California’s rule book on El Niño and La Niña is broken – San Francisco Chronicle

Report on ENSO Predictability and Implications for Sustainable Development Goals

Introduction and Executive Summary

This report analyzes the current state of the El Niño-Southern Oscillation (ENSO) phenomenon, specifically the persistent La Niña pattern, and its diminishing reliability as a sole predictor for seasonal weather in California. The analysis highlights the critical need for evolving forecasting methodologies to support the achievement of key Sustainable Development Goals (SDGs), particularly those related to climate action, water security, and resilient communities.

• Recent data from the National Oceanic and Atmospheric Administration (NOAA) confirms the continuation of a weak La Niña pattern, characterized by cooler-than-average sea surface temperatures in the equatorial Pacific.
• The traditional correlation between ENSO phases and precipitation outcomes (El Niño-wet, La Niña-dry) has proven increasingly unreliable, posing significant challenges to water management and disaster preparedness, directly impacting SDG 6 (Clean Water and Sanitation) and SDG 11 (Sustainable Cities and Communities).
• Sub-seasonal climate drivers, such as atmospheric rivers and the Madden-Julian Oscillation (MJO), are now understood to exert influence that can override the background ENSO state, necessitating a paradigm shift in forecasting.
• Adapting climate forecasting is a crucial component of SDG 13 (Climate Action), enhancing resilience and adaptive capacity to climate-related hazards.

The Evolving Role of ENSO in Climate Forecasting

Historical Context and Weakening Correlation

For nearly a century, ENSO has been a primary tool for long-range forecasting. However, its predictive power for California’s precipitation has weakened, a trend observed since approximately 1990. This shift complicates long-term planning for water resources and agricultural stability, core targets of the SDGs.

• The public perception of ENSO’s influence was cemented by strong correlations during extremely wet El Niño winters in the 1980s and 1990s.
• Recent anomalous events, such as the intensely wet “triple-dip” La Niña winter of 2022-23, underscore the system’s complexity. During that season, eleven atmospheric rivers delivered a season’s worth of precipitation in weeks, defying typical La Niña expectations.
• Conversely, the 2015-16 El Niño, one of the strongest on record, resulted in a dry winter for California, as the storm track was diverted by other atmospheric dynamics.

The Ascendancy of Sub-Seasonal Climate Drivers

Research indicates that short-term atmospheric patterns are often more influential than the background ENSO state in determining seasonal outcomes. Understanding these drivers is essential for building the climate resilience mandated by SDG 13.

1. Atmospheric Rivers: These concentrated corridors of moisture can deliver massive amounts of rain and snow in short periods, significantly impacting water supply (SDG 6) and flood risk (SDG 11).
2. Madden-Julian Oscillation (MJO): This eastward-moving pulse of tropical thunderstorms can energize the jet stream and direct moisture towards specific regions, capable of overpowering the weaker, seasonal influence of ENSO.

Implications for Sustainable Development Goals (SDGs)

SDG 13: Climate Action

The changing behavior of ENSO is a direct consequence of a warming global climate. The baseline sea-surface temperatures used to measure ENSO are rising, blurring the distinctions between phases and complicating forecasts. Taking urgent action to combat climate change and its impacts requires improving scientific models and predictive capabilities.

• International bodies, including NOAA and the Australian Bureau of Meteorology, are revisiting how ENSO events are calculated and categorized to adapt to this shifting baseline. This reflects a global effort to strengthen institutional capacity for climate adaptation.

SDG 6: Clean Water and Sanitation & SDG 11: Sustainable Cities and Communities

Unpredictable shifts between extreme drought and flood conditions pose a direct threat to sustainable water management and urban resilience.

• Effective management of water reservoirs and flood control infrastructure depends on accurate, timely forecasts.
• The failure of traditional ENSO-based predictions can lead to inadequate preparation for extreme weather, endangering communities and jeopardizing progress toward making human settlements safe, resilient, and sustainable.

Advancements in Forecasting for Enhanced Climate Resilience

A Shift Towards Pattern-Based Methodologies

In response to these challenges, the scientific community is moving away from forecasts based solely on ENSO phase labels. The new focus is on integrated, pattern-based models that provide more actionable intelligence for decision-makers.

• The primary question is shifting from “Is it El Niño or La Niña?” to “How is the current ENSO phase interacting with the jet stream and other sub-seasonal patterns?”
• This approach links the slow-moving oceanic background state with the faster atmospheric dynamics that directly cause weather events.

New Tools and Collaborative Frameworks

Progress in forecasting relies on innovation and partnership, aligning with SDG 17 (Partnerships for the Goals).

1. Layered Predictive Models: Researchers at institutions like Scripps’s Center for Western Weather and Water Extremes (CW3E) now layer sub-seasonal predictors like the MJO onto the ENSO baseline to refine regional forecasts.
2. The Atmospheric River Scale: This tool translates complex atmospheric data into practical guidance for flood forecasters and reservoir managers, directly supporting the objectives of SDG 6 and SDG 11.
3. Jet-Stream Configuration Analysis: Scientists are identifying recurring jet-stream patterns that, when combined with specific ENSO and sub-seasonal states, lead to predictable weather outcomes, bridging the gap between seasonal outlooks and real-time weather.

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article discusses issues related to climate patterns, extreme weather events, and their impacts on communities and water management, connecting to the following Sustainable Development Goals (SDGs):

• SDG 13: Climate Action

  The core of the article focuses on understanding and adapting to climate-related phenomena like El Niño and La Niña. It discusses the increasing unpredictability of these patterns due to a changing climate baseline and the resulting extreme weather events, such as atmospheric rivers and flooding. The efforts by scientific institutions to improve forecasting directly relate to climate adaptation and resilience.

• SDG 11: Sustainable Cities and Communities

  The article highlights the direct impact of these climate events on communities. It mentions “heavy rain causes issues for residents in Berkeley” and shows images of flooding on Interstate 880. The work of “flood forecasters” is aimed at protecting communities and infrastructure from water-related disasters, which is a key aspect of making cities and human settlements resilient.

• SDG 6: Clean Water and Sanitation

  The article explicitly mentions the role of improved weather forecasting for “reservoir managers.” Accurate predictions of wet or dry seasons are critical for managing water resources, ensuring water supply, and controlling floods, which aligns with the goal of integrated water resources management.

2. What specific targets under those SDGs can be identified based on the article’s content?

Based on the article’s discussion of improving weather prediction to manage climate impacts, the following specific SDG targets can be identified:

1. Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.

   The article details the breakdown of old predictive models (“El Niño means wet and La Niña means dry”) and the scientific community’s response. The development of more nuanced, pattern-based forecasting tools that incorporate sub-seasonal forces like the Madden-Julian Oscillation and atmospheric rivers is a direct effort to strengthen adaptive capacity. As the article states, researchers are shifting focus to “how that phase coupling with the jet stream right now” to better prepare for climate-related hazards like floods.

2. Target 13.3: Improve education, awareness-raising and human and institutional capacity on climate change mitigation, adaptation, impact reduction and early warning.

   The article itself serves as a tool for public education, explaining why simple labels like “El Niño” are insufficient for forecasting. It also describes how institutions like NOAA, Scripps’s Center for Western Weather and Water Extremes (CW3E), and the National Center for Atmospheric Research (NCAR) are building their capacity. For example, NOAA is “revisiting how they calculate and categorize ENSO events,” and CW3E developed the “Atmospheric River Scale” to provide practical guidance, enhancing institutional capacity for early warning.

3. Target 11.5: By 2030, significantly reduce the number of deaths and the number of people affected and substantially decrease the direct economic losses relative to global gross domestic product caused by disasters, including water-related disasters…

   The article discusses the severe impacts of atmospheric rivers, which caused a “flood season” and significant disruption. The work of “flood forecasters” using advanced tools is aimed directly at mitigating the damage from these water-related disasters. By providing better and more timely warnings, these efforts help protect people, property, and infrastructure, thereby reducing the human and economic losses caused by such events.

4. Target 6.5: By 2030, implement integrated water resources management at all levels…

   The article mentions that the Atmospheric River Scale provides “practical guidance for… reservoir managers.” This demonstrates a direct application of advanced climate science to water resource management. Better forecasting allows reservoir managers to make more informed decisions about storing water during dry periods and releasing it to prevent flooding during extreme rainfall events, which is a core component of integrated water resources management.

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

The article implies several indicators that can be used to measure progress towards the identified targets, even if it does not cite specific quantitative data:

• Development and adoption of advanced forecasting tools:

  An indicator of progress for Targets 13.1 and 13.3 is the shift away from simplistic ENSO labels towards more complex, pattern-based models. The article highlights the creation and use of tools like the “Atmospheric River Scale” by CW3E and the analysis of “recurring jet-stream configurations” by NCAR. The adoption of these tools by forecasters and managers serves as a measure of increased institutional capacity and adaptive strategies.

• Improved accuracy and lead time of sub-seasonal forecasts:

  The article notes that new pattern-based approaches “can sometimes be predicted weeks ahead, bridging the gap between ENSO’s seasonal rhythm and the weather Californians actually feel.” An increase in the accuracy and lead time of these sub-seasonal forecasts for extreme events like atmospheric rivers would be a key performance indicator for progress in early warning systems (Target 13.3) and disaster reduction (Target 11.5).

• Integration of climate science into resource management protocols:

  For Target 6.5, a clear indicator is the extent to which new forecasting tools are integrated into the operational procedures of water management agencies. The article’s mention of the Atmospheric River Scale being used by “reservoir managers” implies that this integration is already happening. Measuring how many water agencies formally adopt such tools for decision-making would be a concrete indicator of progress in integrated water resources management.

4. Create a table with three columns titled ‘SDGs, Targets and Indicators” to present the findings from analyzing the article.

Source: yahoo.com