How predictable is the northern hemisphere summer upper-tropospheric circulation?

The retrospective forecast skill of three coupled climate models (NCEP CFS, GFDL CM2.1, and CAWCR POAMA 1.5) and their multi-model ensemble (MME) is evaluated, focusing on the Northern Hemisphere (NH) summer upper-tropospheric circulation along with surface temperature and precipitation for the 25-year period of 1981–2005. The seasonal prediction skill for the NH 200-hPa geopotential height basically comes from the coupled models’ ability in predicting the first two empirical orthogonal function (EOF) modes of interannual variability, because the models cannot replicate the residual higher modes. The first two leading EOF modes of the summer 200-hPa circulation account for about 84% (35.4%) of the total variability over the NH tropics (extratropics) and offer a hint of realizable potential predictability. The MME is able to predict both spatial and temporal characteristics of the first EOF mode (EOF1) even at a 5-month lead (January initial condition) with a pattern correlation coefficient (PCC) skill of 0.96 and a temporal correlation coefficient (TCC) skill of 0.62. This long-lead predictability of the EOF1 comes mainly from the prolonged impacts of El Niño-Southern Oscillation (ENSO) as the EOF1 tends to occur during the summer after the mature phase of ENSO. The second EOF mode (EOF2), on the other hand, is related to the developing ENSO and also the interdecadal variability of the sea surface temperature over the North Pacific and North Atlantic Ocean. The MME also captures the EOF2 at a 5-month lead with a PCC skill of 0.87 and a TCC skill of 0.67, but these skills are mainly obtained from the zonally symmetric component of the EOF2, not the prominent wavelike structure, the so-called circumglobal teleconnection (CGT) pattern. In both observation and the 1-month lead MME prediction, the first two leading modes are accompanied by significant rainfall and surface air temperature anomalies in the continental regions of the NH extratropics. The MME’s success in predicting the EOF1 (EOF2) is likely to lead to a better prediction of JJA precipitation anomalies over East Asia and the North Pacific (central and southern Europe and western North America).     

Assessment of Erythemal UV Level in Nepal Based on Solar UV Estimates from Total Ozone Mapping Spectrometer

Nepal lies on the southern slope of Himalaya in Asia. In a width ranging between 150 and 250 km, the altitude varies greatly from about 100 m at its southern border to a maximum of 8848 min the northern part. Like the variation in altitude, climatic condition varies quite a lot. Long-term monthly mean erythemal UV daily dose values for Nepal are evaluated using Total Ozone Mapping Spectrometer (TOMS) estimation from the time of its overpass between 1996 and 2003. The results are presented as summer and winter maps of mean UV levels in each satellite grid. The mean winter erythemal UV daily dose ranges between 2.1 and 3.6 kJ m-2 whereas summer values are found to lie between 4.6 and 9.7 kJ m-2. The altitude variation increases the UV levels by about 0.2 kJ km-1 in winter months, and 0.9 kJ km-1 in summer. A multiyear monthly average erythemal daily dose in most of the areas shows that the summer value is about three times higher than that in winter. Although year-to-year variation is not pronounced in high- and mid-elevation regions, UV levels seemed to decrease from 1997 to 2002 in the southern part of the country in the low elevation region by about 5.35%. Due to the combined effects of the altitude, low ozone concentration in the troposphere, and thin air, surface UV radiation at higher altitudes is found to be higher than in the surrounding regions.     

Precipitable water over the tropical Indian Ocean derived from NIMBUS 7 satellite data — A case study

The data from the Scanning Multichannel Microwave Radiometer (SMMR) sensor on board the NIMBUS 7 satellite in the 18 and 21 Ghz channels are used to derive the Precipitable Water (PW) over the tropical Indian Ocean for the period May–September, 1979. The PW values are derived by using both the horizontal and vertical polarization channels. These PW values along with the satellite-derived sea surface temeprature (SST) and the lower tropospheric wind (UW) data reveal the features of the summer monsoon circulation of the tropical Indian Ocean in 1979. Further, the derived PW values from Nimbus 7 SMMR data agree well with features of PW presented by Cadet (1983) inferred from the TIROS—N soundings and FGGE data.     

An analysis of seasonal predictability in coupled model forecasts

In the recent decade, operational seasonal prediction systems based on initialized coupled models have been developed. An analysis of how the predictability of seasonal means in the initialized coupled predictions evolves with lead-time is presented. Because of the short lead-time, such an analysis for the temporal behavior of seasonal predictability involves a mix of both the predictability of the first and the second kind. The analysis focuses on the lead-time dependence of ensemble mean variance, and the forecast spread. Further, the analysis is for a fixed target season of December?CJanuary?CFebruary, and is for sea surface temperature, rainfall, and 200-mb height. The analysis is based on a large set of hindcasts from an initialized coupled seasonal prediction system. Various aspects of predictability of the first and the second kind are highlighted for variables with long (for example, SST), and fast (for example, atmospheric) adjustment time scale. An additional focus of the analysis is how the predictability in the initialized coupled seasonal predictions compares with estimates based on the AMIP simulations. The results indicate that differences in the set up of AMIP simulations and coupled predictions, for example, representation of air?Csea interactions, and evolution of forecast spread from initial conditions do not change fundamental conclusion about the seasonal predictability. A discussion of the analysis presented herein, and its implications for the use of AMIP simulations for climate attribution, and for time-slice experiments to provide regional information, is also included.     

An assessment of the surface climate in the NCEP climate forecast system reanalysis

This paper analyzes surface climate variability in the climate forecast system reanalysis (CFSR) recently completed at the National Centers for Environmental Prediction (NCEP). The CFSR represents a new generation of reanalysis effort with first guess from a coupled atmosphere?Cocean?Csea ice?Cland forecast system. This study focuses on the analysis of climate variability for a set of surface variables including precipitation, surface air 2-m temperature (T2m), and surface heat fluxes. None of these quantities are assimilated directly and thus an assessment of their variability provides an independent measure of the accuracy. The CFSR is compared with observational estimates and three previous reanalyses (the NCEP/NCAR reanalysis or R1, the NCEP/DOE reanalysis or R2, and the ERA40 produced by the European Centre for Medium-Range Weather Forecasts). The CFSR has improved time-mean precipitation distribution over various regions compared to the three previous reanalyses, leading to a better representation of freshwater flux (evaporation minus precipitation). For interannual variability, the CFSR shows improved precipitation correlation with observations over the Indian Ocean, Maritime Continent, and western Pacific. The T2m of the CFSR is superior to R1 and R2 with more realistic interannual variability and long-term trend. On the other hand, the CFSR overestimates downward solar radiation flux over the tropical Western Hemisphere warm pool, consistent with a negative cloudiness bias and a positive sea surface temperature bias. Meanwhile, the evaporative latent heat flux in CFSR appears to be larger than other observational estimates over most of the globe. A few deficiencies in the long-term variations are identified in the CFSR. Firstly, dramatic changes are found around 1998?C2001 in the global average of a number of variables, possibly related to the changes in the assimilated satellite observations. Secondly, the use of multiple streams for the CFSR induces spurious jumps in soil moisture between adjacent streams. Thirdly, there is an inconsistency in long-term sea ice extent variations over the Arctic regions between the CFSR and other observations with the CFSR showing smaller sea ice extent before 1997 and larger extent starting in 1997. These deficiencies may have impacts on the application of the CFSR for climate diagnoses and predictions. Relationships between surface heat fluxes and SST tendency and between SST and precipitation are analyzed and compared with observational estimates and other reanalyses. Global mean fields of surface heat and water fluxes together with radiation fluxes at the top of the atmosphere are documented and presented over the entire globe, and for the ocean and land separately.     

TropFlux wind stresses over the tropical oceans: evaluation and comparison with other products

In this paper, we present TropFlux wind stresses and evaluate them against observations along with other widely used daily air-sea momentum flux products (NCEP, NCEP2, ERA-I and QuikSCAT). TropFlux wind stresses are computed from the COARE v3.0 algorithm, using bias and amplitude corrected ERA-I input data and an additional climatological gustiness correction. The wind stress products are evaluated against dependent data from the TAO/TRITON, PIRATA and RAMA arrays and independent data from the OceanSITES mooring networks. Wind stress products are more consistent amongst each other than surface heat fluxes, suggesting that 10 m-winds are better constrained than near-surface thermodynamical parameters (2 m-humidity and temperature) and surface downward radiative fluxes. QuikSCAT overestimates wind stresses away from the equator, while NCEP and NCEP2 underestimate wind stresses, especially in the equatorial Pacific. QuikSCAT wind stress quality is strongly affected by rain under the Inter Tropical Convergence Zones. ERA-I and TropFlux display the best agreement with in situ data, with correlations >0.93 and rms-differences <0.012 Nm^?2. TropFlux wind stresses exhibit a small, but consistent improvement (at all timescales and most locations) over ERA-I, with an overall 17 % reduction in root mean square error. ERA-I and TropFlux agree best with long-term mean zonal wind stress observations at equatorial latitudes. All products tend to underestimate the zonal wind stress seasonal cycle by ~20 % in the western and central equatorial Pacific. TropFlux and ERA-I equatorial zonal wind stresses have clearly the best phase agreement with mooring data at intraseasonal and interannual timescales (correlation of ~0.9 versus ~0.8 at best for any other product), with TropFlux correcting the ~13 % underestimation of ERA-I variance at both timescales. For example, TropFlux was the best at reproducing westerly wind bursts that played a key role in the 1997–1998 El Niño onset. Hence, we recommend the use of TropFlux for studies of equatorial ocean dynamics.     

Impacts of enhanced CCN on the organization of convection and recent reduced counts of monsoon depressions

Monsoon depressions, that form during the Indian summer monsoon season (June to September) are known to be baroclinic disturbances (horizontal scale 2,000–3,000 km) and are driven by deep convection that carries a very large vertical slope towards cold air aloft in the upper troposphere. Deep convection is nearly always organized around the scale of these depressions. In the maintenance of the monsoon depression the generation of eddy kinetic energy on the scale of the monsoon depression is largely governed by the “in scale” covariance of heating and temperature and of vertical velocity and temperature over the region of the monsoon depression. There are normally about 6–8 monsoon depressions during a summer monsoon season. Recent years 2009, 2010 and 2011 saw very few (around 1, 0 and 1 per season respectively). The best numerical models such as those from ECMWF and US (GFS) carried many false alarms in their 3–5 day forecasts, more like 6–8 disturbances. Even in recent years with fewer observed monsoon depressions a much larger number of depressions is noted in ECMWF forecasts. These are fairly comprehensive models that carry vast data sets (surface and satellite based), detailed data assimilation, and are run at very high resolutions. The monsoon depression is well resolved by these respective horizontal resolutions in these models (at 15 and 35 km). These models carry complete and detailed physical parameterizations. The false alarms in their forecasts leads us to suggest that some additional important ingredient may be missing in these current best state of the art models. This paper addresses the effects of pollution for the enhancement of cloud condensation nuclei and the resulting disruption of the organization of convection in monsoon depressions. Our specific studies make use of a high resolution mesoscale model (WRF/CHEM) to explore the impacts of the first and second aerosol indirect effects proposed by Twomey and Albrecht. We have conducted preliminary studies including examination of the evolution of radar reflectivity (computed inversely from the model hydrometeors) for normal and enhanced CCN effects (arising from enhanced monsoon pollution). The time lapse histories show a major disruption in the organization of convection of the monsoon depressions on the time scale of a week to 10 days in these enhanced CCN scenarios.     

A verification framework for interannual-to-decadal predictions experiments

Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.     

Spatial and temporal variation in daily temperature indices in summer and winter seasons over India (1969–2012)

Spatial and temporal variations in summer and winter extreme temperature indices are studied by using daily maximum and minimum temperatures data from 227 surface meteorological stations well distributed over India for the period 1969–2012. For this purpose, time series for six extreme temperature indices namely, hot days (HD), very hot days (VHD), extremely hot days (EHD), cold nights (CN), very cold nights (VCN), and extremely cold nights (ECN) are calculated for all the stations. In addition, time series for mean extreme temperature indices of summer and winter seasons are also analyzed. Study reveals high variability in spatial distribution of threshold temperatures of extreme temperature indices over the country. In general, increasing trends are observed in summer hot days indices and decreasing trends in winter cold night indices over most parts of the country. The results obtained in this study indicate warming in summer maximum and winter minimum temperatures over India. Averaged over India, trends in summer hot days indices HD, VHD, and EHD are significantly increasing (+1.0, +0.64, and +0.32 days/decade, respectively) and winter cold night indices CN, VCN, and ECN are significantly decreasing (−0.93, −0.47, and −0.15 days/decade, respectively). Also, it is observed that the impact of extreme temperature is higher along the west coast for summer and east coast for winter.

     

Integrated approach for demarcating the fracture zone for well site location : A case study near Gumla and Lohardaga,Bihar

In recent years satellite remote sensing techniques have greatly aided identification of fractures/joints/faults in connection with groundwater exploration. However, due to some constraints in filtering out the type of fracture i.e. shear fracture, tensional fracture, mineralised fracture etc. as to their “open or closed” nature from groundwater aspects, there is need of adding geophysical survey, particularly resistivity survey to further enhance the accuracy, and hence minimise drilling failures. The area under study is a part of Ranchi plateau and lithologically comprising of granite-gneiss. The present paper lays stress on an integrated approach for localising well sites through satellite data analysis and resistivity profiling along with vertical electrical sounding which is based on fracture patterns. With this combined effort i.e. firstly considering the surfacial features like drainage, geomorpho-logy, lithology and lineaments, and secondly taking into account geophysical survey aspects, i.e. depth and thickness of fracture zone, lateral extent, different layer parameter, etc., fairly accurate results are achieved. In the presnt study this type of an integrated survey has been carried out in Lohardaga and Gumla districts in Bihar, the results of which are highlighted in the paper.