) project facilitates the use of NASA Earth Science data holdings within the energy, agricultural, and building heating/cooling design industries. POWER packages solar and meteorological data from several NASA projects in a user friendly GIS-enabled web services system (https://power.larc.nasa.gov). As part of the development of new data products to support the energy and building heating/cooling design communities, we estimate the changes in energy required to heat and cool buildings in the future climate at 14 different NASA site locations spread throughout the continental United States, as projected by CMIP6 climate models under different emissions scenarios. Bias-corrected downscaled time series of meteorological variables are taken from NASA Earth Exchange (NEX) Global Daily Downscaled Projections (GDDP-CMIP6) downscaled climate model data. The spread between the different model projections is accounted for by analyzing both the ensemble average of 22 CMIP6 models and 6 representative models with different climate sensitivities and different interannual variability. Changes in energy use are estimated in two ways. First, changes in the total annual heating and cooling degree days (HDD and CDD, respectively) are calculated relative to the current climate. This is done at all 14 sites. Second, the downscaled time series are used as inputs into RETScreen(R), a clean energy management decision tool, to give an estimate of heating/cooling energy use for a typical office building. We use this estimation method with model data at Langley Research Center. In the next 50 years, the annual total of HDD (CDD) is projected to decrease by 8-38% (increase by 5-28%) at all sites, with the increase in CDD typically a larger magnitude the decrease in HDD. For a typical small office building at Langley Research Center, the amount of energy needed to cool increases by 33-54% and the amount of energy to heat decreases by 29-40%. POWER is working to develop long term climate data services based on these results to include in future data products to provide to users.

Bradley M. Hegyi↗
NASA NTRS2024

Global Nitrogen Needs to Improve Wheat Yield Under Climate Change

Increasing global food demand will require more food production without further exceeding the planetary boundaries, while at the same time adapting to climate change. We used an ensemble of wheat simulation models, with improved yield traits from the highest-yielding wheat genotypes to quantify potential yield gains and associated N requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The improved yield traits increased yield by 16% with current N fertilizer applications under both current climate and mid-century climate change scenarios. To achieve the full yield potential, a 52% increase in global average yield under a mid-century high warming climate scenario (RCP8.5), fertilizer use would need to increase fourfold over current use, which would unavoidably lead to higher environmental impacts from wheat production. Our results show the need to improve soil N availability and N use efficiency, along with yield potential.

Nitrogen↗
NASA NTRS2015

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

Many studies have been carried out during the last decade to study the effect of climate change on crop yields and other key crop characteristics. In these studies, one or several crop models were used to simulate crop growth and development for different climate scenarios that correspond to different projections of atmospheric CO2 concentration, temperature, and rainfall changes (Semenov et al., 1996; Tubiello and Ewert, 2002; White et al., 2011). The Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013) builds on these studies with the goal of using an ensemble of multiple crop models in order to assess effects of climate change scenarios for several crops in contrasting environments. These studies generate large datasets, including thousands of simulated crop yield data. They include series of yield values obtained by combining several crop models with different climate scenarios that are defined by several climatic variables (temperature, CO2, rainfall, etc.). Such datasets potentially provide useful information on the possible effects of different climate change scenarios on crop yields. However, it is sometimes difficult to analyze these datasets and to summarize them in a useful way due to their structural complexity; simulated yield data can differ among contrasting climate scenarios, sites, and crop models. Another issue is that it is not straightforward to extrapolate the results obtained for the scenarios to alternative climate change scenarios not initially included in the simulation protocols. Additional dynamic crop model simulations for new climate change scenarios are an option but this approach is costly, especially when a large number of crop models are used to generate the simulated data, as in AgMIP. Statistical models have been used to analyze responses of measured yield data to climate variables in past studies (Lobell et al., 2011), but the use of a statistical model to analyze yields simulated by complex process-based crop models is a rather new idea. We demonstrate herewith that statistical methods can play an important role in analyzing simulated yield data sets obtained from the ensembles of process-based crop models. Formal statistical analysis is helpful to estimate the effects of different climatic variables on yield, and to describe the between-model variability of these effects.

climate↗
NASA NTRS2014

CMIP5 Historical Simulations (1850-2012) with GISS ModelE2

Observations of climate change during the CMIP5 extended historical period (1850-2012) are compared to trends simulated by six versions of the NASA Goddard Institute for Space Studies ModelE2 Earth System Model. The six models are constructed from three versions of the ModelE2 atmospheric general circulation model, distinguished by their treatment of atmospheric composition and the aerosol indirect effect, combined with two ocean general circulation models, HYCOM and Russell. Forcings that perturb the model climate during the historical period are described. Five-member ensemble averages from each of the six versions of ModelE2 simulate trends of surface air temperature, atmospheric temperature, sea ice and ocean heat content that are in general agreement with observed trends, although simulated warming is slightly excessive within the past decade. Only simulations that include increasing concentrations of long-lived greenhouse gases match the warming observed during the twentieth century. Differences in twentieth-century warming among the six model versions can be attributed to differences in climate sensitivity, aerosol and ozone forcing, and heat uptake by the deep ocean. Coupled models with HYCOM export less heat to the deep ocean, associated with reduced surface warming in regions of deepwater formation, but greater warming elsewhere at high latitudes along with reduced sea ice. All ensembles show twentieth-century annular trends toward reduced surface pressure at southern high latitudes and a poleward shift of the midlatitude westerlies, consistent with observations.

ocean heat content↗
NASA NTRS2014

Reply to Comment by Laprise on 'the Added Value to Global Model Projections of Climate Change by Dynamical Downscaling: a Case Study over the Continental U.S. Using the GISS-ModelE2 and WRF Models'

In his comment, Laprise raises several points that we agree merit consideration. His primary critique is that our study [Racherla et al., 2012] tested the ability of the WRF regional climate model to reproduce historical temperature and precipitation change relative to the driving global climate model (GCM) using only a single simulation rather than an ensemble. He asserts that the observed changes are smaller than the internal variability in the climate system (i.e., not statistically significant) and that thus a single simulation should not necessarily be able to capture the observations. Laprise points out that the statistical signal is reduced for a multi-decadal trend such as the one we analyzed in comparison with mean climatology and cites two studies showing that for particular climate parameters it can take any years for a signal to be discerned over internal variability. He states that The results of theexperiment as designed were strongly influenced by the presence of internal variability and sampling errors,which masked the rather small climate changes that may have occurred as a consequence of changes inforcing during the period considered. While Laprise discusses statistics in general terms at some length, for the actual climate trends examined in our study, he offers no evidence that the forced signal was smallcompared with internal variability. The two studies he cites [de Ela et al., 2013; Maraun, 2013] do not provide convincing evidence as they concern climate variables averaged over different times and areas. One in fact examines extreme precipitation events, which by definition are rare and thus have a lower significance level. We accept the general point that it is important to consider internal variability, and as noted in our paper we agree that an ensemble of simulations is in principle an optimal, though computationally expensive, approach. While we did not present the statistical significance of the observations in our original paper, we have now evaluated those for the regional temperature trends used in our study to evaluate the added value of WRF and thus can analyze data as to the magnitude of the trends with respect to internal variability.

climate↗
NASA NTRS2001

A Study of the Response of Deep Tropical Clouds to Mesoscale Processes: Modeling Strategies and Simulations of TOGA-COARE Convective Systems - Part 1

Interactions between deep tropical clouds over the western Pacific warm pool and the larger-scale environment are key to understanding climate change. Cloud models are an extremely useful tool in simulating and providing statistical information on heat and moisture transfer processes between cloud systems and the environment, and can therefore be utilized to substantially improve cloud parameterizations in climate models. In this paper, the Goddard Cumulus Ensemble (GCE) cloud-resolving model is used in multi-day simulations of deep tropical convective activity over the Tropical Ocean-Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE). Large-scale temperature and moisture advective tendencies, and horizontal momentum from the TOGA-COARE Intensive Flux Array (IFA) region, are applied to the GCE version which incorporates cyclical boundary conditions. Sensitivity experiments show that grid domain size produces the largest response to domain-mean temperature and moisture deviations, as well as cloudiness, when compared to grid horizontal or vertical resolution, and advection scheme. It is found that a minimum grid-domain size of 500 km is needed to adequately resolve the convective cloud features. The control experiment shows that the atmospheric heating and moistening is primarily a response to cloud latent processes of condensation/evaporation, and deposition/sublimation, and to a lesser extent, melting of ice particles. Air-sea exchange of heat and moisture is found to be significant, but of secondary importance, while the radiational response is small. The simulated rainfall and atmospheric heating and moistening, agrees well with observations, and performs favorably to other models simulating this case.

Johnson, Daniel E.↗
NASA NTRS2013

How Well Do Global Climate Models Simulate the Variability of Atlantic Tropical Cyclones Associated with ENSO?

The variability of Atlantic tropical cyclones (TCs) associated with El Nino-Southern Oscillation (ENSO) in model simulations is assessed and compared with observations. The model experiments are 28-yr simulations forced with the observed sea surface temperature from 1982 to 2009. The simulations were coordinated by the U.S. CLIVAR Hurricane Working Group and conducted with five global climate models (GCMs) with a total of 16 ensemble members. The model performance is evaluated based on both individual model ensemble means and multi-model ensemble mean. The latter has the highest anomaly correlation (0.86) for the interannual variability of TCs. Previous observational studies show a strong association between ENSO and Atlantic TC activity, as well as distinctions in the TC activities during eastern Pacific (EP) and central Pacific (CP) El Nino events. The analysis of track density and TC origin indicates that each model has different mean biases. Overall, the GCMs simulate the variability of Atlantic TCs well with weaker activity during EP El Nino and stronger activity during La Nina. For CP El Nino, there is a slight increase in the number of TCs as compared with EP El Nino. However, the spatial distribution of track density and TC origin is less consistent among the models. Particularly, there is no indication of increasing TC activity over the U.S. southeast coastal region as in observations. The difference between the models and observations is likely due to the bias of vertical wind shear in response to the shift of tropical heating associated with CP El Nino, as well as the model bias in the mean circulation.

Tropical Cyclones↗
NASA NTRS2013

An Assessment of the Skill of GEOS-5 Seasonal Forecasts

The seasonal forecast skill of the NASA Global Modeling and Assimilation Office coupled global climate model (CGCM) is evaluated based on an ensemble of 9-month lead forecasts for the period 1993 to 2010. The results from the current version (V2) of the CGCM consisting of the GEOS-5 AGM coupled to the MOM4 ocean model are compared with those from an earlier version (V1) in which the AGCM (the NSIPP model) was coupled to the Poseidon Ocean Model. It was found that the correlation skill of the Sea Surface Temperature (SST) forecasts is generally better in V2, especially over the sub-tropical and tropical central and eastern Pacific, Atlantic, and Indian Ocean. Furthermore, the improvement in skill in V2 mainly comes from better forecasts of the developing phase of ENSO from boreal spring to summer. The skill of ENSO forecasts initiated during the boreal winter season, however, shows no improvement in terms of correlation skill, and is in fact slightly worse in terms of root mean square error (RMSE). The degradation of skill is found to be due to an excessive ENSO amplitude. For V1, the ENSO amplitude is too strong in forecasts starting in boreal spring and summer, which causes large RMSE in the forecast. For V2, the ENSO amplitude is slightly stronger than that in observations and V1 for forecasts starting in boreal winter season. An analysis of the terms in the SST tendency equation, shows that this is mainly due to an excessive zonal advective feedback. In addition, V2 forecasts that are initiated during boreal winter season, exhibit a slower phase transition of El Nino, which is consistent with larger amplitude of ENSO after the ENSO peak season. It is found that this is due to weak discharge of equatorial Warm Water Volume (WWV). In both observations and V1, the discharge of equatorial WWV leads the equatorial geostrophic easterly current so as to damp the El Nino starting in January. This process is delayed by about 2 months in V2 due to the slower phase transition of the equatorial zonal current from westerly to easterly.

GMAO↗
NASA NTRS2018

Past and Future Drought in Mongolia

The severity of recent droughts in semiarid regions is increasingly attributed to anthropogenic climate change, but it is unclear whether these moisture anomalies exceed those of the past and how past variability compares to future projections. On the Mongolian Plateau, a recent decade-long drought that exceeded the variability in the instrumental record was associated with economic, social, and environmental change. We evaluate this drought using an annual reconstruction of the Palmer Drought Severity Index (PDSI) spanning the last 2060 years in concert with simulations of past and future drought through the year 2100 CE. We show that although the most recent drought and pluvial were highly unusual in the last 2000 years, exceeding the 900-year return interval in both cases, these events were not unprecedented in the 2060-year reconstruction, and events of similar duration and severity occur in paleoclimate, historical, and future climate simulations. The Community Earth System Model (CESM) ensemble suggests a drying trend until at least the middle of the 21st century, when this trend reverses as a consequence of elevated precipitation. Although the potential direct effects of elevated CO2 on plant water use efficiency exacerbate uncertainties about future hydroclimate trends, these results suggest that future drought projections for Mongolia are unlikely to exceed those of the last two millennia, despite projected warming.

drought projection↗
NASA NTRS2019

Daily evaluation of 26 precipitation datasets using Stage-IV gauge-radar data for the CONUS

New precipitation (P) datasets are released regularly, following innovations in weather forecasting models, satellite retrieval methods, and multi-source merging techniques. Using the conterminous US as a case study, we evaluated the performance of 26 gridded (sub-)daily P datasets to obtain insight into the merit of these innovations. The evaluation was performed at a daily timescale for the period 2008–2017 using the Kling–Gupta efficiency (KGE), a performance metric combining correlation, bias, and variability. As a reference, we used the high-resolution (4 km) Stage-IV gauge-radar P dataset. Among the three KGE components, the P datasets performed worst overall in terms of correlation (related to event identification). In terms of improving KGE scores for these datasets, improved P totals (affecting the bias score) and improved distribution of P intensity (affecting the variability score) are of secondary importance. Among the 11 gauge-corrected P datasets, the best overall performance was obtained by MSWEP V2.2, underscoring the importance of applying daily gauge corrections and accounting for gauge reporting times. Several uncorrected P datasets outperformed gauge-corrected ones. Among the 15 uncorrected P datasets, the best performance was obtained by the ERA5-HRES fourth-generation reanalysis, reflecting the significant advances in earth system modeling during the last decade. The (re)analyses generally performed better in winter than in summer, while the opposite was the case for the satellite-based datasets. IMERGHH V05 performed substantially better than TMPA-3B42RT V7, attributable to the many improvements implemented in the IMERG satellite P retrieval algorithm. IMERGHH V05 outperformed ERA5-HRES in regions dominated by convective storms, while the opposite was observed in regions of complex terrain. The ERA5-EDA ensemble average exhibited higher correlations than the ERA5-HRES deterministic run, highlighting the value of ensemble modeling. The WRF regional convection-permitting climate model showed considerably more accurate P totals over the mountainous west and performed best among the uncorrected datasets in terms of variability, suggesting there is merit in using high-resolution models to obtain climatological P statistics. Our findings provide some guidance to choose the most suitable P dataset for a particular application.

Hylke E. Beck↗