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In this paper, we introduce a novel strategy to robustly diagnose the onset and demise of the rainy season using daily observed rainfall over seven specific regions across Australia, as demarcated by the Natural Resource Management (NRM) agency of Australia. The methodology lies in developing an ensemble spread of the diagnosed onset and demise from randomly perturbing the observed daily time series of rainfall at synoptic scales to obtain a measure of the uncertainty of the diagnosis. Our results indicate that the spread of the ensemble in the diagnosis of the onset and demise dates of the rainy season is higher in the subtropical region than the tropical region. Secular change of earlier onset, later demise, longer length, and wetter season are also identified in many of these regions. The influence of the PDO at decadal scale, ENSO and Indian Ocean dipole at interannual scale, and MJO at intraseasonal scale also reveals significant influence on the evolution of the rainy season over these regions in Australia. Most important, the covariability of the onset date with the length of the season and seasonal rainfall anomaly of the season is highlighted as a valuable relationship that can be exploited for real-time monitoring and providing an outlook of the forthcoming rainy season, which could serve some of the NRM regions. Significance Statement: We document the rainfall variability during the rainy season over tropical, subtropical, and semiarid regions of Australia and relate them to modes of climate variability spanning from intraseasonal to secular time scales. The study highlights the varied influence of the modes of climate variability on various aspects of the evolution of the rainy season, such as its onset and demise dates and the seasonal rainfall anomaly over these Australian regions. The study uses 114 years of data and shows that the variations in the length of the rainy season and its seasonal rainfall anomaly are strongly dictated by variations of rainy-season onset date. This provides a quick seasonal outlook of the forthcoming rainy season by just monitoring the onset-date evolution in these regions. [ABSTRACT FROM AUTHOR]

Variability in soil moisture has implications for regional terrestrial environments under a warming climate. This paper focuses on the spatiotemporal variability in the intra-annual persistence of soil moisture in China using the fifthgeneration reanalysis dataset by the European Centre for Medium-Range Weather Forecasts for the period 1979-2018. The results show that in China, the mean intra-annual persistence in the humid to arid zones increased from 60 to 115 days in the lower layer but decreased from 19 to 13 days and from 25 to 14 days in the upper and root layers, respectively. However, these changes were strongly attenuated in extremely dry and wet regions due to the scarcity of soil moisture anomalies. Large changes in persistence occurred in the lower soil layer in dryland areas, with a mean difference of up to 40 days between the 2010s and the 1980s. Overall increasing trends dominated the large-scale spatial features, despite regional decreases in the eastern arid zone and the North and Northeast China plains. In the root layer, the two plains experienced an expanded decrease while on the Tibetan Plateau it was dominated by decadal variability. These contrasting changes between the lower and root layers along the periphery of the transition zone was a reflection of the enhanced soil hydrological cycle in the root layer. The enhanced persistence in drylands lower layer is an indication of the intensified impacts of soil moisture anomalies (e.g., droughts) on terrestrial water cycle. These findings may help the understanding of climate change impacts on terrestrial environments. [ABSTRACT FROM AUTHOR]

The systematic bias of the estimated average temperature using daily Tmax and Tmin records relative to the standard average temperature of four time-equidistant observations and its effect on the estimated trend of long-term temperature change have not been well understood. This paper attempts to evaluate the systematic bias across mainland China using the daily data of national observational stations. The results revealed that the positive bias of annual mean temperature was large, reaching 0.58°C nationally on average; regional average bias was lowest in the northwest arid region and highest in the Qinghai–Tibetan Plateau; the bias was low in spring and summer and high in autumn and winter, reaching its lowest point in mid- and late May and highest point in early November. Furthermore, the bias showed a significant upward trend in the past 50 years, with a rising rate of 0.021°C (10 yr)−1, accounting for about 12% of the overall warming as estimated from the data of the observational network; the largest positive trend bias was found in the northwest arid region, while the east monsoon region experienced the smallest change; the most remarkable increase of the bias occurred after early 1990s. These results indicate that the customarily applied method to calculate daily and monthly mean temperature using Tmax and Tmin significantly overestimates the climatological mean and the long-term trend of surface air temperature in mainland China. [ABSTRACT FROM AUTHOR]

Scarcity or unavailability of precipitation observation creates difficulties in hydrologic modeling of mountainous sections of the arid region of northwest China (34°-50°N, 72°-107°E). Tropical Rainfall Measuring Mission (TRMM) precipitation products may be a potential substitute, but they should be evaluated and corrected with ground observation data before application. In this paper, two TRMM Multisatellite Precipitation Analysis (TMPA) precipitation products were evaluated by gauge observations, using indices such as frequency bias index, probability of detection, false alarm ratio, relative mean bias, Nash-Sutcliffe efficiency, and correlation coefficient. Terrain variables were extracted from a digital elevation model, and their rotated principal components were determined to establish a stepwise regression model to adjust TMPA precipitation. Additionally, a back-propagation (BP) neural network was established to correct TMPA precipitation. The results showed that TMPA had an unsatisfactory detection ability in the study area for both precipitation occurrence and amount. TMPA precipitation corrected by a stepwise regression method showed some improvement, but only the results for TRMM 3B43 on a subregion scale were acceptable. The BP neural network method showed better results than the stepwise regression method, and both TRMM 3B42 and TRMM 3B43 corrected by the former method on a subregion scale could be acceptable. Both methods were spatial-scale dependent and showed better results on a subregion scale than on a larger scale. [ABSTRACT FROM AUTHOR]

Hydrological processes in most semiarid regions on Earth have been changing under the impacts of climate change, human activities, or combinations of the two. This paper first presents a trend analysis of the spatiotemporal changes in water resources and then diagnoses their underlying atmospheric and socio-economic causes over 10 catchments in the Laoha basin, a typical semiarid zone of northeast China. The impacts of climate variability and human activities on streamflow change were quantitatively evaluated by the VIC (Variable Infiltration Capacity) model. First, results indicate that six out of the 10 studied catch-ments have statistically significant downward trends in annual streamflow; however, there is no significant change of annual precipitation for all catchments. Two abrupt changes of annual streamflow at 1979 and 1998 are identified for the four largest catchments. Second, the Laoha basin generally experienced three evident dry-wet pattern switches during the past 50 years. Furthermore, this basin is currently suffering from unprecedented water shortages. Large-scale climate variability has affected the local natural hy-drologie system. Third, quantitative evaluation shows human activities were the main driving factors for the streamflow reduction with contributions of approximately 90% for the whole basin. A significant in-crease in irrigated area, which inevitably resulted in tremendous agricultural water consumption, is the foremost culprit contributing to the dramatic runoff reduction, especially at midstream and downstream of the Laoha basin. This study is expected to enable policymakers and stakeholders to make well-informed, short-term practice decisions and better plan long-term water resource and ecoenvironment management strategies. [ABSTRACT FROM AUTHOR]

The majority of the world's population growth to 2050 is projected to occur in the tropics. Hence, there is a serious need for robust methods for undertaking water resource assessments to underpin the sustainable management of water in tropical regions. This paper describes the largest and most comprehensive assessment of the future impacts of runoff undertaken in a tropical region using conceptual rainfall-runoff models (RRMs). Five conceptual RRMs were calibrated using data from 115 streamflow gauging stations, and model parameters were regionalized using a combination of spatial proximity and catchment similarity. Future rainfall and evapotranspiration projections (denoted here as GCMES) were transformed to catchment-scale variables by empirically scaling (ES) the historical climate series, informed by 15 global climate models (GCMs), to reflect a 1°C increase in global average surface air temperature. Using the best-performing RRM ensemble, approximately half the GCMES used resulted in a spatially averaged increase in mean annual runoff (by up to 29%%) and half resulted in a decrease (by up to 26%%). However, ~70%% of the GCMES resulted in a difference of within ±5%% of the historical rainfall (1930-2007). The range in modeled impact on runoff, as estimated by five RRMs (for individual GCMES), was compared to the range in modeled runoff using 15 GCMES (for individual RRMs). For mid- to high runoff metrics, better predictions will come from improved GCMES projections. A new finding of this study is that in the wet-dry tropics, for extremely large runoff events and low flows, improvements are needed in both GCMES and rainfall-runoff modeling. [ABSTRACT FROM AUTHOR]

Parameterization of turbulent flux from bare-soil and undercanopy surfaces is imperative for modeling land–atmosphere interactions in arid and semiarid regions, where flux from the ground is dominant or comparable to canopy-sourced flux. This paper presents the major characteristics of turbulent flux transfers over seven bare-soil surfaces. These sites are located in arid, semiarid, and semihumid regions in Asia and represent a variety of conditions for aerodynamic roughness length (z0m; from <1 to 10 mm) and sensible heat flux (from -50 to 400 W m-2). For each site, parameter kB-1 [=ln(z0m/z0h), where z0h is the thermal roughness length] exhibits clear diurnal variations with higher values during the day and lower values at night. Mean values of z0h for the individual sites do not change significantly with z0m, resulting in kB-1 increasing with z0m, and thus the momentum transfer coefficient increases faster than the heat transfer coefficient with z0m. The term kB-1 often becomes negative at night for relatively smooth surfaces (z0m ∼ 1 mm), indicating that the widely accepted excess resistance for heat transfer can be negative, which cannot be explained by current theories for aerodynamically rough surfaces. Last, several kB-1 schemes are evaluated using the same datasets. The results indicate that a scheme that can reproduce the diurnal variation of kB-1 generally performs better than schemes that cannot. [ABSTRACT FROM AUTHOR]

Detailed information on the spatial and temporal characteristics of precipitation intensity from the mountainous region of northwest Mexico has, until recently, been lacking. As part of the 2004 North American Monsoon Experiment (NAME) enhanced observing period (EOP) surface rain gauge networks along with weather radar and orbiting satellites were employed to observe precipitation in a manner heretofore unprecedented for this semiarid region. The NAME Event Rain gauge Network (NERN), which has been in operation since 2002, contributed to this effort. Building on previous work, this paper presents analyses on the spatial and temporal characteristics of precipitation intensity as observed by NERN gauges. Analyses from the 2004 EOP are compared with the 2002–04 period and with long-term gauge observations. It was found that total precipitation from July to August of 2004 was similar in spatial extent and magnitude to the long-term average, though substantially wetter than 2003. Statistical analyses of precipitation intensity data from the NERN reveal that large precipitation events at hourly and daily time scales are restricted to coastal and low-elevation areas west of the Sierra Madre Occidental. At 10-min time scales, maximum intensity values equal to those at low elevations could be observed at higher elevations though they were comparatively infrequent. It is also shown that the inclusion of NERN observations in existing operational analyses helps to correct significant biases, which, on the seasonal time scale, are of similar magnitude as the interannual variability in precipitation in key headwater regions of northwest Mexico. [ABSTRACT FROM AUTHOR]

Regional precipitation recycling may constitute a feedback mechanism affecting soil moisture memory and the persistence of anomalously dry or wet states. Bulk methods, which estimate recycling based on time-averaged variables, have been applied on a global basis, but these methods may underestimate recycling by neglecting the effects of correlated transients. A back-trajectory method identifies the evaporative sources of vapor contributing to precipitation events by tracing air motion backward in time through the analysis grid of a data-assimilating numerical model. The back-trajectory method has been applied to several large regions; in this paper it is extended to all global land areas for 1979–2003. Meteorological information (wind vectors, humidity, surface pressure, and evaporation) are taken from the NCEP–Department of Energy (DOE) reanalysis, and a hybrid 3-hourly precipitation dataset is produced to establish the termini of the trajectories. The effect of grid size on the recycling fraction is removed using an empirical power-law relationship; this allows comparison among any land areas on a latitude–longitude grid. Recycling ratios are computed on a monthly basis for a 25-yr period. The annual and seasonal averages are consistent with previous estimates in terms of spatial patterns, but the trajectory method generally gives higher estimates of recycling than a bulk method, using compatible spatial scales. High northern latitude regions show the largest amplitude in the annual cycle of recycling, with maxima in late spring/early summer. Amplitudes in arid regions are small in absolute terms, but large relative to their mean values. Regions with strong interannual variability in recycling do not correspond directly to regions with strong intra-annual variability. The average recycling ratio at a spatial scale of 105 km2 for all land areas of the globe is 4.5%; on a global basis, recycling shows a weak positive trend over the 25 yr, driven largely by increases at high northern latitudes. [ABSTRACT FROM AUTHOR]

Because land surface emissivity (ℇ) has not been reliably measured, global climate model (GCM) land surface schemes conventionally set this parameter as simply constant, for example, 1 as in the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) model, and 0.96 for bare soil as in the National Center for Atmospheric Research (NCAR) Community Land Model version 2 (CLM2). This is the so-called constant-emissivity assumption. Accurate broadband emissivity data are needed as model inputs to better simulate the land surface climate. It is demonstrated in this paper that the assumption of the constant emissivity induces errors in modeling the surface energy budget, especially over large arid and semiarid areas where ℇ is far smaller than unity. One feasible solution to this problem is to apply the satellite-based broadband emissivity into land surface models. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument has routinely measured spectral emissivities (ℇλ) in six thermal infrared bands. The empirical regression equations have been developed in this study to convert these spectral emissivities to broadband emissivity (ℇ) required by land surface models. The observed emissivity data show strong seasonality and land-cover dependence. Specifically, emissivity depends on surface-cover type, soil moisture content, soil organic composition, vegetation density, and structure. For example, broadband ℇ is usually around 0.96–0.98 for densely vegetated areas [(leaf area index) LAI > 2], but it can be lower than 0.90 for bare soils (e.g., desert). To examine the impact of variable surface broadband emissivity, sensitivity studies were conducted using offline CLM2 and coupled NCAR Community Atmosphere Models, CAM2–CLM2. These sensitivity studies illustrate that large impacts of surface ℇ occur over deserts, with changes up to 1°–2°C in ground temperature, surface skin temperature, and 2-m surface air temperature, as well as evident changes in sensible and latent heat fluxes. [ABSTRACT FROM AUTHOR]

The area–time integral (ATI) method has previously been successfully used to estimate the area-averaged rain-rate distribution and the rainfall volume over an area from radar or from satellite infrared (IR) data. In most cases, the method was implemented over regions or test areas with an assumed homogeneous climatic character, that is, without a strong spatial variation of the rain regime throughout the test area. In the present paper, the behavior of the ATI method is discussed for a test area displaying two strong gradients of the cumulative annual rainfall: one meridional, at the transition between regions having, respectively, a desertic and an equatorial climate and the other zonal, at the transition between land and sea. The studied area is divided into four subtest areas (north, south, land, and sea) over which the ATI computation is applied separately. The linear coefficient relating the radar-observed area-averaged rain rate and the fractional area where the rain is higher than a threshold calculated over the four subtest areas is found to be almost constant, in agreement with the ergodic character of the rain-rate distribution observed in this region. Similarly, the linear coefficient relating the rain volume over the subtest areas to the IR satellite-derived ATI, a parameter analogous to the Geostationary Operational Environmental Satellite (GOES) Precipitation Index (GPI), is found to be very steady, with a mean value of 3.02 mm h-1 and a coefficient of variation of only 8%. These coefficients, as well as the underlying dynamic and microphysical processes, do not seem significantly influenced by the climatic character, even at a short space scale, in the studied area. The ratio of radar rain areas to cloud areas is, notably, almost constant. For a brightness temperature of 235 K, the ratio of the cloud area to rain area is around 1.68. [ABSTRACT FROM AUTHOR]

This paper examines observational evidence of a positive feedback between the land surface and rainfall in semiarid conditions. The novelty of the work lies in the length scale of study, investigating interactions between soil moisture patterns and deep convection at scales of less than 20 km. The feedback mechanism was proposed in a previous study to explain the development of an anomalous rainfall gradient in the West African Sahel. The aim here is to assess whether such rainfall persistence occurs elsewhere in the region. Convective-scale rainfall patterns are examined using two years of observations from a dense rain gauge network in southwest Niger. Rainfall differences are analyzed between neighboring gauges separated by 7.5-15 km. Under certain surface conditions, a positive correlation between daily and antecedent rainfall differences is established. These circumstances arise when previous storm patterns have modified local evaporation rates. Rainfall gradients in subsequent events tend to persist, reinforcing soil moisture patterns. The effect appears to be most pronounced in mature, large-scale storms. The widespread occurrence of persistence in the dataset provides strong observational evidence of a surface feedback mechanism, with surface-induced low-level humidity anomalies locally enhancing convection in passing storms. Several rainfall patterns that persist for a month are identified. These patterns are linked to surface processes and the frequency of storm passage. [ABSTRACT FROM AUTHOR]