Sensitivity of terrestrial water and energy budgets to CO(2)-physiological forcing: an investigation using an offline land model

Increasing concentrations of atmospheric carbon dioxide (CO(2)) influence climate by suppressing canopy transpiration in addition to its well- known greenhouse gas effect. The decrease in plant transpiration is due to changes in plant physiology (reduced opening of plant stomata). Here, we quantify such changes in water flux for various levels of CO(2) concentrations using the National Center for Atmospheric Research's (NCAR) Community Land Model. We find that photosynthesis saturates after 800 ppmv (parts per million, by volume) in this model. However, unlike photosynthesis, canopy transpiration continues to decline at about 5.1% per 100 ppmv increase in CO(2) levels. We also find that the associated reduction in latent heat flux is primarily compensated by increased sensible heat flux. The continued decline in canopy transpiration and subsequent increase in sensible heat flux at elevated CO(2) levels implies that incremental warming associated with the physiological effect of CO(2) will not abate at higher CO(2) concentrations, indicating important consequences for the global water and carbon cycles from anthropogenic CO(2) emissions.

Monsoon sensitivity to aerosol direct radiative forcing in the community atmosphere model

Aerosol forcing remains a dominant uncertainty in climate studies. The impact of aerosol direct radiative forcing on Indian monsoon is extremely complex and is strongly dependent on the model, aerosol distribution and characteristics specified in the model, modelling strategy employed as well as on spatial and temporal scales. The present study investigates (i) the aerosol direct radiative forcing impact on mean Indian summer monsoon when a combination of quasi-realistic mean annual cycles of scattering and absorbing aerosols derived from an aerosol transport model constrained with satellite observed Aerosol Optical Depth (AOD) is prescribed, (ii) the dominant feedback mechanism behind the simulated impact of all-aerosol direct radiative forcing on monsoon and (iii) the relative impacts of absorbing and scattering aerosols on mean Indian summer monsoon. We have used CAM3, an atmospheric GCM (AGCM) that has a comprehensive treatment of the aerosol-radiation interaction. This AGCM has been used to perform climate simulations with three different representations of aerosol direct radiative forcing due to the total, scattering aerosols and black carbon aerosols. We have also conducted experiments without any aerosol forcing. Aerosol direct impact due to scattering aerosols causes significant reduction in summer monsoon precipitation over India with a tendency for southward shift of Tropical Convergence Zones (TCZs) over the Indian region. Aerosol forcing reduces surface solar absorption over the primary rainbelt region of India and reduces the surface and lower tropospheric temperatures. Concurrent warming of the lower atmosphere over the warm oceanic region in the south reduces the land-ocean temperature contrast and weakens the monsoon overturning circulation and the advection of moisture into the landmass. This increases atmospheric convective stability, and decreases convection, clouds, precipitation and associated latent heat release. Our analysis reveals a defining negative moisture-advection feedback that acts as an internal damping mechanism spinning down the regional hydrological cycle and leading to significant circulation changes in response to external radiative forcing perturbations. When total aerosol loading (both absorbing and scattering aerosols) is prescribed, dust and black carbon aerosols are found to cause significant atmospheric heating over the monsoon region but the aerosol-induced weakening of meridional lower tropospheric temperature gradient (leading to weaker summer monsoon rainfall) more than offsets the increase in summer-time rainfall resulting from the atmospheric heating effect of absorbing aerosols, leading to a net decrease of summer monsoon rainfall. Further, we have carried out climate simulations with globally constant AODs and also with the constant AODs over the extended Indian region replaced by realistic AODs. Regional aerosol radiative forcing perturbations over the Indian region is found to have impact not only over the region of loading but over remote tropical regions as well. This warrants the need to prescribe realistic aerosol properties in strategic regions such as India in order to accurately assess the aerosol impact.

Climate response to changes in atmospheric carbon dioxide and solar irradiance on the time scale of days to weeks

Recent studies show that fast climate response on time scales of less than a month can have important implications for long-term climate change. In this study, we investigate climate response on the time scale of days to weeks to a step-function quadrupling of atmospheric CO2 and contrast this with the response to a 4% increase in solar irradiance. Our simulations show that significant climate effects occur within days of a stepwise increase in both atmospheric CO2 content and solar irradiance. Over ocean, increased atmospheric CO2 warms the lower troposphere more than the surface, increasing atmospheric stability, moistening the boundary layer, and suppressing evaporation and precipitation. In contrast, over ocean, increased solar irradiance warms the lower troposphere to a much lesser extent, causing a much smaller change in evaporation and precipitation. Over land, both increased CO2 and increased solar irradiance cause rapid surface warming that tends to increase both evaporation and precipitation. However, the physiological effect of increased atmospheric CO2 on plant stomata reduces plant transpiration, drying the boundary layer and decreasing precipitation. This effect does not occur with increased solar irradiance. Therefore, differences in climatic effects from CO2 versus solar forcing are manifested within days after the forcing is imposed.

Local and remote impacts of direct aerosol forcing on Asian monsoon

The impact of heating by black carbon aerosols on Indian summer monsoon has remained inconclusive. Some investigators have predicted that black carbon aerosols reduce monsoon rainfall while others have argued that it will increase monsoon rainfall. These conclusions have been based on local influence of aerosols on the radiative fluxes. The impact of aerosol-like heating in one region on the rainfall in a remote region has not been examined in detail. Here, using an atmospheric general circulation model, it has been shown that remote influence of aerosol-like heating can be as important as local influence on Indian summer monsoon. Precipitation in northern Arabian Sea and north-west Indian region increased by 16% in June to July when aerosol-like heating were present globally. The corresponding increase in precipitation due to presence of aerosol-like heating only over South Asia (local impact) and East Asia (remote impact) were 28 and 13%, respectively. This enhancement in precipitation was due to destabilization of the atmosphere in pre-monsoon season that affected subsequent convection. Moreover, pre-monsoon heating of the lower troposphere changed the circulation substantially that enabled influx of more moisture over certain regions and reduced the moist static stability of the atmosphere. It has been shown that regional aerosol heating can have large impact on the phase of upper tropospheric Rossby wave in pre-monsoon season, which acts as a primary mechanism behind teleconnection and leads to the change in precipitation during monsoon season. These results demonstrate that changes in aerosol in one region can influence the precipitation in a remote region through changes in circulation.

Sensitivity of simulated climate to latitudinal distribution of solar insolation reduction in solar radiation management

Solar radiation management (SRM) geoengineering has been proposed as a potential option to counteract climate change. We perform a set of idealized geoengineering simulations using Community Atmosphere Model version 3.1 developed at the National Center for Atmospheric Research to investigate the global hydrological implications of varying the latitudinal distribution of solar insolation reduction in SRM methods. To reduce the solar insolation we have prescribed sulfate aerosols in the stratosphere. The radiative forcing in the geoengineering simulations is the net forcing from a doubling of CO2 and the prescribed stratospheric aerosols. We find that for a fixed total mass of sulfate aerosols (12.6 Mt of SO4), relative to a uniform distribution which nearly offsets changes in global mean temperature from a doubling of CO2, global mean radiative forcing is larger when aerosol concentration is maximum at the poles leading to a warmer global mean climate and consequently an intensified hydrological cycle. Opposite changes are simulated when aerosol concentration is maximized in the tropics. We obtain a range of 1 K in global mean temperature and 3% in precipitation changes by varying the distribution pattern in our simulations: this range is about 50% of the climate change from a doubling of CO2. Hence, our study demonstrates that a range of global mean climate states, determined by the global mean radiative forcing, are possible for a fixed total amount of aerosols but with differing latitudinal distribution. However, it is important to note that this is an idealized study and thus not all important realistic climate processes are modeled.

Impact of diurnal forcing on intraseasonal sea surface temperature oscillations in the Bay of Bengal

The diurnal cycle is an important mode of sea surface temperature (SST) variability in tropical oceans, influencing air-sea interaction and climate variability. Upper ocean mixing mechanisms are significant at diurnal time scales controlling the intraseasonal variability (ISV) of SST. Sensitivity experiments using an Ocean General Circulation Model (OGCM) for the summer monsoon of the year 2007 show that incorporation of diurnal cycle in the model atmospheric forcings improves the SST simulation at both intraseasonal and shorter time scales in the Bay of Bengal (BoB). The increase in SST-ISV amplitudes with diurnal forcing is approximate to 0.05 degrees C in the southern bay while it is approximate to 0.02 degrees C in the northern bay. Increased intraseasonal warming with diurnal forcing results from the increase in mixed layer heat gain from insolation, due to shoaling of the daytime mixed layer. Amplified intraseasonal cooling is dominantly controlled by the strengthening of subsurface processes owing to the nocturnal deepening of mixed layer. In the southern bay, intraseasonal variability is mainly determined by the diurnal cycle in insolation, while in the northern bay, diurnal cycle in insolation and winds have comparable contributions. Temperature inversions (TI) develop in the northern bay in the absence of diurnal variability in wind stress. In the northern bay, SST-ISV amplification is not as large as that in the southern bay due to the weaker diurnal variability of mixed layer depth (MLD) limited by salinity stratification. Diurnal variability of model MLD is not sufficient to create large modifications in mixed layer heat budget and SST-ISV in the northern bay.

Climate change and forests in India

Forests play a critical role in addressing climate change concerns in the broader context of global change and sustainable development. Forests are linked to climate change in three ways. i) Forests are a source of greenhouse gas (GHG) emissions: ii) Forests offer mitigation opportunities to stabilise GHG concentrations: iii) Forests are impacted by climate change. This paper reviews studies related to climate change and forests in India: first, the studies estimating carbon inventory for the Indian land use change and forestry sector (LUCF), then the different models and mitigation potential estimates for the LUCF sector in India. Finally it reviews the studies on the impact of climate change on forest ecosystems in India, identifying the implications for net primary productivity and bio-diversity. The paper highlights data, modelling and research gaps relevant to the GHG inventory, mitigation potential and vulnerability and impact assessments for the forest sector in India.

Problems with geoengineering schemes to combat climate change

The accelerated rate of increase in atmospheric CO2 concentration in recent years has revived the idea of stabilizing the global climate through geoengineering schemes. Majority of the proposed geoengineering schemes will attempt to reduce the amount of solar radiation absorbed by our planet. Climate modelling studies of these so called 'sunshade geoengineering schemes' show that global warming from increasing concentrations of CO2 can be mitigated by intentionally manipulating the amount of sunlight absorbed by the climate system. These studies also suggest that the residual changes could be large on regional scales, so that climate change may not be mitigated on a local basis. More recent modelling studies have shown that these schemes could lead to a slow-down in the global hydrological cycle. Other problems such as changes in the terrestrial carbon cycle and ocean acidification remain unsolved by sunshade geoengineering schemes. In this article, I review the proposed geoengineering schemes, results from climate models and discuss why geoengineering is not the best option to deal with climate change.

Regional Differences in the Influence of Irrigation on Climate

A global climate model experiment is performed to evaluate the effect of irrigation on temperatures in several major irrigated regions of the world. The Community Atmosphere Model, version 3.3, was modified to represent irrigation for the fraction of each grid cell equipped for irrigation according to datasets from the Food and Agriculture Organization. Results indicate substantial regional differences in the magnitude of irrigation-induced cooling, which are attributed to three primary factors: differences in extent of the irrigated area, differences in the simulated soil moisture for the control simulation (without irrigation), and the nature of cloud response to irrigation. The last factor appeared especially important for the dry season in India, although further analysis with other models and observations are needed to verify this feedback. Comparison with observed temperatures revealed substantially lower biases in several regions for the simulation with irrigation than for the control, suggesting that the lack of irrigation may be an important component of temperature bias in this model or that irrigation compensates for other biases. The results of this study should help to translate the results from past regional efforts, which have largely focused on the United States, to regions in the developing world that in many cases continue to experience significant expansion of irrigated land.

Climate change impact assessment: Uncertainty modeling with imprecise probability

Hydrologic impacts of climate change are usually assessed by downscaling the General Circulation Model (GCM) output of large-scale climate variables to local-scale hydrologic variables. Such an assessment is characterized by uncertainty resulting from the ensembles of projections generated with multiple GCMs, which is known as intermodel or GCM uncertainty. Ensemble averaging with the assignment of weights to GCMs based on model evaluation is one of the methods to address such uncertainty and is used in the present study for regional-scale impact assessment. GCM outputs of large-scale climate variables are downscaled to subdivisional-scale monsoon rainfall. Weights are assigned to the GCMs on the basis of model performance and model convergence, which are evaluated with the Cumulative Distribution Functions (CDFs) generated from the downscaled GCM output (for both 20th Century [20C3M] and future scenarios) and observed data. Ensemble averaging approach, with the assignment of weights to GCMs, is characterized by the uncertainty caused by partial ignorance, which stems from nonavailability of the outputs of some of the GCMs for a few scenarios (in Intergovernmental Panel on Climate Change [IPCC] data distribution center for Assessment Report 4 [AR4]). This uncertainty is modeled with imprecise probability, i.e., the probability being represented as an interval gray number. Furthermore, the CDF generated with one GCM is entirely different from that with another and therefore the use of multiple GCMs results in a band of CDFs. Representing this band of CDFs with a single valued weighted mean CDF may be misleading. Such a band of CDFs can only be represented with an envelope that contains all the CDFs generated with a number of GCMs. Imprecise CDF represents such an envelope, which not only contains the CDFs generated with all the available GCMs but also to an extent accounts for the uncertainty resulting from the missing GCM output. This concept of imprecise probability is also validated in the present study. The imprecise CDFs of monsoon rainfall are derived for three 30-year time slices, 2020s, 2050s and 2080s, with A1B, A2 and B1 scenarios. The model is demonstrated with the prediction of monsoon rainfall in Orissa meteorological subdivision, which shows a possible decreasing trend in the future.

A conditional random field-based downscaling method for assessment of climate change impact on multisite daily precipitation in the Mahanadi basin

Downscaling to station-scale hydrologic variables from large-scale atmospheric variables simulated by general circulation models (GCMs) is usually necessary to assess the hydrologic impact of climate change. This work presents CRF-downscaling, a new probabilistic downscaling method that represents the daily precipitation sequence as a conditional random field (CRF). The conditional distribution of the precipitation sequence at a site, given the daily atmospheric (large-scale) variable sequence, is modeled as a linear chain CRF. CRFs do not make assumptions on independence of observations, which gives them flexibility in using high-dimensional feature vectors. Maximum likelihood parameter estimation for the model is performed using limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization. Maximum a posteriori estimation is used to determine the most likely precipitation sequence for a given set of atmospheric input variables using the Viterbi algorithm. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework. The model is used to project the future cumulative distribution function of precipitation. Uncertainty in precipitation prediction is addressed through a modified Viterbi algorithm that predicts the n most likely sequences. The model is applied for downscaling monsoon (June-September) daily precipitation at eight sites in the Mahanadi basin in Orissa, India, using the MIROC3.2 medium-resolution GCM. The predicted distributions at all sites show an increase in the number of wet days, and also an increase in wet day precipitation amounts. A comparison of current and future predicted probability density functions for daily precipitation shows a change in shape of the density function with decreasing probability of lower precipitation and increasing probability of higher precipitation.

Climate-change and agriculture - an indian perspective

In this paper, we examine the major predictions made so far regarding the nature of climate change and its impacts on our region in the light of the known errors of the set of models and the observations over this century. The major predictions of the climate models about the impact of increased concentration of greenhouse gases ave at variance with the observations over the Indian region during the last century characterized by such increases and global warming. It is important to note that as far as the Indian region is concerned, the impact of year-to-year variation of the monsoon will continue to be dominant over longer period changes even in the presence of global warming. Recent studies have also brought out the uncertainties in the yields simulated by crop models. It is suggested that a deeper understanding of the links between climate and agricultural productivity is essential for generating reliable predictions of impact of climate change. Such an insight is also required for identifying cropping patterns and management practices which are tailored for sustained maximum yield in the face of the vagaries of the monsoon.

Carbon sequestration versus bioenergy: A case study from South India exploring the relative land-use efficiency of two options for climate change mitigation

This case study has been carried out as a comparison between two different land-use strategies for climate change mitigation, with possible application within the Clean Development Mechanisms. The benefits of afforestation for carbon sequestration versus for bioenergy production are compared in the context of development planning to meet increasing domestic and agricultural demand for electricity in Hosahalli village, Karnataka, India. One option is to increase the local biomass based electricity generation, requiring an increased biomass plantation area. This option is compared with fossil based electricity generation where the area is instead used for producing wood for non-energy purposes while also sequestering carbon in the soil and standing biomass. The different options have been assessed using the PRO-COMAP model. The ranking of the different options varies depending on the system boundaries and time period. Results indicate that, in the short term (30 years) perspective, the mitigation potential of the long rotation plantation is largest, followed by the short rotation plantation delivering wood for energy. The bioenergy option is however preferred if a long-term view is taken. Short rotation forests delivering wood for short-lived non-energy products have the smallest mitigation potential, unless a large share of the wood products are used for energy purposes (replacing fossil fuels) after having served their initial purpose. If managed in a sustainable manner all of these strategies can contribute to the improvement of the social and environmental situation of the local community. (C) 2009 Elsevier Ltd. All rights reserved.