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This study examines the effects of five cumulus closure assumptions on simulations of summer precipitation in the continental U.S. by utilizing an ensemble cumulus parameterization (ECP) that incorporates multiple alternate closure schemes into a single cloud model formulation. Results demonstrate that closure algorithms significantly affect the summer mean, daily frequency and intensity, and diurnal variation of precipitation, with strong regional dependence. Overall, the vertical velocity (W) closure produces the smallest summer mean biases, while the moisture convergence (MC) closure most realistically reproduces daily variability. Both closures have advantages over others in simulating U.S. daily rainfall frequency distribution, though both slightly overestimate intense rain events. The MC closure is superior at capturing summer rainfall amount, daily variability, and heavy rainfall frequency over the Central U.S., but systematically produces wet biases over the North American Monsoon (NAM) region and Southeast U.S., which can be reduced by using the W closure. The instability tendency (TD) and the total instability adjustment (KF) closures are better at capturing observed diurnal signals over the Central U.S. and the NAM, respectively. The results reasonably explain the systematic behaviors of several major cumulus parameterizations. A preliminary experiment combining two optimal closures (averaged moisture convergence and vertical velocity) in the ECP scheme significantly reduced the wet (dry) biases over the Southeast U.S. in the summer of 1993 (2003), and greatly improved daily rainfall correlations over the NAM. Further improved model simulation skills may be achieved in the future if optimal closures and their appropriate weights can be derived at different time scales based on specific climate regimes.

This study provides an overview of the state of the art of modeling SST teleconnections to Africa and begins to investigate the sources of error. Data are obtained from the Coupled Model Intercomparison Project (CMIP) archives, phases 3 and 5 (CMIP3 and CMIP5), using the “20C3M” and “historical” coupled model experiments. A systematic approach is adopted, with the scope narrowed to six large-scale regions of sub-Saharan Africa within which seasonal rainfall anomalies are reasonably coherent, along with six SST modes known to affect these regions. No significant nonstationarity of the strength of these 6 × 6 teleconnections is found in observations. The capability of models to represent each teleconnection is then assessed (whereby half the teleconnections have observed SST–rainfall correlations that differ significantly from zero). A few of these teleconnections are found to be relatively easy to model, while a few more pose substantial challenges to models and many others exhibit a wide variety of model skill. Furthermore, some models perform consistently better than others, with the best able to at least adequately simulate 80%–85% of the 36 teleconnections. No improvement is found between CMIP3 and CMIP5. Analysis of atmosphere-only simulations suggests that the coupled model teleconnection errors may arise primarily from errors in their SST climatology and variability, although errors in the atmospheric component of teleconnections also play a role. Last, no straight forward relationship is found between the quality of a model’s teleconnection to Africa and its SST or rainfall biases or its resolution. Perhaps not surprisingly, the causes of these errors are complex, and will require considerable further investigation.

In this study, we examined the performance of 33 CMIP5 models to simulate summer monsoon rainfall over India for the century-long period 1901–2005. If the standardized rainfall anomaly from the long-term mean is less than –1, it is defined as a deficit summer monsoon season; moreover, for comprehensive analysis, we used moderate and severe deficit summer monsoons. Models are in three categories to capture the annual cycle of rainfall: (i) correct phase and amplitude (peak), (ii) phase with lower amplitude, and (iii) both failed. Many models are unable to mimic the link between subseasonal and seasonal rainfall, which is maximum for the July–August (JA) and June through September (JJAS) rainfall. Except for three models which overestimate the mean intensity of seasonal deficit monsoons, 24 models simulate the composite geographical pattern pretty well for moderate deficit monsoons. Twenty-five models are well represented/simulated in the category of extreme deficit monsoons. The MME simulates extremely dry deficit monsoons over India, with the out-of-phase feature of India's easternmost regions effectively captured. As two potential forcings of Indian Summer Monsoon Rainfall (ISMR), we examine El-Niño Southern Oscillation (ENSO) and Eurasian Snow. A large majority of deficit monsoons are attributed to factors other than El-Niño and Eurasian Snow in all four categories of model simulations. Models that can accurately recreate the distinguishing properties of deficit monsoons could be used to project future changes in the frequency, intensity, and spatial distribution of rainfall associated with extreme deficits. Research highlights In CMIP5, 10 models simulate the annual cycle of rainfall across India reasonably well, while 18 models simulate a dry bias. Subseasonal rainfall correlations (JJAS-JJ, JJAS-JA, and JJAS-AS) depict majority of the models simulates correlations of JJ rainfall with seasonal JJAS rainfall against the observed whereas, observations demonstrate JA rainfall is outstandingly related with the seasonal periodic rainfall. With the exception of three models that overestimate the mean intensity of seasonal deficit monsoons, 24 models for moderate deficit monsoons closely resemble the composite spatial pattern. The MME simulates extremely powerful deficit monsoons over India, with the out-of-phase feature of India's easternmost regions effectively captured. For the Indian Territory, CMIP5 models can replicate less-frequent but more-intense deficit monsoons.

In flood warning systems, numerical weather predictions (NWP) are important complementary tools as they increase the forecast lead times required to provide timely warnings. However, these predictions may include non-predictable uncertainties that decrease the correlation with gauge observations and ascend the number of missed or false warnings. Investigation of the potential of such products that are generated one-day apart can reveal the success of lead times in hydrological applications. In this study, two periodically generated products of the Weather Research and Forecasting (WRF) model are assessed by considering 15 flood events experienced in the southwest Mediterranean region of Turkey with a network of 26 rain gauges. The general results show that the rainfall distribution of both WRF datasets is found to be similar with slight differences in magnitude. However, the rainfall product generated earlier shows greater agreement in the observational data for 1-hr interval time but the latter one shows less bias and less cumulative error as interval time increases. In season- and elevation-based analyses, both WRF datasets show the highest correlation value in the winter season. With this study, it is revealed that the rainfall correlations results are superior in the former data whereas the rainfall accumulations are better represented with the posterior data.

The identification of extratropical fronts in reanalyses and climate models is an important climate diagnostic that aids dynamical understanding and model verification. This study compares six frontal identification methods that are applied to June and July reanalysis data over the Central Wheatbelt of southwest Western Australia for 1979–2006. Much of the winter rainfall over this region originates from frontal systems. Five of the methods use automated algorithms. These make use of different approaches, based on shifts in 850-hPa winds (WND), gradients of temperature (TGR) and wet-bulb potential temperature (WPT), pattern matching (PMM), and a self-organizing map (SOM). The sixth method was a manual synoptic technique (MAN). On average, about 50% of rain days were associated with fronts in most schemes (although methods PMM and SOM exhibited a lower percentage). On a daily basis, most methods identify the same systems more than 50% of the time, and over the 28-yr period the seasonal time series correlate strongly. The association with rainfall is less clear. The WND time series of seasonal frontal counts correlate significantly with Central Wheatbelt rainfall. All automated methods identify fronts on some days that are classified as cutoff lows in the manual analysis, which will impact rainfall correlations. The front numbers identified on all days by the automated methods decline from 1979 to 2006 (but only the TGR and WPT trends were significant at the 10% level). The results here highlight that automated techniques have value in understanding frontal behavior and can be used to identify the changes in the frequency of frontal systems through time.

Savanna landscapes are globally extensive and highly sensitive to climate change, yet the physical processes and climate phenomena which affect them remain poorly understood and therefore poorly represented in climate models. Both human populations and natural ecosystems are highly susceptible to precipitation variation in these regions due to the effects on water and food availability and atmosphere–biosphere energy fluxes. Here we quantify the relationship between climate phenomena and historical rainfall variability in Australian savannas and, in particular, how these relationships changed across a strong rainfall gradient, namely the North Australian Tropical Transect (NATT). Climate phenomena were described by 16 relevant climate indices and correlated against precipitation from 1900 to 2010 to determine the relative importance of each climate index on seasonal, annual and decadal timescales. Precipitation trends, climate index trends and wet season characteristics have also been investigated using linear statistical methods. In general, climate index–rainfall correlations were stronger in the north of the NATT where annual rainfall variability was lower and a high proportion of rainfall fell during the wet season. This is consistent with a decreased influence of the Indian–Australian monsoon from the north to the south. Seasonal variation was most strongly correlated with the Australian Monsoon Index, whereas yearly variability was related to a greater number of climate indices, predominately the Tasman Sea and Indonesian sea surface temperature indices (both of which experienced a linear increase over the duration of the study) and the El Niño–Southern Oscillation indices. These findings highlight the importance of understanding the climatic processes driving variability and, subsequently, the importance of understanding the relationships between rainfall and climatic phenomena in the Northern Territory in order to project future rainfall patterns in the region.

Many climate models have dry biases in tropical monsoon regions, but it is less clear how these errors can affect these model-simulated tropical–extratropical interactions and rainfall teleconnections. In this study, we evaluate the Australia and Asian (A–A) monsoon rainfall in two versions [Global Atmosphere version 6 (GA6), and version 7 (GA7)] of the UK Met Office Unified Model (UM) with uncoupled atmosphere-only simulations using two horizontal resolutions (N96 ~ 135 km and N216 ~ 60 km). Although UM can capture broad features of rainfall seasonal variations in the monsoon region, there are significant model errors in the locations of monsoon rainfall, its magnitudes and time evolutions, especially a climatological rainfall dry bias over the Indian subcontinent. This dry bias is progressively reduced by increased model resolutions from N96 to N216 and improved model physics from GA6 to GA7. Area-averaged peak monsoon rainfall over the Indian subcontinent (70°–90°E, 5°–25°N) increases by ~ 70% from 3 mm day −1 in GA6N216 to 5 mm day −1 in GA7N216, and rainfall dry bias averaged over a large tropical Asian monsoon domain is reduced by 2/3. In the uncoupled atmosphere-only experiment, the model rainfall is excessively sensitive to its underlying SST conditions, with positive local SST-rainfall correlations occurring over whole tropical oceans, while observations show negative correlations in regions dominated by atmospheric influence although such model performance is improved in its fully coupled runs. Most significant improvement of monsoon rainfall and ENSO (El Nino Southern Oscillations) relationship in GA7 occurred in the Australian continent, with GA7N216 better capturing the observed relationship than GA6N216. Although GA7N216 still suffers a dry bias in the Indian monsoon region, the increased monsoon rainfall substantially increases atmospheric diabatic heating over the region in the middle-upper troposphere, leading to more realistic extratropical circulation Gill-type responses to the heating. This results in improved monsoon-desert rainfall teleconnections, more realistic linkage between tropical Asian monsoon with its subtropical East Asian component, and its interhemispheric influence in Southern Hemisphere. Such results are further confirmed by the model nudging experiments in which the atmospheric wind and temperature are nudged towards observations over the Indian monsoon domain. Our study clearly demonstrates that uncertainty associated with monsoon simulations needs to be considered in future climate projections even outside the monsoon domain.

The Mediterranean California Border Region (MCBR) rainfall's relationship with El Niño‐Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) is reexamined for the period 1951–2014. When stratifying data by ENSO events we found that strong events of either sign yield the highest ENSO–rainfall correlation; but when the stratification was done by rainfall the wet seasons yield the highest ENSO–rainfall correlation. Most strong ENSO events have the same sign as PDO; but the ENSO–rainfall correlation for all ENSO–PDO same‐sign events is almost undistinguishable from the full‐record's correlation. Timewise stratification shows that 30‐year climatological values (MCBR precipitation, PDO and ENSO) and ENSO–rainfall correlations have decreased in recent years.

The proposed study verified the possible influences of sea surface temperature (SST) anomalies in the equatorial Pacific and the tropical Atlantic on the rainfall in the southern region of Brazil. The rainfall stations used have monthly data for the period from 1977 to 2015 and are distributed throughout that region. Monthly TSM anomalies in the Niño 3.4 and Niño 1 + 2 areas, the Southern Oscillation Index and the Monthly Tropical South Atlantic Temperature Index were also used, from the database provided by the Climate Prediction Centre. The results show the association of precipitation in the South region with variations in sea surface temperature in the Tropical Pacific Ocean and, to a lesser extent, with sea surface temperatures in the Tropical South Atlantic Ocean.

The El Niño‐Southern Oscillation (ENSO)–rainfall relationship in the Mediterranean California border region (MCBR) changed recently: considering 30‐year running periods (1951–1952 to 1980–1981, until 1986–1987 to 2015–2016) in four stations, ENSO–rainfall correlations (r), and mean precipitation (P) decreased almost simultaneously. Moreover, r and P appear related throughout the entire record: at the beginning and recently both seem lower than at an intermediate stage. Similar results are found for gridded precipitation data in the vicinity of the MCBR, suggesting that periods of recurrent dry seasons have been less related to ENSO than periods of numerous rainy seasons in this region during the last 65 years.