Numerical weather prediction models

Almost any study of observed variability in the Arctic atmospheric circulation makes use of output from the data assimilation cycles of NWP models. From the preceding discussion, NWP output is also commonly used as lateral forcing for regional climate models or to provide wind forcing for sea ice and coupled ice-ocean models. Output from GCMs is also commonly verified against output from NWP models. We hence

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Time (Julian day)

Figure 9.13 Time series of 24-48 hour Polar MM5 forecasts (thick solid lines) and corresponding AWS observations from the Summit site on central Greenland (thin solid lines) for April through May, 1997. Time series are shown of surface pressure, near-surface temperature, wind speed, wind direction and mixing ratio. Surface pressure in MM5 is interpolated from the height of the model grid point elevation to the height of the AWS station. The model wind speed is interpolated from the lowest model level to the height of the AWS station. The remaining modeled variables are given as the value of the lowest model level (from Bromwich et al., 2001b, by permission of AMS).

91 96 101 106 111 116 121 126 131 136 141 146 151

Time (Julian day)

Figure 9.13 Time series of 24-48 hour Polar MM5 forecasts (thick solid lines) and corresponding AWS observations from the Summit site on central Greenland (thin solid lines) for April through May, 1997. Time series are shown of surface pressure, near-surface temperature, wind speed, wind direction and mixing ratio. Surface pressure in MM5 is interpolated from the height of the model grid point elevation to the height of the AWS station. The model wind speed is interpolated from the lowest model level to the height of the AWS station. The remaining modeled variables are given as the value of the lowest model level (from Bromwich et al., 2001b, by permission of AMS).

tend to think of NWP output as "truth". The assumption, however, is incorrect. It is better to think of NWP models as an optimal blend of observations and an AGCM or regional atmospheric model.

In climate simulations, such as for long-terms means outlined in Section 9.5 or for greenhouse-gas experiments to be reviewed in Chapter 11, specification of the initial boundary conditions (e.g., of SST, sea ice distribution, land surface cover, greenhouse gas concentrations) is much more important than the specification of the initial atmospheric state (i.e., the three-dimensional distribution of temperature, winds and humidity at the start of the model simulation). However, because of the complex and chaotic nature of the atmosphere, predicting the weather at a given time and place depends critically on specification of the initial atmospheric state.

NWP starts with an initial atmospheric state to get a short term (e.g., 6 or 12 hour) forecast of the future atmospheric state as well as of various surface fluxes and variables (e.g., precipitation, evaporation, radiation and SAT). The forecasted atmospheric state will differ from the true atmospheric state due to problems in specifying the initial atmospheric conditions, as well as shortcomings in the model physics, resolution and parameterizations. In NWP, one periodically restores the model back toward the true atmospheric state through assimilation of observations. In an ongoing process, the forecasts, adjusted by data assimilation (the blended fields generally termed "analyses"), represent the new initial state to generate the next forecast (similar techniques are now being applied to sea ice models, see Section 9.4). Assimilation data for NWP models are primarily tropospheric observations of temperature, pressure height, winds and humidity obtained from rawinsonde profiles and satellite retrievals. Surface variables such as precipitation, evaporation, radiation fluxes and SAT are typically not assimilated and are simply model predictions. However, the ERA-40 reanalysis (discussed shortly) does assimilate SAT. Surface conditions, such as sea ice cover, sea surface temperature, snow cover and vegetation cover are generally prescribed from observations.

One of the most widely used sources of NWP output in climate research (and within this textbook) is the NCEP/NCAR reanalysis (Kalnay et al., 1996; Kistler et al., 2001). Most NCEP/NCAR data are provided on a 2.5 x 2.5 degree grid. The NCEP/NCAR reanalysis represents more than 50 years (continually updated) of global atmospheric analyses and surface fields. The effort involves recovery and assembly of numerous atmospheric data sets, which are quality-controlled and assimilated with a constant ("frozen") assimilation and forecast system. As outlined earlier, outputs from operational systems (used for routine weather prediction) contain pseudo-climate signals (jumps) due to frequent changes in these systems. Reanalysis is intended to eliminate this problem. However, inhomogeneities will still be present due to changes in the amount and quality of assimilation data. Users of reanalysis data must always be aware of this problem, especially for time series analysis. Prior to 1958, the frequency of rawinsonde reports in the Arctic is very low. Rawinsonde coverage increased after 1958, and again in the early 1970s. Satellite data (temperature and humidity information) began to be incorporated in the 1970s. Starting in 1979, drifting buoy data from the IABP began to provide regular reports of surface pressure over the Arctic Ocean, helping to constrain the field of atmospheric mass.

There are several existing reanalysis efforts. The NCEP-DOE AMIP-II reanalysis addresses some of the known problems in the NCEP/NCAR effort and incorporates improved model physics. However, fields are only available back to 1979. The ECMWF

ERA-15 effort spanned the period 1978-93. While also widely used, and generally considered to be of higher quality than the NCEP/NCAR effort with respect to the surface fields (e.g., 2-m temperature, precipitation), a drawback is the relatively short record. The newer ERA-40 effort contains many improvements over ERA-15, not only with respect to higher vertical and horizonal resolution, but also as a result of improved treatment of the surface. Many of these improvements were driven by concerns over high-latitude performance. For example, in contrast to the NCEP/NCAR and ERA-15 efforts, ERA-40 incorporates a full range of sea ice concentrations, which along with other improved parameterizations yields better depictions of the near-surface temperature over sea ice. ERA-40 also includes the thermal effects of soil freezing and a frozen soil hydrology. Albedo treatments over snow-covered land surfaces have also been improved.

Other global reanalyses include the Japan Meteorological Agency (JMA-25) effort, and the NASA DAO (Data Assimilation Office) reanalysis. Both NCEP and NASA are planning new reanalyses. NCEP has also recently completed the North American Regional Reanalysis (NARR). This regional reanalysis covers all of North America and Greenland.

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