Elisa Palazzi

Precipitation in the Hindu‐Kush Karakoram Himalaya: Observations and future scenarios

We study the properties of precipitation in the Hindu-Kush Karakoram Himalaya (HKKH) region using currently available data sets. We consider satellite rainfall estimates (Tropical Rainfall Measuring Mission), reanalyses (ERA-Interim), gridded in situ rain gauge data (Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources, Climate Research Unit, and Global Precipitation Climatology Centre), and a merged satellite and rain gauge climatology (Global Precipitation Climatology Project). The data are compared with simulation results from the global climate model EC-Earth. All data sets, despite having different resolutions, coherently reproduce the mean annual cycle of precipitation in the western and eastern stretches of the HKKH. While for the Himalaya only a strong summer precipitation signal is present, associated with the monsoon, the data indicate that the Hindu-Kush Karakoram, which is exposed to midlatitude qwestern weather patternsq, receives water inputs in winter. Time series of seasonal precipitation confirm that the various data sets provide a consistent measurement of interannual variability for the HKKH. The longest observational data sets indicate a statistically significant decreasing trend in Himalaya during summer. None of the data sets gives statistically significant precipitation trends in Hindu-Kush Karakoram during winter. Precipitation data from EC-Earth are in good agreement with the climatology of the observations (rainfall distribution and seasonality). The evolution of precipitation under two different future scenarios (RCP 4.5 and RCP 8.5) reveals an increasing trend over the Himalaya during summer, associated with an increase in wet extremes and daily intensity and a decrease in the number of rainy days. Unlike the observations, the model shows an increasing precipitation trend also in the period 1950-2009, possibly as a result of the poor representation of aerosols in this type of GCMs. Citation: Palazzi, E., J. von Hardenberg, and A. Provenzale (2013), Precipitation in the Hindu-Kush Karakoram Himalaya: Observations and future scenarios, J. Geophys. Res. Atmos., 118, 85-100, doi: 10.1029/2012JD018697.

Precipitation in the Karakoram-Himalaya: a CMIP5 view

This work analyzes the properties of precipitation in the Hindu-Kush Karakoram Himalaya region as simulated by thirty-two state-of-the-art global climate models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5). We separately consider the Hindu-Kush Karakoram (HKK) in the west and the Himalaya in the east. These two regions are characterized by different precipitation climatologies, which are associated with different circulation patterns. Historical model simulations are compared with the Climate Research Unit (CRU) and Global Precipitation Climatology Centre (GPCC) precipitation data in the period 1901–2005. Future precipitation is analyzed for the two representative concentration pathways (RCP) RCP 4.5 and RCP 8.5 scenarios. We find that the multi-model ensemble mean and most individual models exhibit a wet bias with respect to CRU and GPCC observations in both regions and for all seasons. The models differ greatly in the seasonal climatology of precipitation which they reproduce in the HKK. The CMIP5 models predict wetter future conditions in the Himalaya in summer, with a gradual precipitation increase throughout the 21st century. Wetter summer future conditions are also predicted by most models in the RCP 8.5 scenario for the HKK, while on average no significant change can be detected in winter precipitation for both regions. In general, no single model (or group of models) emerges as that providing the best results for all the statistics considered, and the large spread in the behavior of individual models suggests to consider multi-model ensemble means with extreme care.

Observed climate change hotspots

We quantify climate change hotspots from observations, taking into account the differences in precipitation and temperature statistics (mean, variability, and extremes) between 1981–2010 and 1951–1980. Areas in the Amazon, the Sahel, tropical West Africa, Indonesia, and central eastern Asia emerge as primary observed hotspots. The main contributing factors are the global increase in mean temperatures, the intensification of extreme hot-season occurrence in low-latitude regions and the decrease of precipitation over central Africa. Temperature and precipitation variability have been substantially stable over the past decades, with only a few areas showing significant changes against the background climate variability. The regions identified from the observations are remarkably similar to those defined from projections of global climate models under a “business-as-usual” scenario, indicating that climate change hotspots are robust and persistent over time. These results provide a useful background to develop global policy decisions on adaptation and mitigation priorities over near-time horizons.

Snowpack Changes in the Hindu Kush–Karakoram–Himalaya from CMIP5 Global Climate Models

AbstractThe Hindu Kush, Karakoram, and Himalaya (HKKH) mountain ranges feed the most important Asian river systems, providing water to about 1.5 billion people. As a consequence, changes in snow dynamics in this area could severely impact water availability for downstream populations. Despite their importance, the amount, spatial distribution, and seasonality of snow in the HKKH region are still poorly known, owing to the limited availability of surface observations in this remote and high-elevation area. This work considers global climate models (GCM) participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5) and analyzes how they represent current and future snowpack in the HKKH region in terms of snow depth and snow water equivalent. It is found that models with high spatial resolution (up to 1.25°) simulate a spatial pattern of the winter snowpack in greater agreement with each other, with observations, with reanalysis datasets, and with the orographic features of the region, compar...

Multidecadal Variations in the Relationship between the NAO and Winter Precipitation in the Hindu Kush–Karakoram

AbstractWinter precipitation over the Hindu Kush–Karakoram (HKK) range in the western Himalayas is generated by westerly perturbations whose dynamics is affected by the North Atlantic Oscillation (NAO). Larger precipitation is typically recorded during the positive NAO phase. In this work, the relationship between the NAO and winter precipitation in the HKK is explored further, using an ensemble of precipitation datasets and the 40-yr ECMWF Re-Analysis (ERA-40) and Twentieth Century Reanalysis (20CR) data. The mechanisms underlying this relationship are discussed, with a focus on the secular variations that occurred in the last century. The NAO exerts its control on HKK precipitation by altering the intensity of westerly winds in the region of the Middle East jet stream (MEJS). Results indicate that evaporation from the Persian Gulf, the northern Arabian Sea, and the Red Sea plays an important role. During positive NAO phases, westerlies are strengthened and enhanced evaporation occurs from these basins o...

MOCRA: a Monte Carlo code for the simulation of radiative transfer in the atmosphere

This paper describes the radiative transfer model (RTM) MOCRA (MOnte Carlo Radiance Analysis), developed in the frame of DOAS (Differential Optical Absorption Spectroscopy) to correctly interpret remote sensing measurements of trace gas amounts in the atmosphere through the calculation of the Air Mass Factor. Besides the DOAS-related quantities, the MOCRA code yields: 1- the atmospheric transmittance in the vertical and sun directions, 2- the direct and global irradiance, 3- the single- and multiple- scattered radiance for a detector with assigned position, line of sight and field of view. Sample calculations of the main radiometric quantities calculated with MOCRA are presented and compared with the output of another RTM (MODTRAN4). A further comparison is presented between the NO2 slant column densities (SCDs) measured with DOAS at Evora (Portugal) and the ones simulated with MOCRA. Both comparisons (MOCRA-MODTRAN4 and MOCRA-observations) gave more than satisfactory results, and overall make MOCRA a versatile tool for atmospheric radiative transfer simulations and interpretation of remote sensing measurements.

Stochastic Rainfall Downscaling of Climate Models

AbstractPrecipitation extremes and small-scale variability are essential drivers in many climate change impact studies. However, the spatial resolution currently achieved by global climate models (GCMs) and regional climate models (RCMs) is still insufficient to correctly identify the fine structure of precipitation intensity fields. In the absence of a proper physically based representation, this scale gap can be at least temporarily bridged by adopting a stochastic rainfall downscaling technique. In this work, a precipitation downscaling chain is introduced where the global 40-yr ECMWF Re-Analysis (ERA-40) (at about 120-km resolution) is dynamically downscaled using the Protheus RCM at 30-km resolution. The RCM precipitation is then further downscaled using a stochastic downscaling technique, the Rainfall Filtered Autoregressive Model (RainFARM), which has been extended for application to long climate simulations. The application of the stochastic downscaling technique directly to the larger-scale reana...

Insights into elevation-dependent warming in the Tibetan Plateau-Himalayas from CMIP5 model simulations

AbstractWe use the output of twenty-seven Global Climate Models participating in the Coupled Model Intercomparison Project phase 5 (CMIP5) to investigate the temperature changes and their dependence on the elevation in the Tibetan Plateau, Himalaya and Karakoram mountains and in the surrounding areas in historical model simulations and in future projections. The aim of this study is to explore if and to what extent the CMIP5 models show elevation-dependent warming (EDW) in this part of the globe and to investigate what are the driving factors at play and their relative importance. Our results indicate that the models show enhanced rates of warming at higher elevations in the Tibetan Plateau-Himalayan region in the twentieth century, and this phenomenon is projected to strengthen by the end of the twenty-first century under a high-emission scenario. We find a nonlinear relationship between the warming rates and the elevation, for both the minimum and the maximum temperature: regions with temperatures below the freezing level of water show more warming than the regions with temperatures above, likely suggesting a key role of mechanisms involving water phase changes, the presence/absence of snow and the snow-albedo feedback. We consider the main variables simulated by the CMIP5 models whose change may be related to temperature changes at higher elevations. We find that changes in surface albedo, atmospheric humidity and downward longwave radiation are relevant factors for EDW in the Tibetan Plateau-Himalayas, with surface albedo being the leading driver.

Retrieval of Gas Pollutants Vertical Profile in the Boundary Layer by Means of Multiple-Axis DOAS

This paper presents a methodology for the retrieval of the vertical profile of atmospheric gas pollutants in the boundary layer from ground-based remote sensing measurements. Nitrogen dioxide (NO2) and ozone (O3) slant column amounts were obtained with the differential optical absorption spectroscopy (DOAS) technique used in the multiple-axis configuration (referred to as multiaxis DOAS). The measurements were carried out in the Presidential Estate at Castel Porziano (near Rome) from September to November 2006, within a program started in 1994 for studying and monitoring the Estates environment. The retrieval of information on trace gas vertical profiles from the slant column amounts requires as follows: (1) the simulation of the radiative transfer in the atmosphere for air mass factor calculation and (2) the application of inversion schemes. This paper illustrates and discusses the vertical profiles of NO2 and O3 obtained from multiaxis DOAS measurements and their daily evolution on October 29, 2006.

Assessing Climate Change Risks Under Uncertain Conditions

Climate and environmental change is expected to affect hydrometeorological hazard and ecosystem functioning, with possible threats to human societies due to increased probability of extreme events and loss of ecosystem services. In mountain regions, the environmental response could be even larger. For this reason, it is important to obtain estimates of the expected modifications in natural hazards associated with climate and environmental change, to develop appropriate adaptation and risk mitigation strategies. This goal, however, is made difficult by the scale mismatch between climate model projections and land surface response, which requires the use of appropriate climate downscaling procedures. To complicate the picture, one should also cope with the chain of uncertainties which affect climate and risk projections, from the wide range of global climate model estimates for the water cycle variables, to the uncertainties in regional climate response, to the uncertainties in the hydrological and/or ecosystem models themselves. Precipitation data used to validate the models, on the other hand, are also affected by severe uncertainties, especially in mountain regions. This leads to the general problem of assessing natural hazards for different climate and environmental change scenarios under uncertain conditions.