Nwp final paper

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  1. 1. 1 Weather and Climate Error in a Nested Regional Climate Model: OLR and Precipitation NWP Final Project: Fall 2012 Authors: James S. Brownlee, Angie R. Lassman, Robert S. James Date Submitted: December 07, 2012 Abstract Global Climate Models (GCMs) are used heavily to conduct research on how the atmosphere will progress into the future. However, the errors within these models can quickly become large due to the amount of complicated processes that occur in the atmosphere on a variety of time scales. An important aspect of this is how quickly does a GCMs error become indistinguishable from the mean climate error. Once this occurs, it is difficult to get accurate weather forecasts from these models. In order to reduce errors in these models, it is important to analyze many different output quantities from them. For our research, we analyzed a nested regional climate model temporally, ranging from daily to seasonally to yearly, and spatially, focusing on the errors over land and ocean. Our goal for this research is to be able to explain why errors in this model are occurring and what could be done to correct them.
  2. 2. 2 1) Introduction All atmospheric models are created and run knowing that they will inherently contain some error in their output, no matter how good the equations or physical parameterizations are, which increases as the model generates a forecast field becomes farther from its initial state (Allen et al. 2006). Being able to analyze and understand these errors is a method for continuously improving atmospheric models. One way in which to know how far a models forecast field has strayed from the truth is to compare it to observations at the time in which the forecast is valid for, which is the emphasis of our research for this paper. In this study, we will be analyzing output from a nested regional climate model (NRCM), focusing on precipitation (both convective and non-convective components) and how it relates to observed outgoing longwave radiation (OLR). In addition, we will provide some insight of the various errors found in this model, including how quickly precipitation errors within the model equal and surpass the mean climate error. The model output is from a version of the Weather Research and Forecasting model (WRF), which has a horizontal resolution of 36 km, a zonal domain of 0W to 360W, a meridional domain of 30S to 45N, and a daily time interval. Our study will only focus on this domain, which is the coarsest of three within the NRCM. Also, our research will concentrate on the meridional domain of 30S to 30N, and keeping the zonal domain intact (Fig. 1), to be able to more easily complete an analysis between the northern and southern hemispheres. The time domain for this simulation was defined between 1 January 1996 and 31 December 2000. Our investigation has been aided by computer software from the Center for Ocean-Land-Atmosphere Studies called the Grid Analysis and Display System (GrADS), which allowed us to display the model data.
  3. 3. 3 First, we will look at a simple error analysis of the modeled output compared with observation data. This will be done using three time delineations: seasonally, annually, and overall mean error. The second portion of our research will be a statistical analysis of the model errors, as well as the seasonal and mean trends in OLR data. Finally, we will discuss the differences in the convective and non-convective components of the model output, and complete an analysis of zonal and meridional averages in the model output. Combined, these research points will help us to understand errors in atmospheric models; such as the one we are examining, and why they may be occurring. 2) Error Analysis 2.1) Seasonal Error The first figure created is an error analysis of the WRF model output precipitations and the observed precipitation data. The model output uses the sum of the convective and non- convective precipitation while the observed data is the total rain. The time delineation is seasonal from 1996 to 2000, divided into sections from March to May, June to August, September to November, and December to February at 30N to 30S. The red and blue shading scale values in Fig. 2 are determined by subtracting the observed precipitation from the model precipitation. Therefore, the positive values correlate to model overestimation while negative values are model underestimation. The pattern of seasonal variances between the Northern and Southern Hemispheres in Fig. 2 leads to the theory that the incoming radiation is indirectly affecting the over or underestimation of precipitation in the WRF model (Thornton and Running 1999; Richardson 1981). It is obvious in Figs. 2A, D that from December to May the southern hemisphere is receiving the most incoming solar radiation. This is also the case when focusing on Figs. 2B, C
  4. 4. 4 except during this time period, the Northern Hemisphere is receiving more incoming solar radiation. During those time frames, these locations experience a large overestimation in total precipitation by the NRCM. The fact that convection occurs when the Earths surface within a conditionally unstable or moist atmosphere is warmer than its surroundings which leads to evaporation and rising motion in the air column. This process promotes the formation of a convective cloud, such as cumulonimbus or cumulus congestus. The solar radiation ties in with this process during the diurnal heating of the earth's surface, with more incoming solar radiation usually correlating to larger amounts of convective precipitation (Winslow and Hunt Jr. 2001). Also, precipitation is known to be very high near the equator due partially to the influence of the Intertropical Convergence Zone (ITCZ) (Waliser and Gautier 1993). This is due to the convergence of the northeast trade winds and southeast trade winds in a low-pressure zone coupled with the high ability of convection, which then promotes lifting in the air column. The ITCZ is clearly defined with the NRCM overestimating precipitation on each of the seasonal subfigures in Fig. 2. Because this region is usually known for its high amount of precipitation, it is likely that the model has a bias in the dynamic parameters of this region, thus causing the overestimation (Waliser and Gautier 1993). However, the location of this high precipitation region or ITCZ does however vary throughout the year. The northward and southward seasonal movement of this zone can be seen when comparing each subfigure in Fig. 2. One issue that becomes apparent when analyzing this figure is the failure to depict the seasonal transition of the ITCZ. The secondary meridional circulation induced by convective momentum transport (CMT) within the ascending branch of the Hadley circulation is a missing dynamical mechanism that can cause common failure of general circulation models (GCMs) in simulating seasonal migration of ITCZ precipitation maximum across the equator (Janowiak et al. 1995; Wu 2003).
  5. 5. 5 This failure is created from the model bias precipitation peak remaining north of the equator during November through March. 2.2) Annual Error Another way to analyze climate and weather errors in models is to examine them from an annual point of view. Fig. 3 is organized as yearly data from 1996 to 2000. The annual data shown in this figure is very similar to above explanation of Fig. 2, as it is the error of the model output (sum of convective and non-convective precipitation) compared with total rain observations. The purpose of comparing the data by year is to determine if specific meteorological or oceanographic climatological events occurring in that time period have affected the models output. With this said, an occurrence would have to be very large for its impact to show up on an entire years model data. Therefore, eliminating all mesoscale events that may have occurred during the time period. After some research, it was discovered that one of the strongest El Nio event ever was recorded in 1997 to 1998 (Chambers et al. 1999; Williamson et al. 2000). An El Nio is an anomalous ocean warming, occurring about every five years, which generates a dominant source of climate variability across the globe. The atmospheric component linked to El Nio is the Southern Oscillation based upon research from many studies (Rasmusson and Carpenter 1983; McPhaden et al. 1998). This is a fluctuation of surface air pressure at sea level in the tropical eastern and western Pacific Ocean. Experts believe that these atmospheric and oceanic temperature anomalies collaborate together forming the phenomena called El Nio Southern Oscillation (ENSO) (Trenberth 1997). ENSO is likely related to many unusual climatic events occurring around the globe, such as increase in monsoon rainfall, drought and an extraordinary number of storms (Rasmusson and Wallace 1983). When comparing the effects of ENSO to the annual model and observed data characteristic of ENSO
  6. 6. 6 are apparent in Fig. 2B during the year of 1997. Studies of this event show that heavy monsoons occurred as an affect of ENSO. While the Southern Oscillation isnt the only factor influencing the monsoon seasons in Southeast Asia, there is a close association between ENSO and the summer monsoon rainfall, based on how precipitation was distributed (Ropelewski and Halpert 1996). Fig. 3B, compared to the rest of the subfigures, shows a high amount of overestimation in the usually wet region of Southeast Asia. This leads to the idea that the model is positively biased towards heavier precipitation. Also, the interannual change in the extratropical wintertime jet stream over the western and central Pacific is highly influenced by ENSO due to the close relation to the distribution of tropical convection across Indonesia and the tropical Pacific. The Pacific Ocean Basin and the Indian subcontinent have both shown precipitation patterns directly rela