No abstract

Terrestrial ecosystems regulate climate through both biogeochemical (greenhouse-gas regulation) and biophysical (regulation of water and energy) mechanisms 1,2 . However, policies aimed at climate protection through land management, including REDD+ (where REDD is Reducing Emissions from Deforestation and Forest Degradation) 3 and bioenergy sustainability standards 4 , account only for biogeochemical mechanisms. By ignoring biophysical processes, which sometimes offset biogeochemical effects 5,6 , policies risk promoting suboptimal solutions 1,2,4,7-10 . Here, we quantify how biogeochemical 11 and biophysical processes combine to shape the climate regulation values of 18 natural and agricultural ecoregions across the Americas. Natural ecosystems generally had higher climate regulation values than agroecosystems, largely driven by differences in biogeochemical services. Biophysical contributions ranged from minimal to dominant. They were highly variable in space, and their relative importance varied with the spatio-temporal scale of analysis. Our findings reinforce the importance of protecting tropical forests 7,10,12,13 , show that northern forests have a relatively small net effect on climate 5,10,13 , and indicate that climatic effects of bioenergy production may be more positive when biophysical processes are considered 14,15 . Ensuring effective climate protection through land management requires consideration of combined biogeochemical and biophysical processes 7,8 . Our climate regulation value index serves as one potential approach to quantify the full climate services of terrestrial ecosystems.Anthropogenic land use has been, and will continue to be, a major driver of the climate system 6,[16][17][18] . In terms of biogeochemical drivers, land-use change and agriculture together account for over 25% of global greenhouse-gas (GHG) emissions 19 . From 1990 to 2007, gross CO 2 emissions from tropical deforestation were equal to ∼40% of global fossil fuel emissions 18 . In recent years, agriculture has contributed ∼14% of total global GHG emissions 19,20 .Terrestrial ecosystems also strongly affect climate through their control over albedo and evapotransipiration 5,6,8,16,21,22 . Vegetated surfaces-especially forests-typically have lower albedos than bare ground and therefore absorb more incoming solar radiation. The reduction in net radiation (R n ) associated with deforestation has a cooling effect on the climate 5,22,23 -sometimes even outweighing GHG-induced warming 5,18 . Counteracting this, clearing vegetation reduces evapotranspiration and associated latent heat flux (LE). Without the vegetation, energy normally used to evaporate water instead heats the land surface 6,8,14,22,23 . Understanding the counteracting effects of R n and LE is key to quantifying the climate regulation values (CRVs) of different ecosystems 1 .Policies that affect land use may serve as one effective strategy contributing to climate change mitigation 2,12 or may inadvertently exacerbate the problem 24 . Major national and intern...

Forests, through the regulation of regional water balances, provide a number of ecosystem services, including water for agriculture, hydroelectric power generation, navigation, industry, fisheries, and human consumption. Large-scale deforestation triggers complex non-linear interactions between the atmosphere and biosphere, which may impair such important ecosystem services. This is the case for the Southwestern Amazon, where three important river basins (Juruá, Purus, and Madeira) are undergoing significant land-use changes. Here, we investigate the deforestation on river discharge are scale-dependent and vary across and within river basins. Reduction in precipitation due to deforestation is most severe at the end of the dry season. As a result, deforestation increases the dry-season length and the seasonal amplitude of water flow. These effects may aggravate the economic losses from large droughts and floods, such as those experienced in recent years (

Regional Climate Model version 3 (RegCM3) simulations of 17 summers (1988–2004) over part of South America south of 5°S were evaluated to identify model systematic errors. Model results were compared to different rainfall data sets (Climate Research Unit (CRU), Climate Prediction Center (CPC), Global Precipitation Climatology Project (GPCP), and National Centers for Environmental Prediction (NCEP) reanalysis), including the five summers mean (1998–2002) precipitation diurnal cycle observed by the Tropical Rainfall Measuring Mission (TRMM)‐Precipitation Radar (PR). In spite of regional differences, the RegCM3 simulates the main observed aspects of summer climatology associated with the precipitation (northwest‐southeast band of South Atlantic Convergence Zone (SACZ)) and air temperature (warmer air in the central part of the continent and colder in eastern Brazil and the Andes Mountains). At a regional scale, the main RegCM3 failures are the underestimation of the precipitation in the northern branch of the SACZ and some unrealistic intense precipitation around the Andes Mountains. However, the RegCM3 seasonal precipitation is closer to the fine‐scale analyses (CPC, CRU, and TRMM‐PR) than is the NCEP reanalysis, which presents an incorrect north‐south orientation of SACZ and an overestimation of its intensity. The precipitation diurnal cycle observed by TRMM‐PR shows pronounced contrasts between Tropics and Extratropics and land and ocean, where most of these features are simulated by RegCM3. The major similarities between the simulation and observation, especially the diurnal cycle phase, are found over the continental tropical and subtropical SACZ regions, which present afternoon maximum (1500–1800 UTC) and morning minimum (0900–1200 UTC). More specifically, over the core of SACZ, the phase and amplitude of the simulated precipitation diurnal cycle are very close to the TRMM‐PR observations. Although there are amplitude differences, the RegCM3 simulates the observed nighttime rainfall in the eastern Andes Mountains, over the Atlantic Ocean, and also over northern Argentina. The main simulation deficiencies are found in the Atlantic Ocean and near the Andes Mountains. Over the Atlantic Ocean the convective scheme is not triggered; thus the rainfall arises from the grid‐scale scheme and therefore differs from the TRMM‐PR. Near the Andes, intense (nighttime and daytime) simulated precipitation could be a response of an incorrect circulation and topographic uplift. Finally, it is important to note that unlike most reported bias of global models, RegCM3 does not trigger the moist convection just after sunrise over the southern part of the Amazon.

We analyze the local and remote impacts of climate change on the hydroclimate of the Amazon and La Plata basins of South America (SA) in an ensemble of four 21st century projections (1970-2100, RCP8.5 scenario) with the regional climate model RegCM4 driven by the HadGEM, GFDL and MPI global climate models (GCMs) over the SA CORDEX domain. Two RegCM4 configurations are used, one employing the CLM land surface and the Emanuel convective schemes, and one using the BATS land surface and Grell (over land) convection schemes. First, we find considerable sensitivity of the precipitation change signal to both the driving GCM and the RegCM4 physics schemes (with the latter even greater than the first), highlighting the pronounced uncertainty of regional projections over the region. However, some improvements in the simulation of the annual cycle of precipitation over the Amazon and La Plata basins is found when using RegCM4, and some consistent change signals across the experiments are found. One is a tendency towards an extension of the dry season over central SA deriving from a late onset and an early retreat of the SA monsoon. The second is a dipolar response consisting of reduced precipitation over the broad Amazon and Central Brazil region and increased precipitation over the La Plata basin and central Argentina. An analysis of the relative influence on the change signal of local soil-moisture feedbacks and remote effects of Sea Surface Temperature (SST) over the Niño 3.4 region indicates that the former is prevalent over the Amazon basin while the latter dominates over the La Plata Basin. Also, the soil moisture feedback has a larger role in RegCM4 than in the GCMs.

Scientists predict that global agricultural lands will expand over the next few decades due to increasing demands for food production and an exponential increase in cropbased biofuel production. These changes in land use will greatly impact biogeochemical and biogeophysical cycles across the globe. It is therefore important to develop models that can accurately simulate the interactions between the atmosphere and important crops. In this study, we develop and validate a new process-based sugarcane model (included as a module within the Agro-IBIS dynamic agro-ecosystem model) which can be applied at multiple spatial scales. At site level, the model systematically under/ overestimated the daily sensible/latent heat flux (by À10.5% and 14.8%, H and kE, respectively) when compared against the micrometeorological observations from southeast Brazil. The model underestimated ET (relative bias between À10.1% and -12.5%) when compared against an agro-meteorological field experiment from northeast Australia. At the regional level, the model accurately simulated average yield for the four largest mesoregions (clusters of municipalities) in the state of São Paulo, Brazil, over a period of 16 years, with a yield relative bias of À0.68% to 1.08%. Finally, the simulated annual average sugarcane yield over 31 years for the state of Louisiana (US) had a low relative bias (À2.67%), but exhibited a lower interannual variability than the observed yields.

The impacts of change in the Grell convective scheme and biosphere−atmosphere transfer scheme (BATS) in RegCM3 are described. Three numerical experiments (RegZhang, RegClaris and RegArain) are conducted to reduce the RegCM3-Grell rainfall underestimation over tropical South America. The simulation referred to as RegZhang follows modifications made by Zhang et al. (2008) in the BATS. The RegClaris combines the RegZhang BATS parameters with a reduction of water drainage at the bottom of the subsoil layer in the regions covered by the tropical rain forest and a shorter convective time period for the Grell scheme. The RegArain considers this same modification in the Grell scheme, but uses a deeper total soil column and a deeper root system in the BATS. After the first year of simulation, the soil water content in RegZhang is progressively drained out of the soil column resulting in a deficit of rainfall in the Amazon. The RegClaris and RegArain, on the other hand, simulate a similar rainfall annual cycle in the Amazon, showing substantial improvement not only in phase but also in intensity. This improvement is partially related to an increase in evapotranspiration due to a larger availability of water in the soil column. A remote effect is also noted over the La Plata Basin region, where the larger summer rainfall rate may be related to the increase in moisture transport from the Amazon. Wind-and rainfall-based indices are applied to identify South American monsoon (SAM) timing. The RegClaris rainfall rates are adequate to identify the onset and the demise of SAM according to the observed data, whereas the rainfall deficit in RegZhang is associated with a delay in the onset and an early demise of the SAM.

flux values) resulting in higher precipitation rates and a large wet bias. RegCLM is closer to the observations than RegBATS, presenting smaller wet and warm biases over the Amazon basin. On an interannual scale, the magnitudes of the anomalies of the precipitation and air temperature simulated by RegCLM are closer to the observations. In general, RegBATS simulates higher magnitude for the interannual variability signal.