Using data from 33 models from the CMIP5 historical and AMIP5 simulations, we have carried out a systematic analysis of biases in total precipitation and its convective and large-scale components over the south Asian region. We have used 23 years (1983–2005) of data, and have computed model biases with respect to the PERSIANN-CDR precipitation (with convective/large-scale ratio derived from TRMM 3A12). A clustering algorithm was applied on the total, convective, and large-scale precipitation biases seen in CMIP5 models to group them based on the degree of similarity in the global bias patterns. Subsequently, AMIP5 models were analyzed to conclude if the biases were primarily due to the atmospheric component or due to the oceanic component of individual models. Our analysis shows that the set of individual models falling in a given group is somewhat sensitive to the variable (total/convective/large-scale precipitation) used for clustering. Over the south Asian region, some of the convective and large-scale precipitation biases are common across groups, emphasizing that although on a global scale the bias patterns may be sufficiently different to cluster the models into different groups, regionally, it may not be true. In general, models tend to overestimate the convective component and underestimate the large-scale component over the south Asian region, although with spatially varying magnitudes depending on the model group. We find that the convective precipitation biases are largely governed by the closure and trigger assumptions used in the convection parameterization schemes used in these models, and to a lesser extent on details of the individual cloud models. Using two different methods: (i) clustering, (ii) comparing the bias patterns of models from CMIP5 with their AMIP5 counterparts, we find that, in general, the atmospheric component (and not the oceanic component through biases in SSTs and atmosphere-ocean feedbacks) plays a major role in deciding the convective and large-scale precipitation biases. However, the oceanic component has been found important for one of the convective groups in deciding the convective precipitation biases (over the maritime continent).

We analyzed 113 years (1901–2013) of daily rainfall over India to investigate spatiotemporal variability of rainfall seasonality. Rainfall seasonality and mean annual rainfall were found to be high over the Western Ghats, central, and northeastern parts of India and over the Indo‐Gangetic plains, and low over northwest, southern, and northernmost parts of India. Significant decreasing trends in seasonality coupled with decreasing rainfall were found over parts of central India, the Indo‐Gangetic plains, and parts of Western Ghats. Trends in timing of peak rainfall indicate later occurrence in the season, especially over southern Indo‐Gangetic plains, by ~10–20 days per century. In addition, there is a general decrease in the wet‐season duration throughout India by ~10–20 days per century. El Niño–Southern Oscillation and Indian Ocean sea surface temperatures were found to strongly influence seasonality and rainfall over large parts of India. The changes to rainfall and its seasonality will have profound socioeconomic implications for India.

The effects of global warming and geoengineering on annual precipitation and its seasonality over different parts of the world are examined using the piControl, 4xCO 2 and G1 simulations from eight global climate models participating in the Geoengineering Model Intercomparison Project. Specifically, we have used relative entropy, seasonality index, duration of the peak rainy season and timing of the peak rainy season to investigate changes in precipitation characteristics under 4xCO 2 and G1 scenarios with reference to the piControl. In a 4xCO 2 world, precipitation is projected to increase over many parts of the globe, along with an increase in both the relative entropy and seasonality index. Further, in a 4xCO 2 world the increase in peak precipitation duration is found to be highest over the subpolar climatic region. However, over the tropical rain belt, the duration of the peak precipitation period is projected to decrease. Furthermore, there is a significant shift in the timing of the peak precipitation period by 15 days-2 months (forward) over many parts of the Northern Hemisphere except for over a few regions, such as North America and parts of Mediterranean countries, where a shift in the precipitation peak by 1-3 months (backward) is observed. However, solar geoengineering is found to significantly compensate many of the changes projected in a 4xCO 2 scenario. Solar geoengineering nullifies the precipitation increase to a large extent. Relative entropy and the seasonality index are almost restored back to that in the control simulations, although with small positive and negative deviations over different parts of the globe, thus, significantly nullifying the impact of 4xCO 2 . However, over some regions, such as northern parts of South America, the Arabian Sea and Southern Africa, geoengineering does not significantly nullify changes in the seasonality index seen in 4xCO 2 . Finally, solar geoengineering significantly compensates the changes in timing of the peak and duration of the peak precipitation seen in 4xCO 2 .

Using uncertainty quantification techniques, we carry out a sensitivity analysis of a large number (17) of parameters used in the NCAR CAM5 cloud parameterization schemes. The LLNL PSUADE software is used to identify the most sensitive parameters by performing sensitivity analysis. Using Morris One-At-a-Time (MOAT) method, we find that the simulations of global annual mean total precipitation, convective, large-scale precipitation, cloud fractions (total, low, mid, and high), shortwave cloud forcing, longwave cloud forcing, sensible heat flux, and latent heat flux are very sensitive to the threshold-relative-humidity-for-stratiform-low-clouds ($$rhminl)$$ r h m i n l ) and the auto-conversion-size-threshold-for-ice-to-snow $$\left( {dcs} \right).$$ dcs . The seasonal and regime specific dependence of some parameters in the simulation of precipitation is also found for the global monsoons and storm track regions. Through sensitivity analysis, we find that the Somali jet strength and the tropical easterly jet associated with the south Asian summer monsoon (SASM) show a systematic dependence on $$dcs$$ dcs and $$rhminl$$ rhminl . The timing of the withdrawal of SASM over India shows a monotonic increase (delayed withdrawal) with an increase in $$dcs$$ dcs . Overall, we find that $$rhminl$$ rhminl , $$dcs$$ dcs , $$ai,$$ a i , and $$as$$ as are the most sensitive cloud parameters and thus are of high priority in the model tuning process, in order to reduce uncertainty in the simulation of past, present, and future climate.

Uncertainty quantification (UQ) in weather and climate models is required to assess the sensitivity of their outputs to various parameterization schemes and thereby improve their consistency with observations. Herein, we present an efficient UQ and Bayesian inference for the cloud parameters of the NCAR Single Column Atmosphere Model (SCAM6) using surrogate models based on a polynomial chaos expansion. The use of a surrogate model enables to efficiently propagate uncertainties in parameters into uncertainties in model outputs. We investigated eight uncertain parameters: the auto-conversion size threshold for ice to snow (dcs), the fall speed parameter for stratiform cloud ice (ai), the fall speed parameter for stratiform snow (as), the fall speed parameter for cloud water (ac), the collection efficiency of aggregation ice (eii), the efficiency factor of the Bergeron effect (berg_eff), the threshold maximum relative humidity for ice clouds (rhmaxi), and the threshold minimum relative humidity for ice clouds (rhmini). We built two surrogate models using two non-intrusive methods: spectral projection (SP) and basis pursuit denoising (BPDN). Our results suggest that BPDN performs better than SP as it enables to filter out internal noise during the process of fitting the surrogate model. Five out of the eight parameters (namely dcs, ai, rhmaxi, rhmini, and eii) account for most of the variance in predicted climate variables (e.g., total precipitation, cloud distribution, shortwave and longwave cloud radiative effect, ice, and liquid water path). A first-order sensitivity analysis reveals that dcs contributes ~40–80% of the total variance of the climate variables, ai around 15–30%, and rhmaxi, rhmini, and eii around 5–15%. The second- and higher-order effects contribute ~7 and 20%, respectively. The sensitivity of the model to these parameters was further explored using response curves. A Markov chain Monte Carlo (MCMC) sampling algorithm was also implemented for the Bayesian inference of dcs, ai, as, rhmini, and berg_eff using cloud distribution data collected at the Southern Great Plains (USA). The inferred parameters suggest improvements in the global Climate Earth System Model (CESM2) simulations of the tropics and sub-tropics.