Assessment of Intraseasonal to Interannual Climate Prediction and Predictability (2010) / Chapter Skim
Currently Skimming:
2 Climate Prediction
2 Climate Prediction
Pages 21-53
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From page 21... ...
Progress in the 1980s extended prediction timescales, exploiting improved observational awareness of ENSO variability in the tropical Pacific and its associated teleconnections. Future improvements in prediction quality depend upon the ability to identify and understand patterns of variability and specific processes that operate on ISI timescales.
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, the limit for making skillful forecasts of midlatitude weather systems is estimated to be approximately two weeks5, largely due to the sensitivity of forecasts to the atmospheric initial conditions (see Box 2.1)
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showed that even with a perfect model and essentially perfect initial conditions, the fact that the atmosphere is chaotic7 causes forecasts to lose all predictive information after a finite time. He estimated the "limit of predictability" for weather as about two weeks, an estimate that still stands: it is generally considered not possible to make detailed weather predictions beyond two weeks based on atmospheric initialization alone.
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In other words, a minor error in an observation or in the model can lead to an abrupt loss of forecast quality if the atmospheric conditions are unstable. For climate prediction on ISI timescales, the initial conditions involve phenomena with much longer timescales than the dominant atmospheric instabilities.
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Although operational, extended forecasts continued to focus on surface temperature and precipitation over continents, the atmospheric initial conditions were no longer considered important for making these forecasts; atmospheric ISI prediction was now considered a boundary value problem (Lorenz, 1975; Chen and Van den Dool, 1997; Shukla, 1998; Chu, 1999)
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For a given soil moisture anomaly, the lifetime of the anomaly The particular metrics used to evaluate prediction quality were the multi-model Brier Skill Score for 2m8 temperature and rainfall and the Mean Square Skill Score for the Nino3.4 SSTA.
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Soil moisture anomalies at meter depth have inherent time scales of weeks to months. As panel (a)
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A positive soil moisture anomaly at the Atmospheric Radiation Measurement/Cloud and Radiation Testbed (ARM/CART) site in Oklahoma decreases with a time scale much longer than the atmospheric events that caused it.
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; TIW: tropical instability wave (in the ocean) ; MJO/MISV: Madden-Julian Oscillation/Monsoon intraseasonal variability; NAM: Northern Hemisphere annular mode; SAM: Southern Hemisphere annular mode; AO: Arctic oscillation; NAO: North Atlantic oscillation; QBO: quasi-biennial oscillation, IOD/ZM: Indian Ocean dipole/zonal mode; AMOC: Atlantic meridional overturning circulation.
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Soil moisture initialization in forecast systems is known to affect the evolution of forecasted precipitation and air temperature in certain areas during certain times of the year on intraseasonal time scales (e.g., Koster et al., 2010)
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These characteristics suggest that some aspects of sea ice may be predictable on ISI seasonal time scales. In the Southern Hemisphere, sea ice concentration anomalies can be predicted statistically by a linear Markov model on seasonal time scales (Chen and Yuan, 2004)
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, and their time scales for propagating across ocean basins can be of the order of days to weeks. Figure 2.5 shows a time-longitude plot of equatorial outgoing longwave radiation (OLR)
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. SST and mixed layer feedback on subseasonal time scales The large/planetary spatial and subseasonal time scales of the CCEWs and MJO discussed above, along with the often strong impact of these phenomena on surface fluxes via wind speed and cloudiness, can result in significant modulation of the ocean surface mixed layer,
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. The manifestation of the Arctic Oscillation in the Atlantic sector is commonly referred to at the North Atlantic Oscillation (NAO)
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. In sensitive areas such as Europe in winter, experiments suggest that the influence of stratospheric variability on land surface temperatures can exceed the local effect of sea surface temperature.
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in the ocean TIWs are most prevalent in the eastern Pacific Ocean and are evident in SST and other quantities such as ocean surface chlorophyll and even boundary-layer cloudiness, particularly just north of the equator. They arise from shear-flow and baroclinic instabilities and result in westward propagating wave-like features having length scales on the order of 1000s of km and time scales of about 1–2 weeks.
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ENSO forcing triggers the Antarctic Dipole, with implications for sea ice prediction at seasonal timescales. Indian Ocean Dipole/Zonal Mode (IOD/IOZM)
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Forest fires provide another example of relatively rapid changes in atmospheric composition that can affect climate on ISI time scales. The Indonesian fires in 1997–98 helped to exacerbate the very strong El Niño drought.
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or impacts from space. While most of these high impact events cannot be predicted with any accuracy, intraseasonal to interannual climate predictions subsequent to the event would be affected by the changed atmospheric composition.
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This interaction can manifest itself as decreased thermal damping, as in the case of the ocean mixed layer response to atmospheric forcing, or as quasi-periodic evolution of upper ocean heat content, as in the case of ENSO. Subgrid-scale processes, such as deep convection and stratus clouds in the atmosphere, or coastal upwelling in the ocean, are important components of this ocean-atmosphere interaction.
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Models typically have some initialization and representation of snow cover effects, but the quality and impact of these effects are largely untested. Soil Moisture Soil moisture initialization as a source of subseasonal prediction quality is discussed in greater detail in Chapter 4.
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METHODOLOGIES USED TO QUANTITATIVELY ESTIMATE PREDICTION SKILL Prediction skill has been studied in depth since the early 1900s when Finley claimed considerable skill in forecasting tornadoes. Nearly simultaneously, recognition of the limitations of measuring skill surfaced (Murphy, 1991; 1993)
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An example of several PDFs associated with predictions of SST in the tropical Pacific Ocean is shown in Figure 2.8. The initial prediction is shown in green.
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and a summary of their use for mode identification is contained in Appendix A Often, as the time scale increases, the nonlinear contribution to the modes tends to be filtered.
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On the intraseasonal time scale, when monsoon variability has been probed by a nonlinear neural network technique (Cavazos et al., 2002) , a picture emerges with nonlinear modes related to the nonlinear dynamics embedded in the observed systems (Cavazos et al., 2002)
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and summarizes several contributions to forecast quality, namely correlation, bias, and variance error. The root mean squared error (RMSE; equal to the square root of MSE)
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. If the forecast system has no skill, then the hit rate and false alarm rates are similar and the curve lies along the diagonal in the graph (area = 0.5)
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Thus, if a forecast includes uncertainty, it is important to assess the meaningfulness of that uncertainty; probabilistic forecasts need to be assessed probabilistically. Providing deterministic metrics as well, such as correlation or hit rate of the most likely outcome, may give additional information of use to decision makers, but provided alone, deterministic measures undermine the richness of the forecast information.
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The probabilistic ROC is a variant of the ROC described previously that considers the hit rates and false alarm rates for events forecast at varying levels of probabilistic confidence. The Brier skill score (BSS)
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the climatological odds. The skill metric is a Heidke skill score, and is calculated by including only those forecasts that differ from climatological odds.
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Many measures of prediction skill are sensitive to how much the prediction deviates from climatology; therefore, the assessment of seasonal predictions can be influenced by both changes in the drivers of climate predictability as well as trends or other slowly varying changes in the background state. ENSO exerts the greatest influence on seasonal-to-interannual climate variability globally (e.g.
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From page 52... ...
52 Assessment of Intraseasonal to Interannual Climate Prediction and Predictability FIGURE 2.13 Progress in the seasonal forecast skill of the ECMWF operational system during the last decade. The solid bar shows the relative reduction in mean absolute error of forecast of SST in the Eastern Pacific (NINO3)
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From page 53... ...
CHALLENGES TO IMPROVING PREDICTION SKILL This chapter has provided the historical perspective on climate prediction, pointed to where there are opportunities to improve prediction quality by improving our understanding and representation in models of sources of predictability, and reviewed the methods available to quantify skill. From the 1980s to the 1990s, seasonal prediction quality improved dramatically, but then did not improve further (Kirtman and Pirani, 2008, 2009)
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