Assessment of Intraseasonal to Interannual Climate Prediction and Predictability (2010) / Chapter Skim
Currently Skimming:
3 Building Blocks of Intraseasonal to Interannual Forecasting
3 Building Blocks of Intraseasonal to Interannual Forecasting
Pages 54-100
The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.
From page 54... ...
Past advances that have contributed to improvements in forecast quality are noted, and the section ends by presenting areas in which further improvement could be realized. HISTORICAL PERSPECTIVE FOR INTRASEASONAL TO INTERANNUAL FORECASTING Scientific weather prediction originated in the 1930s, with the objective of extending forecasts as far into the future as possible.
|
From page 55... ...
and improved understanding and treatment of underlying mechanisms. One major direction of advancement in forecasting has been that of dynamical modeling (see "Dynamical Models"section in this chapter)
|
From page 56... ...
OBSERVATIONS Observations are an essential starting point for climate prediction. In contrast to weather prediction, which focuses primarily on atmospheric observations, ISI prediction requires information about the atmosphere, ocean, land surface, and cryosphere.
|
From page 57... ...
Some examples of improvements arising from the assimilation of specific observations, such as AMSU radiances, are discussed in the "Data Assimilation" section of this chapter. The incorporation of targeted observations that focus on atmospheric processes that are sources of ISI predictability could also contribute to ISI forecast quality.
|
From page 58... ...
Therefore, the initialization of sea surface and sub-surface ocean state is required for near-term climate prediction. Unfortunately, the comprehensive observation of the global oceans started much later than in the atmosphere and even today there are challenges that prevent collection of routine observations over large parts of the ocean.
|
From page 59... ...
. It provides a key constraint on initial conditions for seasonal forecasting at many centers around the world.
|
From page 60... ...
and the International Oceanographic Commission (IOC) to coordinate sustained global ocean in situ observations, including Argo floats, surface SST drifters, Volunteer Observing Ship-based measurements, tropical moored arrays, and the extra-tropical moored buoys.
|
From page 61... ...
Building Blocks of Intraseasonal to Interannual Forecasting 61 FIGURE 3.2 Examples of the spatial distribution of various ocean observations mentioned in the text. Top panel: Argo floats, which can provide surface and sub-surface information.
|
From page 62... ...
Process studies by CLIVAR and others add to understanding of ocean dynamics, develop improved parameterizations of processes not resolved in ocean models, and guide longer term investments in ocean observing. Land The land variables of potential relevance for seasonal prediction -- the variables for which accurate initialization may prove fruitful -- are soil moisture, snow, vegetation structure, water table depth, and land heat content.
|
From page 63... ...
A common approach is to utilize the soil moisture produced by the atmospheric analysis already being used to generate the atmospheric initial conditions. This approach has the advantage of convenience, and the soil moisture conditions that are produced reflect reasonable histories of atmospheric forcing, as generated during the analysis integrations -- if the analysis says that May is a relatively rainy month, then the June 1 soil moisture conditions produced will be correspondingly wet.
|
From page 64... ...
Compounding the difficulty of estimating SWE from space are spatial heterogeneities in snowpack associated with topography and vegetation. The LDAS approach described above can provide SWE in addition to soil moisture states, assuming the land model used employs an adequate treatment of snow physics.
|
From page 65... ...
through a land data assimilation approach that combines the LDAS system information with observations of variables such as soil moisture, snow, and skin temperature. For maximum effectiveness, the land models utilized in these systems need to include temperature state variables representing at least the depth of the annual temperature cycle (i.e., a few meters)
|
From page 66... ...
In terms of monitoring climate variability and change and weather and climate prediction, these reports identify the priority cryosphere observations as: long-term consistent records of cryosphere variables, high spatial and temporal resolution fields of snowfall, snow water equivalent, snow depth, albedo and temperature, and mapping of permafrost and frozen soil, lake and sea ice characteristics. Remote sensing methods can be used to address sea ice extent (recorded since the late 1970s)
|
From page 67... ...
and have formed the basis for forecast quality (Johansson et al., 1998)
|
From page 68... ...
Dark shading indicates negative anomalies, and light shading indicates positive anomalies.The patterns emerge from a rotated Empirical Orthogonal Function analysis of monthly mean 500hPa geopotential heights. Contour interval is 10 m.
|
From page 69... ...
matrix. For example, in Figure 3.4, the rotated EOFs that are derived from monthly 500-hPa geopotential height data define two teleconnection patterns, the North Atlantic Oscillation (NAO)
|
From page 70... ...
. Despite the potential pitfalls, CCA has been shown to exhibit considerable skill for long-range climate forecasting (Barnston and He, 1996)
|
From page 71... ...
is based on a reconstruction from the leading eigenmodes of the data field at a number of periods prior to forecast time. The constructed analogue approach has been used successfully to forecast at lead times of up to a year (van den Dool et al., 2003)
|
From page 72... ...
(2009) have shown that kernelized methods lead to additional skill in ENSO forecasts over traditional PCA techniques FIGURE 3.6 Schematic of an Artificial Neural Network (ANN)
|
From page 73... ...
followed thereafter. Evolution of Dynamical ISI Prediction Some of the earliest attempts at making ISI predictions with dynamical models were performed to essentially extend the range of weather forecasts.
|
From page 74... ...
to distinguish them from short and medium range weather forecasts. The numerical models used for extended range forecasts were the same AGCMs that were then being used for weather forecasting.
|
From page 75... ...
Because of the rapid decay of quality with lead time, one would not expect useful predictions on seasonal or longer time scales to arise solely from atmospheric initial conditions. To obtain forecast quality on longer timescales, one has to consider predictability arising from the knowledge of the evolution of boundary conditions or external forcing11 (Lorenz, 1975; Charney and Shukla, 1981)
|
From page 76... ...
Over the last decade, the ENSO forecast quality associated with CGCMs has improved significantly. Reductions in the model bias and improved ocean initial conditions have now enabled CGCMs to be competitive with statistical models.
|
From page 77... ...
In this section, we provide a brief overview of the state-of-the-art in model resolution for CGCMs used for ISI prediction at two of the major operational forecasting centers, NCEP and ECMWF. The atmospheric component of the NCEP Climate Forecast System (CFS)
|
From page 78... ...
. Model errors in the tropical Pacific, such as a cold SST bias or a ‘double' Inter-tropical Convergence Zone, are particularly troublesome because they impact phenomena such as ENSO and the MJO that are important for ISI prediction.
|
From page 79... ...
DATA ASSIMILATION For the purposes of climate system prediction, data assimilation (DA) is the process of creating initial conditions for dynamical models.
|
From page 80... ...
. Modern data assimilation for short-term numerical weather prediction objectively combines observations, model predictions started at earlier times, and a priori statistical information about the observations and the model to create initial conditions for updated model predictions.
|
From page 81... ...
Building Blocks of Intraseasonal to Interannual Forecasting 81 FIGURE 3.10 Satellite observing systems available for data assimilation in the ERA-Interim starting in 1989.
|
From page 82... ...
The ensemble members are adjusted using observations to produce initial conditions for a set of predictions. EnKF techniques are now in operational use for ensemble weather prediction (Houtekamer and Mitchell, 2005)
|
From page 83... ...
Ocean Data Assimilation As pointed out in Chapter 2, much of the information required for successful ISI predictions resides in the initial conditions for the ocean, land surface, and cryosphere components of the climate system. There is a much shorter history of prediction and data assimilation for these components.
|
From page 84... ...
Ocean DA systems based on this algorithm continue to be used at operational prediction centers like NCEP, ECMWF and UKMO. Many of them incorporate a number of heuristic enhancements, for instance the use of independently produced sea surface temperature analyses instead of observations of near-surface ocean temperature.
|
From page 85... ...
Remote sensing measurements of sea surface height, which is a model state variable, from the TOPEX/Jason-1 altimetry have been available since 1992. Behringer (2007)
|
From page 86... ...
Here we describe progress on another aspect of land assimilation, i.e., the merging of land surface state observations with estimates from the corresponding land model prognostic variables using mathematically optimal techniques. A popular approach involves adjusting the land model's soil moisture reservoirs in response to screen-level (2 m)
|
From page 87... ...
Understanding or improving forecast quality requires information about the predictions that go into a forecast. Increasingly, forecasts are generated from multiple prediction inputs, which can be objective (e.g., predictions from statistical or dynamical models)
|
From page 88... ...
. Overall, the seasonal climate predictions are more confident, and many skill metrics are higher, during ENSO events (Figure 3.13: Goddard and Dilley, 2005)
|
From page 89... ...
The red and yellow bars correspond to dynamical models: the Climate Forecast System (CFS) is a state-of-the-art model developed in the mid2000s (Saha et al., 2006)
|
From page 90... ...
Currently, forecast quality is often difficult to compare across systems because of differences in forecast format, verification data, the choice of skill metrics, or even differences in graphical appearance. A mechanism to provide a consistent view of prediction quality across models was established in 2006 by the World Meteorological Organization.
|
From page 91... ...
Combined Forecast Systems A growing body of literature touts the benefit of multiple prediction inputs in climate forecasts. Many national centers that produce real-time forecasts include one or more dynamical models, one or more statistical models, and perhaps also the subjective interpretation or experience of the forecasters involved.
|
From page 92... ...
Comparison of the official forecasts, which include the subjective intervention, against the purely objective consolidation indicates that the subjective element reduces forecast quality (O'Lenic et al., 2008) , particularly during winter in the absence of a strong ENSO signal (Livezey and Timofeyeva, 2008)
|
From page 93... ...
and other international organizations, bring together countries within a region, such as Southeastern South America or the Greater Horn of Africa, to develop a consensus outlook for the climate of the upcoming season. Seasonal climate predictions from the participating NMHSs are discussed in conjunction with those from international centers.
|
From page 94... ...
, which refers broadly to the statistical correction of dynamical models. MOS techniques can be used to correct systematic biases of dynamical models by translating the aspects of the observed variability that the model captures correctly into something that more closely resembles the observations (e.g.
|
From page 95... ...
The prediction inputs can include statistical models, dynamical models, and the combination of the two. The main weakness of MME is a lack of design behind the specific models included; MME usually draws on whatever respectable models are available, and thus does not necessarily span all uncertainties in model physics.
|
From page 96... ...
Climate Prediction Center operational seasonal forecasts are issued on the 3rd Thursday of each month at 8:30 AM, and a team of 7 forecasters at CPC rotates throughout the course of the year in preparing these forecasts. The process begins with a
|
From page 97... ...
A telephone conference call is conducted the preceding Friday to discuss the current status of the climate system and the content of the available tools with partners in the broad climate community. Based on these discussions and the forecaster's own interpretation of the forecast tools, the forecaster manually draws draft forecast maps for all thirteen forecast leads for both temperature and precipitation.
|
From page 98... ...
• Targeted observations for specific climate processes that are poorly understood could improve dynamical models by providing more realistic initial conditions, improved parameterizations of sub-grid scale processes, and/or data to be used in validation. • Sustained observations of the fluxes of heat and moisture between the atmosphere and ocean or between the land and atmosphere are useful for identifying biases and errors in dynamical models.
|
From page 99... ...
Estimates of prognostic states at the land surface (e.g. soil moisture)
|
From page 100... ...
Comparison among systems is critical for identifying opportunities for model improvement, as well as novel combinations of forecast models that may improve quality. • Subjective intervention into forecasts needs to be minimized and documented.
|
Key Terms
This material may be derived from roughly machine-read images, and so is provided only to facilitate research.
More
information on Chapter Skim is available.