June July August Research Articles

Diffusion Characteristics of Swells in the North Indian Ocean

Research on the diffusion characteristics of swells contributes positively to wave energy forecasting, swell monitoring, and early warning. In this work, the South Indian Ocean westerly index (SIWI) and Indian Ocean swell diffusion effect index (IOSDEI) are defined on the basis of the 45-year (September 1957–August 2002) ERA-40 wave reanalysis data from the European Centre for Medium-Range Weather Forecasts (ECMWF) to analyze the impact of the South Indian Ocean westerlies on the propagation of swell acreage. The following results were obtained: 1) The South Indian Ocean swell mainly propagates from southwest to northeast. The swell also spreads to the Arabian Sea upon reaching low-latitude waters. The 2.0-meter contour of the swell can reach northward to Sri Lankan waters. 2) The size of the IOSDEI is determined by the SIWI strength. The IOSDEI requires approximately 2–3.5 days to fully respond to the SIWI. The correlations between SIWI and IOSDEI show obvious seasonal differences, with the highest correlations found in December–January–February (DJF) and the lowest correlations observed in June–July–August (JJA). 3) The SIWI and IOSDEI have a common period of approximately 1 week in JJA and DJF. The SIWI leads by approximately 2–3 days in this common period.

The diurnal cycle of the clouds extending above the tropical tropopause observed by spaceborne lidar

Abstract. The presence of clouds above the tropopause over tropical convection centers has so far been documented by spaceborne instruments that are either sun-synchronous or insensitive to thin cloud layers. Here we document, for the first time through direct observation by spaceborne lidar, how the tropical cloud fraction evolves above the tropopause throughout the day. After confirming previous studies that found such clouds most frequently above convection centers, we show that stratospheric clouds and their vertical extent above the tropopause follow a diurnal rhythm linked to convective activity. The diurnal cycle of the stratospheric clouds displays two maxima: one in the early night (19:00–20:00 LT) and a later one (00:00–01:00 LT). Stratospheric clouds extend up to 0.5–1 km above the tropopause during nighttime, when they are the most frequent. The frequency and the vertical extent of stratospheric clouds is very limited during daytime, and when present they are found very close to the tropopause. Results are similar over the major convection centers (Africa, South America and the Warm Pool), with more clouds above land in DJF (December–January–February) and less above the ocean and in JJA (June–July–August).

Investigating ENSO and its teleconnections under climate change in an ensemble view – a new perspective

Abstract. The changes in the El Niño–Southern Oscillation (ENSO) phenomenon and its precipitation-related teleconnections over the globe under climate change are investigated in the Community Earth System Model Large Ensemble from 1950 to 2100. For the investigation, a recently developed ensemble-based method, the snapshot empirical orthogonal function (SEOF) analysis, is used. The instantaneous ENSO pattern is defined as the leading mode of the SEOF analysis carried out at a given time instant over the ensemble. The corresponding principal components (PC1s) characterize the ENSO phases. By considering sea surface temperature (SST) regression maps, we find that the largest changes in the typical amplitude of SST fluctuations occur in the June–July–August–September (JJAS) season, in the Niño3–Niño3.4 (5∘ N–5∘ S, 170–90∘ W; NOAA Climate Prediction Center) region, and the western part of the Pacific Ocean; however, the increase is also considerable along the Equator in December–January–February (DJF). The Niño3 amplitude also shows an increase of about 20 % and 10 % in JJAS and DJF, respectively. The strength of the precipitation-related teleconnections of the ENSO is found to be nonstationary, as well. For example, the anticorrelation with precipitation in Australia in JJAS and the positive correlation in central and northern Africa in DJF are predicted to be more pronounced by the end of the 21th century. Half-year-lagged correlations, aiming to predict precipitation conditions from ENSO phases, are also studied. The Australian and Indonesian precipitation and that of the eastern part of Africa in both JJAS and DJF seem to be well predictable based on the ENSO phase, while the southern Indian precipitation relates to the half-year previous ENSO phase only in DJF. The strength of these connections increases, especially from the African region to the Arabian Peninsula.

Early-Holocene simulations using different forcings and resolutions in AWI-ESM

The earliest part of the Holocene, from 11.5k to 7k (k = 1000 years before present), is a critical transition period between the relatively cold last deglaciation and the warm middle Holocene. It is marked by more pronounced seasonality and reduced greenhouse gases (GHGs) than the present state, as well as by the presence of the Laurentide Ice Sheet (LIS) and glacial meltwater perturbation. This paper performs experiments under pre-industrial and different early-Holocene regimes with AWI-ESM (Alfred Wegener Institute–Earth System Model), a state-of-the-art climate model with unstructured mesh and varying resolutions, to examine the sensitivity of the simulated Atlantic meridional overturning circulation (AMOC) to early-Holocene insolation, GHGs, topography (including properties of the ice sheet), and glacial meltwater perturbation. In the experiments with early-Holocene Earth orbital parameters and GHGs applied, the AWI-ESM simulation shows a JJA (June–July–August) warming and DJF (December–January–February) cooling over the mid and high latitudes compared with pre-industrial conditions, with amplification over the continents. The presence of the LIS leads to an additional regional cooling over the North America. We also simulate the meltwater event around 8.2k. Big discrepancies are found in the oceanic responses to different locations and magnitudes of freshwater discharge. Our experiments, which compare the effects of freshwater release evenly across the Labrador Sea to a more precise injection along the western boundary of the North Atlantic (the coastal region of LIS), show significant differences in the ocean circulation response, as the former produces a major decline of the AMOC and the latter yields no obvious effect on the strength of the thermohaline circulation. Furthermore, proglacial drainage of Lakes Agassiz and Ojibway leads to a fast spin-down of the AMOC, followed, however, by a gradual recovery. Most hosing experiments lead to a warming over the Nordic Sea and Barents Sea of varying magnitudes, because of an enhanced inflow from lower latitudes and a northward displacement of the North Atlantic deep convection. These processes exist in both of our high- and low-resolution experiments, but with some local discrepancies such as (1) the hosing-induced subpolar warming is much less pronounced in the high-resolution simulations; (2) LIS coastal melting in the high-resolution model leads to a slight decrease in the AMOC; and (3) the convection formation site in the low- and high-resolution experiments differs, in the former mainly over northeastern North Atlantic Ocean, but in the latter over a very shallow subpolar region along the northern edge of the North Atlantic Ocean. In conclusion, we find that our simulations capture spatially heterogeneous responses of the early-Holocene climate.

Elucidating space based observations of solar induced chlorophyll fluorescence over terrestrial vegetation of India

Remotely sensed plant fluorescence signals have the potential to facilitate a better understanding of vegetation photosynthetic dynamics. Space based observations of solar-induced chlorophyll fluorescence (SIF) provides an alternative non-invasive approach to investigate gross primary production (GPP), a key element of ecological research. Orbiting carbon observatory (OCO-2) is providing continuous global measurements of SIF at 757 nm and 771 nm wavelengths with nadir and glint mode observations. Using one of the three spectrometer instruments, measurements of SIF complements OCO-2’s Carbon dioxide data with information on when and where plants are drawing carbon from the atmosphere. Here, we present spatio-temporal patterns of space borne observations of SIF and its correlation with GPP and vegetation indices over diverse terrestrial vegetation of India. The results indicate a spatial variability in SIF ranging from 0.001 to 0.6 (± 0.16) Wm−2 µm−1 sr−1 and 0.001 to 0.7 (± 0.2) Wm−2 µm−1 sr−1 in nadir and glint modes, respectively. OCO-2 derived SIF at finer scale shows a similar trend with MODIS derived GPP during September–October–November and June–July–August seasons. A linear correlation of satellite derived SIF with GPP (Pearson’s coefficient, r = 0.84), EVI (r = 0.72) and NDVI (r = 0.63) at 757 nm for 0.05° × 0.05° grid over terrestrial India is reported, besides seasonal variations. Amongst the diverse vegetation types, SIF and GPP are well correlated over Evergreen broadleaf forests. The study provides new insights and useful inputs to GPP modelling and improved understanding of terrestrial carbon cycle research in India.

“Easterlies”‐induced precipitation in eastern Patagonia: Seasonal influences of ENSO'S FLAVOURS and SAM

AbstractThe seasonal influence of the phases of El Niño‐Southern Oscillation (ENSO) and of Southern Annular Mode (SAM) on precipitation over eastern Patagonia is examined. Also, their impact in generating persistent daily easterly moisture flux leading to daily precipitation events. The significant signatures of the drivers on precipitation are found in March–April–May (MAM), June–July–August (JJA) for ENSO and November–December–January (NDJ) for SAM. Over the east coast, precipitation is mainly favoured owing to an overall growth of the seasonal mean number of precipitation days (~30%) by central Niño in JJA and by negative SAM phase in NDJ, and growth of the frequency of persistent events of daily westward moisture flux (~90%) by eastern Niña in MAM. A great spatial variability shapes the anomalies. Evidence is shown for weakened seasonal westerlies linked to Eastern Pacific Niño in MAM. Enhanced daily easterlies with net daily westward moisture flux in the region lead to strong daily precipitation. Growth in precipitation intensity in the northeast coast is also associated with Eastern Pacific Niño in MAM. In the interior lands, south of 45°S, westward moisture flux is increased up to ~200% by negative SAM phase in NDJ. The nature of extratropical low‐frequency transient eddies travelling over the South Pacific will influence the frequency and intensity of daily local precipitation as well as the persistent westward flux during ENSO events.

The substructure of extremely hot summers in the Northern Hemisphere

Abstract. In the last decades, extremely hot summers (hereafter extreme summers) have challenged societies worldwide through their adverse ecological, economic and public-health effects. In this study, extreme summers are identified at all grid points in the Northern Hemisphere in the upper tail of the June–July–August (JJA) seasonal mean 2 m temperature (T2m) distribution, separately in ERA-Interim (ERAI) re-analyses and in 700 simulated years with the Community Earth System Model (CESM) large ensemble for present-day climate conditions. A novel approach is introduced to characterise the substructure of extreme summers, i.e. to elucidate whether an extreme summer is mainly the result of the warmest days being anomalously hot, of the coldest days being anomalously mild or of a general shift towards warmer temperatures on all days of the season. Such a statistical characterisation can be obtained from considering so-called rank day anomalies for each extreme summer – that is, by sorting the 92 daily mean T2m values of an extreme summer and by calculating, for every rank, the deviation from the climatological mean rank value of T2m. Applying this method in the entire Northern Hemisphere reveals spatially strongly varying extreme-summer substructures, which agree remarkably well in the re-analysis and climate model data sets. For example, in eastern India the hottest 30 d of an extreme summer contribute more than 65 % to the total extreme-summer T2m anomaly, while the colder days are close to climatology. In the high Arctic, however, extreme summers occur when the coldest 30 d are substantially warmer than they are climatologically. Furthermore, in roughly half of the Northern Hemisphere land area, the coldest third of summer days contributes more to extreme summers than the hottest third, which highlights that milder-than-normal coldest summer days are a key ingredient of many extreme summers. In certain regions, e.g. over western Europe and western Russia, the substructure of different extreme summers shows large variability and no common characteristic substructure emerges. Furthermore, we show that the typical extreme-summer substructure in a certain region is directly related to the region's overall T2m rank day variability pattern. This indicates that in regions where the warmest summer days vary particularly strongly from one year to the other, these warmest days are also particularly anomalous in extreme summers (and analogously for regions where variability is largest for the coldest days). Finally, for three selected regions, thermodynamic and dynamical causes of extreme-summer substructures are briefly discussed, indicating that, for instance, the onset of monsoons, physical boundaries like the sea ice edge or the frequency of occurrence of Rossby wave breaking strongly determines the substructure of extreme summers in certain regions.

Comparative assessment of two RegCM versions in simulating Indian Summer Monsoon

In the present study, sensitivity of Indian Summer Monsoon (ISM) to the cumulus convection scheme (CCS) is assessed using Regional Climate Model (RegCM4.4.5). Seasonal scale (May–June–July–August–September) simulation of the model forced with European Centre for Medium Range Weather Forecasts reanalysis data (ERA interim) is carried out for the consecutive three monsoon years 2007, 2008 and 2009. Four major CCS (MIT, Grell, Tiedtke and Kain–Fritsch) are employed. Model simulated results are validated with various observed and reanalysis datasets. In addition, the model results are also compared with that of its earlier version (RegCM4.1). Detailed analysis reveals that the model’s ability to delineate large scale ISM features such as Heat Low, Tibetan high, Somali Jet, Tropical Easterly Jet (TEJ), Sub-tropical Westerly Jet (STWJ) are fairly well using multiple CCS options. Fairly, better model performance is identified while Grell over ocean and MIT over land (GO_ML) is used. This scheme exhibits relatively lower warm (cold) bias over entire northwest and partially central India (peninsular and other parts of India). Both lower and upper level circulation pattern including TEJ and STWJ are better simulated by GO_ML scheme. Simulated distribution of precipitation is also more realistic and closed to TRMM data using that scheme. Further comparison of results from two model versions indicates that the simulation with recent version (4.4) is more realistic than that with the earlier version (RegCM4.1). The study concludes that RegCM4.4 with GO_ML would be the optimal combination when overall performance of the model is taken into consideration.

Performance Evaluation of Bias Correction Methods for Climate Change Monthly Precipitation Projections over Costa Rica

Six bias correction (BC) methods; delta-method (DT), linear scaling (LS), power transformation of precipitation (PTR), empirical quantile mapping (EQM), gamma quantile mapping (GQM) and gamma-pareto quantile mapping (GPQM) were applied to adjust the biases of historical monthly precipitation outputs from five General Circulation Models (GCMs) dynamically downscaled by two Regional Climate Models (RCMs) for a total of seven different GCM-RCM pairs over Costa Rica. High-resolution gridded precipitation observations were used for the control period 1951–1980 and validated over the period 1981–1995. Results show that considerable biases exist between uncorrected GCM-RCM outputs and observations, which largely depend on GCM-RCM pair, seasonality, climatic region and spatial resolution. After the application of bias correction, substantial biases reductions and comparable performances among most BC methods were observed for most GCM-RCM pairs; with EQM and DT marginally outperforming the remaining methods. Consequently, EQM and DT were selectively applied to correct the biases of precipitation projections from each individual GCM-RCM pair for a near-future (2011–2040), mid-future (2041–2070) and far-future (2071–2100) period under Representative Concentration Pathways (RCPs) 2.6, 4.5 and 8.5 using the control period 1961–1990. Results from the bias-corrected future ensemble-mean anticipate a marked decreasing trend in precipitation from near to far-future periods during the dry season (December, January, February (DJF) and March, April, May (MAM)) for RCP4.5 and 8.5; with pronounced drier conditions for those climatic regions draining towards the Pacific Ocean. In contrast, mostly wetter conditions are expected during the dry season under RCP2.6, particularly for the Caribbean region. In most of the country, the greatest decrease in precipitation is projected at the beginning of the rainy season (June, July, August (JJA)) for the far-future period under RCP8.5, except for the Caribbean region where mostly wetter conditions are anticipated. Regardless of future period, slight increases in precipitation with higher radiative forcing are expected for SON excluding the Caribbean region, where precipitation is likely to increase with increasing radiative forcing and future period. This study demonstrates that bias correction should be considered before direct application of GCM-RCM precipitation projections over complex territories such as Costa Rica.

Two Propagation Pathways of the Boreal Summer Quasi-Biweekly Oscillation of the Atmospheric Heat Source Over the Tibetan Plateau

ABSTRACTBased on the daily Japanese 55-year Reanalysis data, the characteristics of the summer (June–July–August) quasi-biweekly oscillation (QBWO) of atmospheric apparent heat sources over the Tibetan Plateau (TP) and its surrounding areas (TPSR) are investigated. The first three leading modes of the atmospheric intraseasonal heat source over the TPSR reflect two independent propagation features of the QBWO. The first, which propagates from east to west, is associated with the mid- to upper tropospheric circulation, with large-scale anomalous cyclones and anticyclones propagating from northeast China, across the TP, and finally arriving in West Asia. The intensity of this QBWO rapidly increases when approaching the TP and significantly weakens during its westward journey upon leaving the TP. The second, which moves from west to east, is closely correlated with the mid-latitude Rossby wave train emanating from southern Europe that travels over the TP to the East Asian coast. Remarkable differences can be observed between the intensity of the wave train over the east and west sides of the TP. To the west of 90°E, the QBWO strengthens when proceeding to the TP and reaches its maximum over the TP. To the east of 90°E, the circulation anomalies are maintained for about half a cycle. For both propagation pathways, the QBWO of the heat source over the TPSR greatly modulates the circulation and precipitation anomalies over the TP, the Indian Peninsula, and the Indochina Peninsula.

An assessment of global satellite‐based precipitation datasets in capturing precipitation extremes: A comparison with observed precipitation dataset in India

AbstractThe availability of global satellite‐based precipitation datasets provides an asset to accomplish precipitation dependent analysis where gauge based precipitation datasets are not available or limited. In this study, we have taken three most popular and globally accepted satellite‐based daily gridded (0.25° × 0.25°) precipitation datasets such as Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Satellite Soil Moisture to Rain (SM2RAIN‐ASCAT) and Tropical Rainfall Measuring Mission (TRMM now available as Global Precipitation Measurement [GPM]) for 10 years (2007–2016) time‐series durations to test their reliability across India. The India Meteorological Department (IMD) observed daily gridded (0.25° × 0.25°) precipitation data have been taken as reference data to compare the other three satellite‐based gridded precipitation datasets by developing standard extreme precipitation indices (SEPIs). The precipitation extremity has been tested in the wet season (June–July–August–September) and throughout the year. We have also analysed the extreme behaviour of precipitation (in both upper and lower tails) using quantile‐quantile (Q–Q) regression analysis after selecting 33 random precipitation grids across India. The overall analysis results showed that all satellite‐based datasets have significant spatial heterogeneity in estimating precipitation extremes accurately which varies across India. Among all satellite‐precipitation datasets, TRMM found closer to IMD than SM2RAIN‐ASCAT and CHIRPS. The frequency based SEPIs showed that CHIRPS, TRMM and SM2RAIN‐ASCAT have similarities to IMD precipitations. The intensity‐based SEPIs show that TRMM and CHIRPS have significant similarities with IMD precipitations. The wet season‐based analysis results showed that TRMM and CHIRPS are closer to IMD precipitations than SM2RAIN‐ASCAT satellite‐precipitations. Overall TRMM and CHIRPS datasets performed well across most regions in India, while SM2RAIN‐ASCAT dataset has performed poorly in India, especially for extreme precipitation cases. Q–Q plots show that each satellite‐based precipitation dataset captured most of extreme cases in different quantile intervals with respect to IMD precipitation; however, SM2RAIN‐ASCAT has slightly under‐performed at many regions in India.

Latitudinal heterogeneity and hotspots of uncertainty in projected extreme precipitation

Projected precipitation from climate models is used in a wide range of fields for climate change impact assessment. However, the spatial pattern of uncertainty across latitudes and the global uncertainty hotspots are not well understood despite their importance for regional adaptation planning. In this study, we describe uncertainties in projected extreme precipitation changes per K global warming across latitudes, and decompose the overall uncertainty into climate model and internal variability uncertainties. We then identify global uncertainty hotspots and discuss the broader implications. Our results show that both uncertainty sources are highly heterogeneous across latitudes, while climate model uncertainty exceeds internal variability uncertainty for all seasons and precipitation intensities. The largest difference between model and internal variability uncertainties is found in tropical regions where model uncertainty is thrice as large as internal variability uncertainty in June–July–August season and twice as large as that in the other seasons. Tropical and subtropical regions are identified as the global uncertainty hotspots, with the Sahara desert and the southern part of the Middle East being the local hotspots. The large uncertainty in the tropics and subtropics is primarily due to the convective nature of rainstorms which cannot be adequately represented by coarse-scale climate models, and also to sparse observation networks based on which climate models can be tuned and improved. The results highlight areas where future model development and improvement efforts should focus to reduce the overall uncertainties in projected precipitation extremes.

Assessing Spatio-Temporal Variability of Wildfires and their Impact on Sub-Saharan Ecosystems and Air Quality Using Multisource Remotely Sensed Data and Trend Analysis

Globally, wildfires are considered the most commonly occurring disasters, resulting from natural and anthropogenic ignition sources. Wildfires consist of burning standing biomass at erratic degrees of intensity, severity, and frequency. Consequently, wildfires generate large amounts of smoke and other toxic pollutants that have devastating impacts on ambient air quality and human health. There is, therefore, a need for a comprehensive study that characterizes land–atmosphere interactions with regard to wildfires, critical for understanding the interrelated and multidimensional impacts of wildfires. Current studies have a limited scope and a narrow focus, usually only focusing on one aspect of wildfire impacts, such as air quality without simultaneously considering the impacts on land surface changes and vice versa. In this study, we use several multisource data to determine the spatial distribution, frequency, disturbance characteristics of and variability and distribution of pollutants emitted by wildfires. The specific objectives were to (1) study the sources of wildfires and the period they are prevalent in sub-Saharan Africa over a 9 year period, i.e., 2007–2016, (2) estimate the seasonal disturbance of wildfires on various vegetation types, (3) determine the spatial distribution of black carbon (BC), carbon monoxide (CO) and smoke, and (4) determine the vertical height distribution of smoke. The results show largest burned areas in December–January–February (DJF), June–July–August (JJA) and September–October–November (SON) seasons, and reciprocal high emissions of BC, CO, and smoke, as observed by Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). In addition, the results reveal an increasing trend in the magnitude of BC, and CO concentration driven by meteorological conditions such as low precipitation, low relative humidity, and low latent heat flux. Overall, this study demonstrates the value of multisource remotely sensed data in characterising long-term wildfire patterns and associated emissions. The results in this study are critical for informing better regional fire management and air quality control strategies to preserve endangered species and habitats, promote sustainable land management, and reduce greenhouse gases (GHG) emissions.

Present day bias and future change signal of temperature over China in a series of multi-GCM driven RCM simulations

Simulation of surface air temperature over China from a set of regional climate model (RCM) climate change experiments are analyzed with the focus on bias and change signal of the RCM and driving general circulation models (GCMs). The set consists of 4 simulations by the RCM of RegCM4 driven by 4 different GCMs for the period of 1979–2099 under the mid-range RCP4.5 (representative concentration pathway) scenario. Results show that for present day conditions, the RCM provides with more spatial details of the distribution and in general reduces the biases of GCM, in particular in DJF (December–January–February) and over areas with complex topography. Bias patterns show some correlation between the RCM and driving GCM in DJF but not in JJA (June–July–August). In JJA, the biases in RCM simulations show similar pattern and low sensitivity to the driving GCM, which can be attributed to the large effect of internal model physics in the season. For change signals, dominant forcings from the driving GCM are evident in the RCM simulations as shown by the magnitude, large scale spatial distribution, as well as interannual variation of the changes. The added value of RCM projection is characterized by the finer spatial detail in sub-regional (river basins) and local scale. In DJF, profound warming over the Tibetan Plateau is simulated by RCM but not GCMs. In general no clear relationships are found between the model bias and change signal, either for the driving GCMs or nested RCM.

Effects of a stochastic multicloud parameterization on the simulated Asian‐Australian monsoon rainfall in an AGCM

AbstractThe Asian‐Australian monsoon (AAM) plays a vital role in modulating the global and local climates. Current numerical models, including climate system models (CSMs) and atmospheric general circulation models (AGCMs), have difficulties in capturing the fundamental and intrinsic features of the AAM. By applying the stochastic multicloud model (SMCM) to the state‐of‐the‐art ECHAM6.3 atm model, we investigated the impacts of SMCM on the simulation of AAM precipitation. Though the modified model with the SMCM improves simulations of the AAM precipitation in June–July–August–September season, the solstice mode, the AAM domain, and the AAM precipitation intensity, deficiencies also exist in other aspects, that is, simulations of rainfall in December–January–February–March and April–May seasons, and the equinox mode. Analyses also unravelled the modified model improves in simulating the spatial patterns of seasonal variation of precipitation in the Indian monsoon region, western North Pacific monsoon region, and Australian monsoon region in terms of pattern correlation coefficient and normalized root‐mean square error scores. The enhanced simulation of solar radiation in the modified model favours improving the performance of the simulated solstice mode. In addition, moisture budget analysis was applied to investigate physical processes that modulate the variations and distributions of AAM precipitation. Analyses revealed that variations and distributions of the vertical integral of moisture flux convergence is closely associated with changes in precipitation, while the stratification of vertical moist transport is associated with changes in precipitation over land in the AAM region. In addition, the modified model influences the AAM precipitation mainly through the variations of moisture flux convergence. The present work potentially provides a method to improve the simulated capability of AGCMs.

A monitoring and prediction system for compound dry and hot events

Compound dry and hot events (i.e. concurrent or consecutive occurrences of dry and hot events), which may cause larger impacts than those caused by extreme events occurring in isolation, have attracted wide attention in recent decades. Increased occurrences of compound dry and hot events in different regions around the globe highlight the importance of improved understanding and modeling of these events so that they can be tracked and predicted ahead of time. In this study, a monitoring and prediction system of compound dry and hot events at the global scale is introduced. The monitoring component consists of two indicators (standardized compound event indicator and a binary variable) that incorporate both dry and hot conditions for characterizing the severity and occurrence. The two indicators are shown to perform well in depicting compound dry and hot events during June–July–August 2010 in western Russia. The prediction component consists of two statistical models, including a conditional distribution model and a logistic regression model, for predicting compound dry and hot events based on El Niño–Southern Oscillation, which is shown to significantly affect compound events of several regions, including northern South America, southern Africa, southeast Asia, and Australia. These models are shown to perform well in predicting compound events in large regions (e.g. northern South America and southern Africa) during December–January–February 2015–2016. This monitoring and prediction system could be useful for providing early warning information of compound dry and hot events.

PROJECTION OF INCIDENT SURFACE SOLAR RADIATION IN CHINA UNDER A CLIMATE CHANGE SCENARIO

Abstract. We projected incident surface solar radiation (SSR) over China in the middle (2040–2059) and end (2080–2099) of the 21st century in the Representative Concentration Pathway (RCP) 8.5 scenario using a multi-model ensemble derived from the weighted average of seven global climate models (GCMs). The multi-model ensemble captured the contemporary (1979–2005) spatial and temporal characteristics of SSR and reproduced the long-term temporal evolution of the mean annual SSR in China. However, it tended to overestimate values compared to observations due to the absence of aerosol effects in the simulations. The future changes in SSR showed increases over eastern and southern China, and decreases over the Tibetan Plateau (TP) and northwest China relative to the present day. At the end of the 21st century, there were SSR increases of 9–21 W m−2 over northwest, central, and south China, and decreases of 18–30 W m−2 over the TP in June–July–August (JJA). In northeast China, SSR showed seasonal variation with increases in JJA and decreases in December–January–February. The time series of annual SSR had a decreased linear trend for the TP, and a slightly increased trend for China during 2006–2099. The results of our study suggest that solar energy resources will likely decrease in the TP under future climate change scenarios.

Boundary Layer Heights as Derived From Ground-Based Radar Wind Profiler in Beijing

The vertical structure of wind is a key factor in modulating air quality, from which the determination of boundary layer height (BLH) remains a major challenge. In this paper, we developed an improved threshold method to determine the BLH from radar wind profiler (RWP) measurements. The normalized signal-to-noise ratio (SNR) profiles were used instead of the original SNR profiles to avoid instrumental inconsistencies. Additionally, a peak filter was designed to indicate the BLH based on the maximum SNR by taking into account the multiple peaks in the SNR profile. This algorithm was then applied to the RWP measurements taken in the summer (June–July–August) of 2018 in Beijing to obtain the BLHs. Validation analyses suggested that the BLH retrievals from RWP exhibited high consistency with those from radiosondes, with an average correlation coefficient of 0.69 (0.66) and a root mean squared error of 0.39 (0.41) in the daytime (nighttime). Additionally, the major features of summertime BLHs in Beijing were examined. In particular, a distinct diurnal variation in BLH was observed with a peak (1630 ± 510 m) occurring at 0600 universal time coordinated (UTC) and a minimum (587 ± 343 m) at 2300 UTC. Therefore, the algorithm presented here has great potential to be applied to other regions to obtain reliable BLHs. The findings obtained here highlight the importance of vertical wind structure in air quality studies.

The China Multi-Model Ensemble Prediction System and Its Application to Flood-Season Prediction in 2018

Multi-model ensemble prediction is an effective approach for improving the prediction skill short-term climate prediction and evaluating related uncertainties. Based on a combination of localized operation outputs of Chinese climate models and imported forecast data of some international operational models, the National Climate Center of the China Meteorological Administration has established the China multi-model ensemble prediction system version 1.0 (CMMEv1.0) for monthly–seasonal prediction of primary climate variability modes and climate elements. We verified the real-time forecasts of CMMEv1.0 for the 2018 flood season (June–August) starting from March 2018 and evaluated the 1991–2016 hindcasts of CMMEv1.0. The results show that CMMEv1.0 has a significantly high prediction skill for global sea surface temperature (SST) anomalies, especially for the El Nino–Southern Oscillation (ENSO) in the tropical central–eastern Pacific. Additionally, its prediction skill for the North Atlantic SST triple (NAST) mode is high, but is relatively low for the Indian Ocean Dipole (IOD) mode. Moreover, CMMEv1.0 has high skills in predicting the western Pacific subtropical high (WPSH) and East Asian summer monsoon (EASM) in the June–July–August (JJA) season. The JJA air temperature in the CMMEv1.0 is predicted with a fairly high skill in most regions of China, while the JJA precipitation exhibits some skills only in northwestern and eastern China. For real-time forecasts in March–August 2018, CMMEv1.0 has accurately predicted the ENSO phase transition from cold to neutral in the tropical central–eastern Pacific and captures evolutions of the NAST and IOD indices in general. The system has also captured the main features of the summer WPSH and EASM indices in 2018, except that the predicted EASM is slightly weaker than the observed. Furthermore, CMMEv1.0 has also successfully predicted warmer air temperatures in northern China and captured the primary rainbelt over northern China, except that it predicted much more precipitation in the middle and lower reaches of the Yangtze River than observation.

Dependence of Rainfall on Solar Activity Features

In the present paper, we have studied the statistical analysis between all India homogeneous rainfall (RF) with sunspot number (SN) during 1900-2014 (115-year period). We have calculated correlations coefficient of rainfall with sunspot number (SN) for annual and seasonal months: January, February (JF); March April May (MAM); June July August September (JJAS) and October November December (OND) and we have obtained high correlation ranging between 0.75 to 0.95. Our results show that rainfall is strongly influenced by sunspot number. Our study also indicates that occurrence of solar activity features play an important role for variability of rainfall.