Evaluation of Asian summer precipitation in different configurations of a high-resolution general circulation model in a range of decision-relevant spatial scales

High-resolution general circulation models (GCMs) can provide new insights into the simulated distribution of global precipitation. We evaluate how summer precipitation is represented over Asia in global simulations with a grid length of 14 km. Three simulations were performed: one with a convection parametrization, one with convection represented explicitly by the model's dynamics, and a hybrid simulation with only shallow and mid-level convection parametrized. We evaluate the mean simulated precipitation and the diurnal cycle of the amount, frequency, and intensity of the precipitation against satellite observations of precipitation from the Climate Prediction Center morphing method (CMORPH). We also compare the high-resolution simulations with coarser simulations that use parametrized convection. The simulated and observed precipitation is averaged over spatial scales defined by the hydrological catchment basins; these provide a natural spatial scale for performing decision-relevant analysis that is tied to the underlying regional physical geography. By selecting basins of different sizes, we evaluate the simulations as a function of the spatial scale. A new BAsin-Scale Model Assessment ToolkIt (BASMATI) is described, which facilitates this analysis. We find that there are strong wet biases (locally up to 72 mm d−1 at small spatial scales) in the mean precipitation over mountainous regions such as the Himalayas. The explicit convection simulation worsens existing wet and dry biases compared to the parametrized convection simulation. When the analysis is performed at different basin scales, the precipitation bias decreases as the spatial scales increase for all the simulations; the lowest-resolution simulation has the smallest root mean squared error compared to CMORPH. In the simulations, a positive mean precipitation bias over China is primarily found to be due to too frequent precipitation for the parametrized convection simulation and too intense precipitation for the explicit convection simulation. The simulated diurnal cycle of precipitation is strongly affected by the representation of convection: parametrized convection produces a peak in precipitation too close to midday over land, whereas explicit convection produces a peak that is closer to the late afternoon peak seen in observations. At increasing spatial scale, the representation of the diurnal cycle in the explicit and hybrid convection simulations improves when compared to CMORPH; this is not true for any of the parametrized simulations. Some of the strengths and weaknesses of simulated precipitation in a high-resolution GCM are found: the diurnal cycle is improved at all spatial scales with convection parametrization disabled, the interaction of the flow with orography exacerbates existing biases for mean precipitation in the high-resolution simulations, and parametrized simulations produce similar diurnal cycles regardless of their resolution. The need for tuning the high-resolution simulations is made clear. Our approach for evaluating simulated precipitation across a range of scales is widely applicable to other GCMs.

Disentangling the effect of regional SST bias on the double-ITCZ problem

This study investigates the causes of the double intertropical convergence zone (ITCZ) bias by disentangling the individual contribution of regional sea surface temperature (SST) biases. We show that a previously suggested Southern Ocean warm bias effect in displacing the zonal-mean ITCZ southward is diminished by the southern midlatitude cold bias effect. The northern extratropical cold bias turns out to be most responsible for a southward-displaced zonal-mean precipitation, but the zonal-mean diagnostics poorly represent the spatial pattern of the tropical Pacific response. Examination of longitude-latitude structure indicates that the overall spatial pattern of tropical precipitation bias is largely shaped by the local SST bias. The southeastern tropical Pacific wet bias is driven by warm bias along the west coast of South America with negligible influence from the Southern Ocean warm bias. While our model experiments are idealized with ocean dynamics being absent, the results shed light on where preferential foci should be applied in model development to improve the certain features of tropical precipitation bias.

Evaluation of Asian summer precipitation in different configurations of a high-resolution GCM at a range of decision-relevant spatial scales

High-resolution general circulation models (GCMs) can provide new insights into the simulated distribution of global precipitation. We evaluate how summer precipitation is represented over Asia in global simulations with a grid length of 14 km. Three simulations were performed: one with a convection parametrization, one with convection represented explicitly by the model's dynamics, and a hybrid simulation with only shallow and mid-level convection parametrized. We evaluate the mean simulated precipitation and the diurnal cycle of the amount, frequency and intensity of the precipitation against satellite observations of precipitation from the Climate Prediction Center morphing method (CMORPH). We also compare the high-resolution simulations with coarser simulations that use parametrized convection. The simulated and observed precipitation is averaged over spatial scales defined by the hydrological catchment basins; these provide a natural spatial scale for performing decision-relevant analysis that is tied to the underlying regional physical geography. By selecting basins of different sizes, we evaluate the simulations as a function of the spatial scale. A new BAsin-Scale Model Assessment ToolkIt (BASMATI) is described, which facilitates this analysis. We find that there are strong wet biases (locally up to 72 mm day−1 at small spatial scales) in the mean precipitation over mountainous regions such as the Himalayas. The explicit convection simulation worsens existing wet and dry biases compared to the parametrized convection simulation. When the analysis is performed at different basin scales, the precipitation bias decreases as the spatial scales increase for all simulations; the lowest-resolution simulation has the smallest root mean squared error compared to CMORPH. In the simulations, a positive mean precipitation bias over China is primarily found to be due to too frequent precipitation for the parametrized convection simulation, and too intense precipitation for the explicit convection simulation. The simulated diurnal cycle of precipitation is strongly affected by the representation of convection: parametrized convection produces a peak in precipitation too close to midday over land, whereas explicit convection produces a peak that is closer to the late afternoon peak seen in observations. At increasing spatial scale, the representation of the diurnal cycle in the explicit and hybrid convection simulations improves when compared to CMORPH; this is not true for any of the parametrized simulations. Some of the strengths and weaknesses of simulated precipitation in a high-resolution GCM are found: the diurnal cycle is improved at all spatial scales with convection parametrization disabled; the interaction of the flow with orography exacerbates existing biases for mean precipitation in the high-resolution simulations; and parametrized simulations produce similar diurnal cycles regardless of their resolution. The need for tuning the high-resolution simulations is made clear. Our approach for evaluating simulated precipitation across a range of scales is widely applicable to other GCMs.

Temperature and precipitation verification over Pannonian Basin in EURO-CORDEX simulations during summer season

<p>In previous studies, it was noticed that many Regional Climate Models (RCMs) tend to overestimate mean near surface air temperature and underestimate precipitation in the Pannonian Basin during summer, leading to so-called summer drying problem [1]. Our intention for this study was to analyze temperature and precipitation biases in the state of the art EURO-CORDEX multi-model ensemble results in the summer season. Models’ results from the historical runs, and over time period 1971-2000, for temperature, precipitation and sea level pressure were verified against gridded E-OBS data set. In total there were 30 selected integrations, with different combinations of RCMs and Global Climate Models (GCMs). In order to assess the impact of the different lateral boundary conditions on the results from RCMs simulations, emphasizing the errors of the corresponding driving models used in 30 RCMs simulations, results from driving GCMs are also verified.</p><p>Verification results for selected time period was expressed in term of four verification scores: bias, root mean square error (RMSE), spatial correlation coefficient and standard deviations. Verification scores were evaluated within a sub-domain in the center of the region bounded by longitudes, 14E and 27E, and latitudes, 43.5N and 50N, in which topography elevation is below 200 m. This sub-domain was selected to eliminate the influence of results over the surrounding mountains on spatially averaged scores [2], because previous studies indicated a pronounced summer drying problem in low lying areas. Our analysis showed that 17 RCMs tend to overestimate the temperature, 8 RCMs tend to underestimate the temperature and 5 RCMs tend to estimate temperature around E-OBS gridded data set. On the other hand, most of the RCMs that overestimate the temperature, underestimate the precipitation. According to the results, temperature bias was in the range from -1.9°C to +4.4°C , while precipitation bias was in the range from 42% to -70%. For some models the positive temperature and negative precipitation bias were even more pronounced, leading to the conclusion, that the problem is still present in the majority of analyzed simulations. Analysis of the sea level pressure was conducted as an indirect indicator of errors in advection processes in RCMs, which was indicated, beside others, as a potential precursor of temperature and precipitation biases [3]. To better understand the sources and reasons for summer drying problem further research is needed.</p><p>[1] Kotlarski S. et al., (2014): Regional climate modelling on European scales: a joint standard evaluation of the EURO-CORDEX RCM ensemble. Geoscientific Model Development 7:1297–1333, doi: 10.5194/gmd-7-1297-2014</p><p>[2] Lazic I., Djurdjevic V., (2019): EURO-CORDEX regional climate models’ performances in representing temperature and precipitation over Pannonian Basin, Book of abstracts, 5th PannEx Workshop, 3-5 June 2019, Novi Sad, Serbia.</p><p>[3] Szépszó G., (2006): Adaptation of the REMO model at the Hungarian Meteorological Service (in Hungarian). Proceedings of the 31st Scientific Days for Meteorology, 125–135.</p><p><em>Keywords</em>: summer drying problem, verification, EURO-CORDEX, Pannonian Basin</p><p>Acknowledgement: This study was supported by the Serbian Ministry of Science and Education, under grant no. 176013.</p>

New Statistical Methods for Precipitation Bias Correction Applied to WRF Model Simulations in the Antisana Region, Ecuador

The Ecuadorian Andes are characterized by a complex spatiotemporal variability of precipitation. Global circulation models do not have sufficient horizontal resolution to realistically simulate the complex Andean climate and in situ meteorological data are sparse; thus, a high-resolution gridded precipitation product is needed for hydrological purposes. The region of interest is situated in the center of Ecuador and covers three climatic influences: the Amazon basin, the Andes, and the Pacific coast. Therefore, regional climate models are essential tools to simulate the local climate with high spatiotemporal resolution; this study is based on simulations from the Weather Research and Forecasting (WRF) Model. The WRF Model is able to reproduce a realistic precipitation variability in terms of the diurnal cycle and seasonal cycle compared to observations and satellite products; however, it generated some nonnegligible bias in the region of interest. We propose two new methods for precipitation bias correction of the WRF precipitation simulations based on in situ observations. One method consists of modeling the precipitation bias with a Gaussian process metamodel. The other method is a spatial adaptation of the cumulative distribution function transform approach, called CDF-t, based on Voronoï diagrams. The methods are compared in terms of precipitation occurrence and intensity criteria using a cross-validation leave-one-out framework. In terms of both criteria, the Gaussian process metamodel approach yields better results. However, in the upper parts of the Andes (>2000 m), the spatial CDF-t method seems to better preserve the spatial WRF physical patterns.