December January February Research Articles

Unusual past dry and wet rainy seasons over Southern Africa and South America from a climate perspective

Southern Africa and Southern South America have experienced recent extremes in dry and wet rainy seasons which have caused severe socio-economic damages. Selected past extreme events are here studied, to estimate how human activity has changed the risk of the occurrence of such events, by applying an event attribution approach (Stott et al., 2004)comprising global climate models of Coupled Model Intercomparison Project 5 (CMIP5). Our assessment shows that models' representation of mean precipitation variability over Southern South America is not adequate to make a robust attribution statement about seasonal rainfall extremes in this region. Over Southern Africa, we show that unusually dry austral summers as occurred during 2002/2003 have become more likely, whereas unusually wet austral summers like that of 1999/2000 have become less likely due to anthropogenic climate change. There is some tentative evidence that the risk of extreme high 5-day precipitation totals (as observed in 1999/2000) have increased in the region. These results are consistent with CMIP5 models projecting a general drying trend over SAF during December–January–February (DJF) but also an increase in atmospheric moisture availability to feed heavy rainfall events when they do occur. Bootstrapping the confidence intervals of the fraction of attributable risk has demonstrated estimates of attributable risk are very uncertain, if the events are very rare. The study highlights some of the challenges in making an event attribution study for precipitation using seasonal precipitation and extreme 5-day precipitation totals and considering natural drivers such as ENSO in coupled ocean–atmosphere models.

The influence of extratropical Atlantic Ocean region on wet and dry years in North-Northeastern Brazil

The rainy season of north-Northeastern Brazil (NE), one of the tropical regions with the largest rainfall interannual variability, is associated with the Intertropical Convergence Zone (ITCZ) influence, which is located in the southermost position during this season (March-April-May). However, there is a large interannual variability in the ITCZ position, associated with atmospheric and oceanic anomalies, responsible for dry or wet years in the region. Besides the tropical Pacific Sea Surface Temperature (SST) anomalies and the Tropical Atlantic SST gradient influences on this variability, extratropical atmospheric influences are identified over the North and South Atlantic. Composites of cases with anomalous precipitation during the middle of the rainy season (April) indicate the El Nino-Southern Oscillation (ENSO) features and northern and southern Atlantic atmospheric anomalies in December-January-February (DJF). When El Nino and La Nina years are removed from the composites of dry and wet cases, respectively, the centers of action over the extratropical North Atlantic in DJF remain, and features of extratropical South Hemisphere become more evident. The tropical SST dipole is still present with inverted signs in each case, consistent with the NE precipitation anomalies. The main mode of variability in the Southern Hemisphere (South Annular Mode) and in the Northern Hemisphere (North Annular Mode) is present in DJF during the dry cases. These annular patterns occur in the composites of Northeast Brazil precipitation anomalies including or excluding ENSO years. Therefore, besides ENSO relations with SAM discussed in previous studies, another connection exists over the South Atlantic, linking anomalies between high and tropical latitudes. The precipitation anomalies over NE have contributions of the two extratropical Atlantic hemispheres regions. The atmospheric anomalies observed in the pre-rainy season can be a helpful tool in monitoring the North Northeastern B

Stochastic bias correction of dynamically downscaled precipitation fields for Germany through Copula-based integration of gridded observation data

Abstract. Dynamically downscaled precipitation fields from regional climate models (RCMs) often cannot be used directly for regional climate studies. Due to their inherent biases, i.e., systematic over- or underestimations compared to observations, several correction approaches have been developed. Most of the bias correction procedures such as the quantile mapping approach employ a transfer function that is based on the statistical differences between RCM output and observations. Apart from such transfer function-based statistical correction algorithms, a stochastic bias correction technique, based on the concept of Copula theory, is developed here and applied to correct precipitation fields from the Weather Research and Forecasting (WRF) model. For dynamically downscaled precipitation fields we used high-resolution (7 km, daily) WRF simulations for Germany driven by ERA40 reanalysis data for 1971–2000. The REGNIE (REGionalisierung der NIEderschlagshöhen) data set from the German Weather Service (DWD) is used as gridded observation data (1 km, daily) and aggregated to 7 km for this application. The 30-year time series are split into a calibration (1971–1985) and validation (1986–2000) period of equal length. Based on the estimated dependence structure (described by the Copula function) between WRF and REGNIE data and the identified respective marginal distributions in the calibration period, separately analyzed for the different seasons, conditional distribution functions are derived for each time step in the validation period. This finally allows to get additional information about the range of the statistically possible bias-corrected values. The results show that the Copula-based approach efficiently corrects most of the errors in WRF derived precipitation for all seasons. It is also found that the Copula-based correction performs better for wet bias correction than for dry bias correction. In autumn and winter, the correction introduced a small dry bias in the northwest of Germany. The average relative bias of daily mean precipitation from WRF for the validation period is reduced from 10% (wet bias) to −1% (slight dry bias) after the application of the Copula-based correction. The bias in different seasons is corrected from 32% March–April–May (MAM), −15% June–July–August (JJA), 4% September–October–November (SON) and 28% December–January–February (DJF) to 16% (MAM), −11% (JJA), −1% (SON) and −3% (DJF), respectively. Finally, the Copula-based approach is compared to the quantile mapping correction method. The root mean square error (RMSE) and the percentage of the corrected time steps that are closer to the observations are analyzed. The Copula-based correction derived from the mean of the sampled distribution reduces the RMSE significantly, while, e.g., the quantile mapping method results in an increased RMSE for some regions.

Seasonal prediction skill of winter temperature over North India

The climatology, amplitude error, phase error, and mean square skill score (MSSS) of temperature predictions from five different state-of-the-art general circulation models (GCMs) have been examined for the winter (December–January–February) seasons over North India. In this region, temperature variability affects the phenological development processes of wheat crops and the grain yield. The GCM forecasts of temperature for a whole season issued in November from various organizations are compared with observed gridded temperature data obtained from the India Meteorological Department (IMD) for the period 1982–2009. The MSSS indicates that the models have skills of varying degrees. Predictions of maximum and minimum temperature obtained from the National Centers for Environmental Prediction (NCEP) climate forecast system model (NCEP_CFSv2) are compared with station level observations from the Snow and Avalanche Study Establishment (SASE). It has been found that when the model temperatures are corrected to account the bias in the model and actual orography, the predictions are able to delineate the observed trend compared to the trend without orography correction.

Climatology and trends of summer high temperature days in India during 1969–2013

Based on the daily maximum air temperature data from 176 stations in India from 1969 to 2013, the climatological distribution of the number of days with high temperature (HT) defined as days with maximum temperature higher than 37°C during summer season (March–June) are studied. With a focus on the regional variability and long-term trends, the impacts of HT days are examined by dividing the country into six geographical regions (North, West, North-central, East, South-central and South). Although the long-term (1969–2013) climatological numbers of HT days display well-defined spatial patterns, there is clear change in climatological mean and coefficient of variation of HT days in a recent period (1991–2013). The long period trends indicate increase in summer HT days by 3%, 5%, and 18% in north, west, and south regions, respectively and decrease by 4% and 9% in north-central and east regions respectively. However, spatial variations in HT days exist across different regions in the country. The data analysis shows that 2010 was the warmest summer year and 2013 was the coolest summer year in India. Comparison of spatial distributions of trends in HT days for 1969–1990 and 1991–2013 periods reveal that there is an abrupt increase in the number of HT days over north, west and north-central regions of India probably from mid 1990s. A steep increase in summer HT days in highly populated cities of Mumbai, New Delhi, Chennai, Jaipur, and Visakhapatnam is noticed during the recent period of 1991–2013. The summer HT days over southern India indicate significant positive correlation with Nino 3.4 index for three months’ running mean (December–January–February, January–March, February–April, March–May and April–June).

Land surface anomalies preceding the 2010 Russian heat wave and a link to the North Atlantic oscillation

The Eurasian wheat belt (EWB) spans a region across Eastern Ukraine, Southern Russia, and Northern Kazakhstan; accounting for nearly 15% of global wheat production. We assessed land surface conditions across the EWB during the early growing season (April–May–June; AMJ) leading up to the 2010 Russian heat wave, and over a longer-term period from 2000 to 2010. A substantial reduction in early season values of the normalized difference vegetation index occurred prior to the Russian heat wave, continuing a decadal decline in early season primary production in the region. In 2010, an anomalously cold winter followed by an abrupt shift to a warmer-than-normal early growing season was consistent with a persistently negative phase of the North Atlantic oscillation (NAO). Regression analyses showed that early season vegetation productivity in the EWB is a function of both the winter (December–January–February; DJF) and AMJ phases of the NAO. Land surface anomalies preceding the heat wave were thus consistent with highly negative values of both the DJF NAO and AMJ NAO in 2010.

Changes in the Brewer-Dobson circulation for 1980–2009 revealed in MERRA reanalysis data

Changes in the Brewer-Dobson circulation (BDC) during the 30 years 1980–2009 are investigated using Modern Era Retrospective-analysis for Research and Applications (MERRA) reanalysis data. The mass streamfunction that is induced by wave forcings in the transformed Eulerian-mean (TEM) equation through the downward-control principle is used as a proxy for the BDC. The changes in the BDC are investigated using two aspects: the wave propagation conditions in the stratosphere and the wave activity in the upper troposphere. They are compared in the first (P1) and second (P2) 15-year periods. The resolved wave forcing, expressed by the Eliassen-Palm (EP) flux divergence (EPD), is significantly enhanced during the December-January-February (DJF) season in P2 in both the Northern Hemisphere (NH) high latitudes and the Southern Hemisphere (SH) mid- and high latitudes. The increased zonal mean zonal wind at high latitudes in the SH, caused by ozone depletion, leads to an upward shift of the Rossby-wave critical layer and this allows more transient planetary waves to propagate into the stratosphere. In the NH, the enhanced EPD in DJF leads to an increase in the frequency of Sudden Stratospheric Warming (SSW) events. The gravity wave drag (GWD) is smaller than the EPD and the change in it between the two time periods is insignificant. The residual term in the TEM equation is similar to the GWD in the two periods, but its change between the two periods is as large as the change in the EPD. Among the four components of the EP flux at 250 hPa, the meridional heat flux played a dominant role in the enhancement of the BDC in P2.

Influence of ENSO SSTs on the spread of the probability density function for precipitation and land surface temperature

The impact of the interannual variations in ENSO SSTs on the spread of probability density function (PDF) for the seasonal mean of variables of societal relevance are analyzed based on a large set of the hindcasts from NCEP CFSv2. The study is focused on the analysis of global rainfall and 2-m temperature over land (T2m) for December–January–February (DJF) seasonal mean. For rainfall, the spatial distribution of the ENSO SST induced changes on the spread of PDF strongly resembles changes in the mean but have a smaller amplitude. Over the central-eastern equatorial Pacific, changes in the spread lead to a reduction in signal-to-noise ratio (SNR) during El Nino years while to an increase in the SNR during La Nina years. Over extratropics, year to year changes in the spread are relatively small. For T2m, the changes in spread have little systematic dependence on the ENSO SSTs and the amplitudes of the changes in spread are much smaller than corresponding changes in the ensemble mean. The results demonstrate small systematic year to year variations in the PDF spread, for example over extratropics for rainfall and over most of global land areas for T2m, and indicate that it might be a good practice in seasonal predictions to assume that the spread of seasonal means from year to year is constant and the skill in seasonal forecast information resides primarily in the shift of the first moment of the seasonal mean of the PDF.

SST prediction methodologies and verification considerations for dynamical mid-summer rainfall forecasts for South Africa

Seasonal-to-interannual hindcasts (re-forecasts) for December-January-February (DJF) produced at a 1-month lead-time by the ECHAM4.5 atmospheric general circulation model (AGCM) are verified after calibrating model output to DJF rainfall at 94 districts across South Africa. The AGCM is forced with SST forecasts produced by (i) statistically predicted SSTs, and (ii) predicted SSTs from a dynamically coupled ocean-atmosphere model. The latter SST forecasts in turn consist of an ensemble mean of SST forecasts, and also by considering the individual ensemble members of the SST forecasts. Probabilistic hindcasts produced for two separate category thresholds are verified over a 24-year test period from 1978/79 to 2001/02 by investigating the various AGCM configurations’ attributes of discrimination (whether the forecasts are discernibly different given different outcomes) and reliability (whether the confidence communicated in the forecasts is appropriate). Deterministic hindcast skill is additionally calculated through a range of correlation estimates between hindcast and observed DJF rainfall. For both probabilistic and deterministic verification the hindcasts produced by forcing the AGCM with dynamically predicted SSTs attain higher skill levels than the AGCM forced with statistical SSTs. Moreover, ensemble mean SST forecasts lead to improved skill over forecasts that considered an ensemble distribution of SST forecasts.Keywords: AGCM, SST predictions, seasonal forecasting, South Africa

Simulation of the interannual variations of aerosols in China: role of variations in meteorological parameters

Abstract. We used the nested grid version of the global three-dimensional Goddard Earth Observing System chemical transport model (GEOS-Chem) to examine the interannual variations (IAVs) of aerosols over heavily polluted regions in China for years 2004–2012. The role of variations in meteorological parameters was quantified by a simulation with fixed anthropogenic emissions at year 2006 levels and changes in meteorological parameters over 2004–2012. Simulated PM2.5 (particles with a diameter of 2.5 μm or less) aerosol concentrations exhibited large IAVs in North China (NC; 32–42° N, 110–120° E), with regionally averaged absolute percent departure from the mean (APDM) values of 17, 14, 14, and 11% in December-January-February (DJF), March-April-May (MAM), June-July-August (JJA), and September-October-November (SON), respectively. Over South China (SC; 22–32° N, 110–120° E), the IAVs in PM2.5 were found to be the largest in JJA, with the regional mean APDM values of 14% in JJA and of about 9% in other seasons. The concentrations of PM2.5 over the Sichuan Basin (SCB; 27–33° N, 102–110° E) were simulated to have the smallest IAVs among the polluted regions examined in this work, with APDM values of 8–9% in all seasons. All aerosol species (sulfate, nitrate, ammonium, black carbon, and organic carbon) were simulated to have the largest IAVs over NC in DJF, corresponding to the large variations in meteorological parameters over NC in this season. Process analyses were performed to identify the key meteorological parameters that determined the IAVs of different aerosol species in different regions. While the variations in temperature and specific humidity, which influenced the gas-phase formation of sulfate, jointly determined the IAVs of sulfate over NC in both DJF and JJA, wind (or convergence of wind) in DJF and precipitation in JJA were the dominant meteorological factors to influence IAVs of sulfate over SC and the SCB. The IAVs in temperature and specific humidity influenced gas-to-aerosol partitioning, which were the major factors that led to the IAVs of nitrate aerosol in China. The IAVs in wind and precipitation were found to drive the IAVs of organic carbon aerosol. We also compared the IAVs of aerosols simulated with variations in meteorological parameters alone with those simulated with variations in anthropogenic emissions alone; the variations in meteorological fields were found to dominate the IAVs of aerosols in northern and southern China over 2004–2012. Considering that the IAVs in meteorological fields are mainly associated with natural variability in the climate system, the IAVs in aerosol concentrations driven by meteorological parameters have important implications for the effectiveness of short-term air quality control strategies in China.

Role of the western tropical Pacific in the North Pacific regime shift in the winter of 1998/1999

AbstractIn this study, we examine the role of the western tropical Pacific in the North Pacific regime shift during the winter (December–January–February) of 1998/1999. The North Pacific regime shift in the winter of 1998/1999 is characterized by a dipole‐like structure along 40°N where significant warming is prominent in the western and central North Pacific. This shift might have been associated with an abrupt warming in the western tropical Pacific after 1998/1999, in the North Equatorial Current (NEC) bifurcation region (8°N–18°N, 125°E–160°E) in particular. The NEC bifurcation region experiences a regime shift from cooling to warming during the 1998/1999. Consequently, the Kuroshio current, which originates in the NEC bifurcation region, could transport the anomalous warm water into the North Pacific. In addition, a sudden shift in the NEC bifurcation latitude to the south also contributes to the 1998/1999 regime shift in the North Pacific. We suggest a possible mechanism that could have caused the regime shift in the NEC bifurcation region during the 1998/1999. An abrupt deepening of the thermocline depth in the NEC bifurcation region from 1997/1998 to 1998/1999, which is associated with the biggest El Niño in 1997/1998 and a subsequent La Niña event in 1998/1999, plays a role in initiating the shift in the NEC bifurcation region. Subsequently, the frequent occurrence of the La Niña events after 1998/1999 plays a role in maintaining the regime shift toward warming by sustaining a deepening of the thermocline depth.

Projected increases in summer and winter UK sub-daily precipitation extremes from high-resolution regional climate models

Summer (June–July–August; JJA) UK precipitation extremes projections from two UK Met Office high-resolution (12 km and 1.5 km) regional climate models (RCMs) are shown to be resolution dependent. The 1.5 km RCM projects a uniform () increase in 1 h JJA precipitation intensities across a range of return periods. The 12 km RCM, in contrast, projects decreases in short return period (≦̸5 years) events but strong increases in long return period (⩾20 years) events. We have low physical and statistical confidence in the 12 km RCM projections for longer return periods. Both models show evidence for longer dry periods between events. In winter (December–January–February; DJF), the models show larger return level increases (⩾40%). Both DJF projections are consistent with results from previous work based on coarser resolution models.

Inter-annual and inter-seasonal variability of the Orkney wave power resource

The waters surrounding the Orkney archipelago in the north of Scotland are one of the key regions in the world suitable for exploitation of both wave and tidal energy resources. Accordingly, Orkney waters are currently host to 1.08GW of UK Crown Estate leased wave and tidal energy projects, with a further 0.5GW leased in the southern part of the adjacent Pentland Firth. Although several wave resource models exist of the region, most of these models are commercial, and hence the results not publicly available, or have insufficient spatial/temporal resolution to accurately quantify the wave power resource of the region. In particular, no study has satisfactorily resolved the inter-annual and inter-seasonal variability of the wave resource around Orkney. Here, the SWAN wave model was run at high resolution on a high performance computing system, quantifying the Orkney wave power resource over a ten year period (2003–2012), a decade which witnessed considerable inter-annual variability in the wave climate. The results of the validated wave model demonstrate that there is considerable variability of the wave resource surrounding Orkney, with an extended winter (December–January–February–March, DJFM) mean wave power ranging from 10 to 25kW/m over the decade of our study. Further, the results demonstrate that there is considerably less uncertainty (30%) in the high energy region to the west of Orkney during winter months, in contrast to much greater uncertainty (60%) in the lower energy region to the east of Orkney. The DJFM wave resource to the west of Orkney correlated well with the DJFM North Atlantic Oscillation (NAO). Although a longer simulated time period would be required to fully resolve inter-decadal variability, these preliminary results demonstrate that due to considerable inter-annual variability in the NAO, it is important to carefully consider the time period used to quantify the wave power resource of Orkney, or regions with similar exposure to the North Atlantic. Finally, our study reveals that there is significantly less variability in the practical wave power resource, since much of the variability in the theoretical resource is contained within relatively few extreme events, when a wave device enters survival mode.

Separation of the lapse rate and the cold point tropopauses in the tropics and the resulting impact on cloud top-tropopause relationships

Four years of temperature profiles from Constellation Observing System for Meteorology, Ionosphere, and Climate GPS satellite retrievals are used to examine the difference between the World Meteorological Organization lapse rate definition and the cold point definition of the tropopause in the tropics. The separation between the cold point tropopause (CPT) and lapse rate tropopause (LRT) heights is quantified in seasonal averages and with the frequency of occurrence. In seasonal averages, small separations, <0.5 km, are found in the deep tropics, increasing to ~1 km toward higher latitudes and maximizing at ~1.5 km near the jet streams. The seasonal average separations show significant longitudinal structures in the December-January-February (DJF) and June-July-August (JJA) seasons. Case studies indicate that breaking Rossby waves and their effects extending into the equatorial region are responsible for the longitudinal structure in the DJF season. The seasonal average CPT-LRT separation therefore identifies the regions of the tropical upper troposphere-lower stratosphere that are controlled by extratropical forcing. Examination of individual profiles shows that a small yet significant fraction (~12%) of temperature profiles has CPT-LRT separations of 1 km or larger in the region of small seasonal average separation. These large separations are produced by wave perturbations of the upper tropospheric temperature structure. The impact of tropopause separation on the cloud top-tropopause relationship is examined using colocated CALIPSO cloud top data. We find that the frequency of clouds above the tropopause is reduced by approximately 50% if the CPT is used instead of LRT. The occurrence of clouds above the CPT is nevertheless significant, especially over the western Pacific in the DJF season and over the Asian monsoon region in the JJA season.

Analysis of surface air temperature warming rate of China in the last 50 years (1962–2011) using k-means clustering

Using observed surface temperature data from about 570 meteorological stations in China, the linear temperature trend over the last 50 years (1962–2011) is estimated. Average warming rates at all stations are 0.284 ± 0.142, 0.200 ± 0.163, and 0.392 ± 0.193 °C/10 a for annual, June–July–August (JJA), and December–January–February (DJF) mean temperatures, respectively. The warming rates (°C/a) have a regressed slope of 0.001 with latitude, and their relationship with latitude using data from stations below 1,000 m altitude is estimated at 0.009 °C/(10 a · degree). A k-means clustering is constructed using correlation coefficients to quantify the closeness. The clustering schemes with k = 5 and k = 10 are successfully implemented for annual, DJF, and JJA mean temperatures. The k = 10 scheme showed greater ability to distinguish different locations than the k = 5 clustering. In both schemes, the mean estimated warming rates are averaged over stations in each cluster. The clusters in DJF are much different than those for the entire year and for JJA. The average warming rate in each cluster is estimated and compared. Using stations in the cluster of high-elevation southeast Tibetan Plateau, the relationship between warming rates and altitude is estimated at 1.0 × 10−2 °C/(10 a · 100 m).

Wave characteristic analysis and wave energy resource evaluation in the China Sea

This study presents the characteristics of the China Sea wave energy resource and wave climate for the period August 1999 to July 2009, using a 10-yr hindcast wave data obtained from WAVEWATCH-III (WW3) numerical wave model forced with QuikSCAT/NCEP (QN) wind data. Results show that: (1) The China Sea wave field has significant seasonal differences, especially the South China Sea which is obviously affected by the monsoon. In JJA (June, July, August), the dominant wave direction is south to southwest. In DJF (December, January, February), the dominant wave direction is north to northeast. The Luzon Strait is the relative large center of significant wave height in DJF (2.4–2.8 m), SON (September, October, November) (2.0–2.4 m), and MAM (March, April, May) (1.0–1.4 m), while the southeast area of the Indo-China Peninsula is the large center in JJA (1.2–1.6 m). (2) The rough sea occurrence in the China Sea is relative low, of below 14% in each season. The relative high occurrence happened in DJF, followed by the SON, lowest in MAM. (3) The extreme wave height with return period of 20 yr in the Bohai Sea, Yellow Sea, low latitude of the South China Sea is below 8 m. The large areas mainly distribute in the middle latitude. The distribution of extreme wave height with return period of 30 yr is similar and about 2 m larger than that with return period of 20 yr. (4) We find the China Sea wave power increases gradually from MAM to DJF. The Luzon Strait is the relative large region of wave power in DJF (30–40 kW/m), SON (21–27 kW/m), and MAM (6–10 kW/m), while the southeast area of the Indo-China Peninsula is the large center in JJA (6–11 kW/m). (5) The stability of the China Sea wave power exhibits obvious regional and seasonal differences. The wave power is the most stable in DJF. The wave power in the north area of the South China and East Sea is apparently more stable than that in other sea areas, in the offshore is more stable than that in the near shore. (6) Judging from the value and stability of wave power, we find that the relative rich-energy region locates in the Luzon Strait and adjacent waters in DJF, SON, and MAM, while in the southeast area of the Indo-China Peninsula in JJA.

Surface temperature forecast skill comparison for the west coast of Saudi Arabia

Given the growing interest of the general public in accessing commercial weather forecasts through various media outlets and the available impetuses for promoting tourism in Saudi Arabia (SA), a first attempt is made to present a forecast skill comparison for surface temperature in four cities (Wejh, Yenbo, Jeddah, and Gizan) along the west coast of SA, for the 61-day transitional period (from January 16 to March 16) between the December-January-February (DJF) and the March-April-May (MAM) seasons. A simple skill score comparison method is used to assess the next-day city forecasts for surface temperature from six commercial weather forecast providers based on the operational numerical weather prediction (NWP) model outputs. All the NWP model forecast providers performed better than the respective daily climatology (Clm) for each station. Depending upon the station and the provider, the absolute average maximum daily surface temperature difference between the forecasts and the observations was less than 2 °C. Daily surface temperature forecasts from two versions of an atmospheric-ocean general circulation model are also compared to assess their performance for these coastal locations.

Assimilation of MODIS snow cover through the Data Assimilation Research Testbed and the Community Land Model version 4

To improve snowpack estimates in Community Land Model version 4 (CLM4), the Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) was assimilated into the Community Land Model version 4 (CLM4) via the Data Assimilation Research Testbed (DART). The interface between CLM4 and DART is a flexible, extensible approach to land surface data assimilation. This data assimilation system has a large ensemble (80-member) atmospheric forcing that facilitates ensemble-based land data assimilation. We use 40 randomly chosen forcing members to drive 40 CLM members as a compromise between computational cost and the data assimilation performance. The localization distance, a parameter in DART, was tuned to optimize the data assimilation performance at the global scale. Snow water equivalent (SWE) and snow depth are adjusted via the ensemble adjustment Kalman filter, particularly in regions with large SCF variability. The root-mean-square error of the forecast SCF against MODIS SCF is largely reduced. In DJF (December-January-February), the discrepancy between MODIS and CLM4 is broadly ameliorated in the lower-middle latitudes (23°–45°N). Only minimal modifications are made in the higher-middle (45°–66°N) and high latitudes, part of which is due to the agreement between model and observation when snow cover is nearly 100%. In some regions it also reveals that CLM4-modeled snow cover lacks heterogeneous features compared to MODIS. In MAM (March-April-May), adjustments to snow move poleward mainly due to the northward movement of the snowline (i.e., where largest SCF uncertainty is and SCF assimilation has the greatest impact). The effectiveness of data assimilation also varies with vegetation types, with mixed performance over forest regions and consistently good performance over grass, which can partly be explained by the linearity of the relationship between SCF and SWE in the model ensembles. The updated snow depth was compared to the Canadian Meteorological Center (CMC) data. Differences between CMC and CLM4 are generally reduced in densely monitored regions.

Analysis of RegCM3 generated weather regimes over central Brazil: a case study in Distrito Federal

Low level atmospheric circulations downscaled by regional climate model are used to identify austral summer (December–January–February) weather regimes (WRs) over central Brazil. For the period 1989–2006, the Kohonen’s self-organizing maps method is applied to provide simulated weather patterns and their relationship with daily rainfall. Six WRs were defined: two related to the active phase of the South American monsoon system (SAMS), with the large occurrence of days with extreme rainfall; two related to the break phase and few occurrences of extreme rainfall; and two others resembling transient WRs, those with fewer extreme rainfall. The WRs formed a cycle obtained from their transition probabilities, which suggests alternating phases of the convergence zone and their association with extreme rainfall. Thus, the variability of summer precipitation, related to the behavior of the simulated WRs enables the verification of the regional dynamic model on intraseasonal time scale.

Monitoring spatial-temporal variability of aerosol over Kenya

This study sought to investigate the spatial and temporal variations of aerosols over Kenya based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite sensor Aerosol Optical Depth (AOD) data for the period between 2001 and 2012. A Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model was used for trajectory analysis in order to reconstruct the origins of air masses and understand the Spatial and temporal variability of aerosol concentrations. Validation of MODIS AOD using Aerosol Robotic Network (AERONET) indicated that MODIS overestimated the aerosol loading over the study region. Space time variability of MODIS AOD measurements over Kenya showed a decreasing trend in aerosol loading with a long term mean of between 0.02 and 0.56. Mean monthly AOD values showed two peaks during the months of July and December while seasonal variations indicated high aerosol loading during the December – January –February (DJF) and June –July –August (JJA) season. Back trajectory analyses showed that aerosols mainly dust and sea salt reaching Kenya were transported from either Arabian or Indian sub continent or western parts of the Indian Ocean respectively. Therefore, long term and more comprehensive satellite AOD retrievals are necessary in order to achieve a better understanding of spatial and temporal variations in aerosols over KenyaKey Words: Aerosol Optical Depth, MODIS, Kenya