Title: Climate Change in the Los Angeles Region: temperature and precipitation
Principal Investigators: Glen MacDonald, UCLA, Alex Hall, UCLA
Collaborators and Partners: Daniel Walton, UCLA, Neil Berg, UCLA, Ruth Cerezo-Mota, UCLA
This project was created to assess climate change in the region of Los Angeles County and determine its impacts at space and time scales relevant for municipal planning and policymaking. To do this, dynamic and statistical downscaling techniques were implemented in the region over two different periods: baseline (August 1981-August 2000) and future (August
2041-August 2061).
The regional model used for the dynamical downscaling is the Weather Research and
Forecasting (WRF) Model. NARR is used as the boundary condition (BC) for the baseline simulation, whereas for the future WRF is driven by NARR plus a perturbation. This perturbation was extracted from the National Center for Atmospheric Research (NCAR)
Community Climate System Model version 4. The simulation was carried out using three oneway nested domains, the outer domain covers California and has a spatial resolution of 18 km, the middle domain covers the south half of the state with a horizontal resolution of 6 km; and the inner domain centred on Los Angeles County with a horizontal resolution of 2 km. The results of this project are focused on the innermost domain: Los Angeles County
To do a multi-model estimation of mid-century warming over the LA region, a hybrid dynamicalstatistical approach has been developed. This technique uses the physically-consistent spatial patterns derived from the dynamical model run and then employs a statistical model that scales the patterns using warming values sampled from the global models. The statistical model uses two parameters sampled from the global models: (1) the regional mean warming, and (2) the contrast in warming between land and ocean areas. Validation of the model, performed by comparing the dynamical to statistical downscaled warming, shows that the statistical model can accurately approximate the annual mean and monthly warming patterns, with average errors smaller than 0.5 °C.
The statistical model was applied to models from the CMIP3 and CMIP5 archives to produce an ensemble of possible warming outcomes in the LA region. By downscaling many models (21
CMIP3 A1b and 18 CMIP5 RCP8.5), we were able to establish the uncertainty in our predictions due to both inter-model and inter-scenario differences. We found that the ensemble mean annual warming and the uncertainty (95% confidence interval) to be only slightly lower in the
CMIP3 (2.0 ± 0.8 °C) models than in the CMIP5 models (2.5 ± 1.1 °C), though even these small systematic differences may be partly due to differences in radiative forcing between the A1b and RCP8.5 emissions scenarios. Perhaps more significantly, the CMIP5 models had higher
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land-sea contrast values. Thus, when the CMIP5 models are statistically downscaled, they show elevated warming, especially over inland and mountainous parts of the domain.
In the case of the precipitation, first a validation of the model output was performed. The validation was done at daily and monthly scales using various observation-based gridded products (i.e. CPC daily precipitation, University of Delaware monthly precipitation and also the very high resolution reanalysis NARR). From this part of the study we can conclude that WRF is in good agreement with the 'observations' with higher correlation at monthly scales than a daily. WRF also is able to appropriately reproduce the rainfall distribution over the very complex orography of the region.
With the validation complete, a linear precipitation model is now being implemented as part of a hybrid statistical-dynamical downscaling approach to examine mid-20th century precipitation projections over the LA region. The linear model ingests various meteorological variables, such as winds, near-surface temperature, and stability, and it returns an orographic precipitation field. Compared to WRF, the linear model is very computationally cheap. Since the linear model only uses a finite set of inputs, we can also easily examine the controlling factors (e.g. future change in winds) behind the precipitation changes. So far, we have shown that the linear model output is able to accurately reproduce the WRF dynamically-downscaled baseline climatology of the wet-season precipitation. Soon we will validate whether the linear model is able to also reproduce the climate change signals in precipitation as diagnosed from the WRF-CCSM4 run. If we can successfully reproduce those precipitation changes, then we will have the complete framework to efficiently downscale the CMIP5 precipitation projections to the LA region.
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