25 research outputs found

    Impact of Land Surface Initialization Approach on Subseasonal Forecast Skill: a Regional Analysis in the Southern Hemisphere

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    The authors use a sophisticated coupled land-atmosphere modeling system for a Southern Hemisphere subdomain centered over southeastern Australia to evaluate differences in simulation skill from two different land surface initialization approaches. The first approach uses equilibrated land surface states obtained from offline simulations of the land surface model, and the second uses land surface states obtained from reanalyses. The authors find that land surface initialization using prior offline simulations contribute to relative gains in subseasonal forecast skill. In particular, relative gains in forecast skill for temperature of 10%-20% within the first 30 days of the forecast can be attributed to the land surface initialization method using offline states. For precipitation there is no distinct preference for the land surface initialization method, with limited gains in forecast skill irrespective of the lead time. The authors evaluated the asymmetry between maximum and minimum temperatures and found that maximum temperatures had the largest gains in relative forecast skill, exceeding 20% in some regions. These results were statistically significant at the 98% confidence level at up to 60 days into the forecast period. For minimum temperature, using reanalyses to initialize the land surface contributed to relative gains in forecast skill, reaching 40% in parts of the domain that were statistically significant at the 98% confidence level. The contrasting impact of the land surface initialization method between maximum and minimum temperature was associated with different soil moisture coupling mechanisms. Therefore, land surface initialization from prior offline simulations does improve predictability for temperature, particularly maximum temperature, but with less obvious improvements for precipitation and minimum temperature over southeastern Australia

    Influence of Leaf Area Index Prescriptions on Simulations of Heat, Moisture, and Carbon Fluxes

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    Leaf-area index (LAI), the total one-sided surface area of leaf per ground surface area, is a key component of land surface models. We investigate the influence of differing, plausible LAI prescriptions on heat, moisture, and carbon fluxes simulated by the Community Atmosphere Biosphere Land Exchange (CABLEv1.4b) model over the Australian continent. A 15-member ensemble monthly LAI data-set is generated using the MODIS LAI product and gridded observations of temperature and precipitation. Offline simulations lasting 29 years (1980-2008) are carried out at 25 km resolution with the composite monthly means from the MODIS LAI product (control simulation) and compared with simulations using each of the 15-member ensemble monthly-varying LAI data-sets generated. The imposed changes in LAI did not strongly influence the sensible and latent fluxes but the carbon fluxes were more strongly affected. Croplands showed the largest sensitivity in gross primary production with differences ranging from -90 to 60 %. PFTs with high absolute LAI and low inter-annual variability, such as evergreen broadleaf trees, showed the least response to the different LAI prescriptions, whilst those with lower absolute LAI and higher inter-annual variability, such as croplands, were more sensitive. We show that reliance on a single LAI prescription may not accurately reflect the uncertainty in the simulation of the terrestrial carbon fluxes, especially for PFTs with high inter-annual variability. Our study highlights that the accurate representation of LAI in land surface models is key to the simulation of the terrestrial carbon cycle. Hence this will become critical in quantifying the uncertainty in future changes in primary production

    Evaluation of the CABLEv2.3.4 Land Surface Model Coupled to NU‐WRFv3.9.1.1 in Simulating Temperature and Precipitation Means and Extremes Over CORDEX AustralAsia Within a WRF Physics Ensemble

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    The Community Atmosphere Biosphere Land Exchange (CABLE) model is a third‐generation land surface model (LSM). CABLE is commonly used as a stand‐alone LSM, coupled to the Australian Community Climate and Earth Systems Simulator global climate model and coupled to the Weather Research and Forecasting (WRF) model for regional applications. Here, we evaluate an updated version of CABLE within a WRF physics ensemble over the COordinated Regional Downscaling EXperiment (CORDEX) AustralAsia domain. The ensemble consists of different cumulus, radiation and planetary boundary layer (PBL) schemes. Simulations are carried out within the NASA Unified WRF modeling framework, NU‐WRF. Our analysis did not identify one configuration that consistently performed the best for all diagnostics and regions. Of the cumulus parameterizations the Grell‐Freitas cumulus scheme consistently overpredicted precipitation, while the new Tiedtke scheme was the best in simulating the timing of precipitation events. For the radiation schemes, the RRTMG radiation scheme had a general warm bias. For the PBL schemes, the YSU scheme had a warm bias, and the MYJ PBL scheme a cool bias. Results are strongly dependent on the region of interest, with the northern tropics and southwest Western Australia being more sensitive to the choice of physics options compared to southeastern Australia which showed less overall variation and overall better performance across the ensemble. Comparisons with simulations using the Unified Noah LSM showed that CABLE in NU‐WRF has a more realistic simulation of evapotranspiration when compared to GLEAM estimates.This project is supported through funding from the Australian Research Council (ARC) Centre of Excellence for Climate Extremes (CE170100023). J. Kala is supported by an ARC Discovery Early Career Researcher Grant (DE170100102)

    An investigation of future fuel load and fire weather in Australia

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    We present an assessment of the impact of future climate change on two key drivers of fire risk in Australia, fire weather and fuel load. Fire weather conditions are represented by the McArthur Forest Fire Danger Index (FFDI), calculated from a 12-member regional climate model ensemble. Fuel load is predicted from net primary production, simulated using a land surface model forced by the same regional climate model ensemble. Mean annual fine litter is projected to increase across all ensemble members, by 1.2 to 1.7 t ha-1 in temperate areas, 0.3 to 0.5 t ha-1 in grassland areas and 0.7 to 1.1 t ha-1 in subtropical areas. Ensemble changes in annual cumulative FFDI vary widely, from 57 to 550 in temperate areas, -186 to 1372 in grassland areas and -231 to 907 in subtropical areas. These results suggest that uncertainty in FFDI projections will be underestimated if only a single driving model is used. The largest increases in fuel load and fire weather are projected to occur in spring. Deriving fuel load from a land surface model may be possible in other regions, when this information is not directly available from climate model outputs

    Two decades of OH variability as inferred by an inversion of atmospheric transport and chemistry of methyl chloroform

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    International audienceWe developed an iterative inverse method to infer inter-annual sources and sinks of methyl chloroform (MCF) from atmospheric measurements, on a monthly basis. The methodology is presented and used to estimate two decades of OH variability between 1980 and 2000, using varying meteorology. When OH concentrations are adjusted with loose prior errors and MCF emissions are adjusted within inventory bounds, we show that substantial OH inter-annual variability (8.5±1.0% of the mean) and trend (-0.7%.yr-1) are necessary to match MCF observations. This result is confirmed by a series of sensitivity tests addressing main limitations of previous studies. However, we show that it is also possible to match MCF observations with a 65% reduction of OH year-to-year variations and a 60% reduction of absolute OH trend, but still a consistency of inferred emissions with inventory values at a ±2s level. On the other hand, the phase of inferred OH variations is a more robust feature of our set of inversions. Overall, MCF inversions can only provide a range of OH variations unless inventory uncertainties are further reduced
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