Weather and climate are often discussed in parallel; however, they describe distinct phenomena. Weather indicates immediate atmospheric conditions, while climate signifies expected conditions in the long run. As discussions about climate change intensify, the focus is on identifying shifts in these long-term averages of daily weather. While the weather can change rapidly, climate represents the average of weather across extended periods and geographic spaces.

Importance of climate forecasting

Climate prediction involves making educated guesses about future climate conditions over a variety of timescales and geographical scopes. Unlike day-to-day weather forecasting, climate predictions relate to deviations from average conditions over upcoming seasons or years.

Sun over a mountain range in Nevada. Source: UnsplashSun over a mountain range in Nevada. Source: Unsplash

The generated data proves valuable for numerous governmental, non-governmental and private entities for long-term planning and decision-making across sectors such as agriculture, disaster prevention, insurance and economic activities. Over the past two decades, researchers have made considerable progress, with many research centers routinely making such predictions due to an enhanced understanding of seasonal predictability sources and advancements in climate models.

The impact of ENSO and climate change hiatuses

The level of predictability varies based on the timescale and the variable being predicted (the predictand). Slowly changing boundary conditions, like sea surface temperatures, soil moisture, snow cover and sea ice, typically offer predictability on seasonal and interannual timescales. The El Niño-Southern Oscillation (ENSO) phenomenon, with its significant influence on the global climate system, provides a major source of seasonal and interannual climate predictability.

Recent research findings suggest that the stratosphere's effect on the troposphere also contributes to seasonal predictability. However, errors in initial and boundary conditions, as well as deficiencies in prediction models, restrict the accuracy of these predictions. Consequently, predicting seasonal anomalies accurately is currently possible only a few months ahead.

The ENSO cycle disrupts regular conditions in the Pacific Ocean, causing significant impacts on global weather, ecosystems and economies.

The climate does not necessarily steadily increase. A 'climate change hiatus' refers to periods of slower short-term temperature trends amidst the consistent long-term trend of global warming. The notion of a hiatus in climate change refers to periods of less certain short-term temperature trends amid the robust multi-decadal long-term trend of global warming since the late 19th century. The 1998 to 2012 hiatus, for instance, shows a rise of 0.05° C per decade, contrasted with a longer-term increase of 0.12° C per decade from 1951 to 2012. Despite these hiatus periods in surface-air temperature records, other facets of the climate system, like sea level rise and Arctic sea ice decline, are thought to consistently exhibit signs of warming.

Change in average temperature. Source: NASA’s Scientific Visualization Studio, Key and Title by uploader (Eric Fisk)Change in average temperature. Source: NASA’s Scientific Visualization Studio, Key and Title by uploader (Eric Fisk)

Prediction models and their limitations

Techniques for estimating seasonal and interannual predictability often evaluate the climate signal due to boundary forcings and the unpredictable climate noise due to random variations, or the signal-to-noise ratio. With the introduction of nonlinear methods like the nonlinear local Lyapunov exponent (NLLE) and the condition nonlinear optimal perturbation, researchers can quantify predictability more accurately, offering valuable insight into climate predictability limitations and guiding future prediction model enhancements.

Seasonal to interannual predictions predominantly depend on empirical approaches using observational data, dynamic methods applying general circulation models (GCMs) or a combination thereof. However, due to the impact of unpredictable weather variations, particularly outside the tropics, these predictions often exhibit low accuracy. It is, therefore, more suitable to present probabilistic forecasts, estimating the likelihood that seasonal or annual mean temperature and precipitation will be above, near or below the norm.

Harnessing machine learning in weather predictions

The UN Paris Agreement intends to limit global temperature rise to well below 2° C, preferably 1.5° C above preindustrial levels. These targets are believed to be able to mitigate numerous climate risks such as health impacts, agricultural disruptions, coastal community threats, ecosystem disturbances and extreme weather events. Given their significance, predicting when these warming thresholds will be reached is a matter of interest to scientists, policymakers and the public.

A study using global climate models, machine learning and historical climate data aimed to forecast when these warming thresholds will be reached under various climate-forcing scenarios. This framework relied on four pillars: identifying when the ensemble-mean forced response hits the global warming threshold for each climate model, training an Artificial Neural Network (ANN) to use simulated temperature anomaly maps to predict when each global warming threshold is met, using observed temperature anomaly maps to predict when each threshold is met, and employing explainable artificial intelligence methods to identify key regions influencing the ANN's prediction.

Areas of improvement in climate modeling

Several key areas of skepticism and potential improvement in climate modeling have been highlighted by experts. Some researchers voice concerns over the Earth System Models' (ESMs) simplistic depiction of crucial processes like plant growth. They underscore the necessity of more accurately incorporating nutrient dynamics, such as nitrogen and phosphorus, into these models.

Improving resolution is another critical point, which can help better capture essential small-scale effects like air flowing over mountain ranges. Current approximations contribute to uncertainty in the predictions generated by these models.

The need for improved cloud simulation is also emphasized. Disagreements among different models about the future behavior of clouds lead to uncertainties in the models' projections.

Better observational data, including long-term measurements from satellites and ground-based observations, is also deemed crucial to refine climate forecasting models.

Efforts should be directed toward reducing uncertainties in climate sensitivity and achieving a better understanding of atmospheric circulation changes due to climate change, according to some experts.

The significance of accurately simulating the water cycle is also a point where improvements can be made, emphasizing the correct representation of precipitation patterns. In tying together these various elements, researchers can hope for a more comprehensive and accurate climate model.

Navigating climate forecasting

Climate prediction is a nuanced field, straddling the dynamic line between the unpredictability of weather and the cyclical predictability of long-term climate trends. While considerable advancements have been made, the inherent challenges and complexities involved should be acknowledged. Innovative methodologies, like machine learning, offer promising avenues to enhance predictive accuracy, particularly in defining warming thresholds.

About the author

Jody Dascalu is a freelance writer in the technology and engineering niche. She studied in Canada and earned a Bachelor of Engineering. As an avid reader, she enjoys researching upcoming technologies and is an expert on a variety of topics.

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