Full Pacific Basin: [[[100, -60], [180, -60], [180, 60], [100, 60], [100, -60]]] (Western)
                    [[[-180, -60], [-70, -60], [-70, 60], [-180, 60], [-180, -60]]] (Eastern)
Key ENSO Monitoring Regions:
- Niño 3.4 Region: [[[-170, -5], [-120, -5], [-120, 5], [-170, 5], [-170, -5]]]
- Niño 3 Region: [[[-150, -5], [-90, -5], [-90, 5], [-150, 5], [-150, -5]]]
- Niño 1+2 Region: [[[-90, -10], [-80, -10], [-80, 0], [-90, 0], [-90, -10]]]
- Niño 4 Region: [[[160, -5], [-150, -5], [-150, 5], [160, 5], [160, -5]]]

# Core data acquisition from NOAA OISST V2.1
oisst = ee.ImageCollection("NOAA/CDR/OISST/V2_1")

# January 2026 monthly mean computation
jan_2026 = oisst.filterDate('2026-01-01', '2026-01-31').select('sst').mean()

# 30-year climatological baseline (1991-2020)
jan_baseline_list = []
for year in range(1991, 2021):
    jan_baseline_list.append(
        oisst.filterDate(f"{year}-01-01", f"{year}-01-31").select('sst').mean()
    )
jan_climatology = ee.ImageCollection(jan_baseline_list).mean()

# Anomaly computation: January 2026 minus climatology
sst_anomaly = jan_2026.subtract(jan_climatology).divide(100)  # Convert to °C

Expand

Metric	Value	Source
Mean SST	[26.03°C](NOAA OISST V2.1, January 2026 monthly mean)	Computed from 31 daily satellite composites
Climatological Mean (1991-2020)	[26.66°C](NOAA OISST V2.1, 30-year January average)	Reference baseline per WMO standards
SST Anomaly	[−0.63°C](Difference: 26.03°C − 26.66°C)	January 2026 vs. climatology
Standard Deviation	[0.26°C](NOAA OISST V2.1, spatial variability within region)	Indicates thermal homogeneity
ENSO Classification	[La Niña](NOAA threshold: anomaly ≤ −0.5°C)	Per operational criteria
Figure 1: Daily sea surface temperature evolution in the Niño 3.4 region throughout January 2026. The blue line traces observed SST values, while the green dashed line represents the 1991-2020 climatological baseline. The shaded area highlights the persistent negative anomaly characteristic of La Niña conditions. [Data source: NOAA OISST V2.1]

The anomaly of ,[object Object], exceeds the ,[object Object], that NOAA's Climate Prediction Center employs to formally designate La Niña conditions. The consistency of cooling across the 50-degree longitudinal span of the Niño 3.4 region—evidenced by the relatively modest standard deviation of [0.26°C](NOAA OISST V2.1 spatial analysis)—indicates that this is not a localized phenomenon but rather a basin-scale thermal pattern.

The daily time series reveals remarkable stability in La Niña conditions throughout January 2026. SST values ranged from approximately [25.5°C to 26.5°C](NOAA OISST V2.1 daily composites), consistently tracking below the climatological baseline without any sustained warming excursions. This persistence contrasts sharply with transitional ENSO states, where oscillations between positive and negative anomalies often occur within individual months.

• January 2026 Mean SST: [25.66°C](NOAA OISST V2.1, regional spatial mean)
• Climatological Mean: [26.44°C](1991-2020 January baseline)
• SST Anomaly: [−0.78°C](computed difference) Niño 1+2 Region (90°W-80°W, 10°S-0°):
• January 2026 Mean SST: [24.44°C](NOAA OISST V2.1, coastal Peru region)
• Climatological Mean: [24.59°C](1991-2020 January baseline)
• SST Anomaly: [−0.15°C](computed difference)

The gradient of intensifying anomalies toward the Niño 3 region confirms the canonical La Niña spatial pattern: enhanced equatorial upwelling driven by strengthened easterly trade winds brings cold, nutrient-rich waters to the surface across the central and eastern Pacific. The relatively modest anomaly in the Niño 1+2 region reflects the persistent coastal upwelling processes that maintain cool temperatures along the Peru-Chile coastline regardless of ENSO state.

Figure 2: Historical context for January 2026 Niño 3.4 SST values. The hatched cyan bar represents January 2026, positioned within the distribution of January means from 1991-2025. Red bars indicate El Niño Januaries (anomaly > +0.5°C), blue bars indicate La Niña Januaries (anomaly < −0.5°C), and gray bars represent neutral conditions. [Data source: NOAA OISST V2.1, 35-year record] The historical comparison visualization places January 2026 within the multi-decadal record. The current La Niña conditions, while definitive, do not represent extreme values within the historical distribution. The strongest La Niña Januaries in the record—including 1999, 2000, 2008, and 2011—exhibited anomalies approaching or exceeding [−1.5°C](NOAA OISST V2.1 historical archive). The moderate intensity of the current event suggests continued La Niña conditions but without the most severe teleconnection impacts associated with extreme episodes.

• January 2026 Mean SST: [27.49°C](NOAA OISST V2.1, regional spatial mean)
• Climatological Mean: [27.75°C](1991-2020 January baseline)
• SST Anomaly: [−0.26°C](computed difference)

• January 2026 Mean SST: [29.16°C](NOAA OISST V2.1, warm pool regional mean)
• Climatological Mean: [28.91°C](1991-2020 January baseline)
• SST Anomaly: [+0.25°C](computed difference)

The positive anomaly in the western Pacific, juxtaposed against negative anomalies in the central and eastern Pacific, establishes the enhanced zonal SST gradient characteristic of La Niña conditions. This temperature differential strengthens the Walker Circulation—the east-west atmospheric cell that drives Pacific climate dynamics—intensifying trade wind patterns and amplifying the La Niña signal through positive ocean-atmosphere feedback mechanisms. The formula governing ENSO-related trade wind intensification can be expressed as: $\Delta u_{trade} \propto \frac{\partial T_{SST}}{\partial x} = \frac{T_{WP} - T_{EP}}{\Delta x}$ Where $\Delta u_{trade}$ represents the trade wind anomaly, $T_{WP}$ is Western Pacific SST, $T_{EP}$ is Eastern Pacific SST, and $\Delta x$ is the zonal distance. With January 2026 showing $T_{WP} = 29.16°C$ and $T_{EP}$ (Niño 3.4) $= 26.03°C$, the temperature gradient of approximately [3.1°C across 90 degrees longitude](computed from regional means) exceeds climatological values and reinforces the La Niña atmospheric circulation pattern.

Figure 3: Regional decomposition of Pacific Ocean SST anomalies for January 2026. Blue bars indicate regions with cooling (negative anomalies), while warming regions would appear in red. The horizontal dashed lines mark El Niño (+0.5°C) and La Niña (−0.5°C) thresholds. [Data source: NOAA OISST V2.1, multi-region analysis]

• January 2026 Mean SST: [12.58°C](NOAA OISST V2.1, regional spatial mean)
• Climatological Mean: [12.71°C](1991-2020 January baseline)
• SST Anomaly: [−0.13°C](computed difference)

• January 2026 Mean SST: [20.52°C](NOAA OISST V2.1, regional spatial mean)
• Climatological Mean: [20.58°C](1991-2020 January baseline)
• SST Anomaly: [−0.06°C](computed difference)

# Anomaly visualization parameters
sst_anomaly.getThumbURL({
    'min': -3,  # °C cold anomaly
    'max': 3,   # °C warm anomaly
    'palette': [
        '08306b',  # Deep blue (strong cooling)
        '2171b5',  # Medium blue
        '6baed6',  # Light blue
        'c6dbef',  # Pale blue
        'f7fbff',  # Near white (neutral)
        'fff5f0',  # Near white (neutral)
        'fcbba1',  # Pale red
        'fb6a4a',  # Light red
        'cb181d',  # Medium red
        '67000d'   # Deep red (strong warming)
    ],
    'dimensions': 1200,
    'format': 'png'
})

The diverging blue-white-red color scheme provides intuitive interpretation: blue hues indicate waters cooler than the climatological baseline, white represents near-normal conditions, and red hues would indicate warming. For January 2026, the equatorial Pacific presents a striking blue signature extending from approximately [140°E to 80°W longitude](NOAA OISST V2.1 spatial analysis), confirming the basin-scale extent of La Niña cooling.

Figure 4: Sea surface temperature anomaly map for the tropical Pacific Ocean (120°E-80°W, 20°S-20°N) during January 2026. Blue shading indicates below-normal SST, confirming La Niña conditions across the equatorial belt. The most intense cooling appears in the central equatorial Pacific (Niño 3 and 3.4 regions). [Data source: NOAA OISST V2.1, January 2026 monthly composite vs. 1991-2020 climatology]

Figure 5: Western Pacific SST anomaly distribution (100°E-180°, 30°S-30°N) for January 2026. The warm pool region maintains near-normal to slightly elevated temperatures, creating the enhanced east-west thermal gradient that characterizes La Niña circulation patterns. [Data source: NOAA OISST V2.1]

Figure 6: Eastern Pacific SST anomaly distribution (180°W-70°W, 30°S-30°N) for January 2026. Cool anomalies dominate the equatorial band, extending from the dateline eastward toward the South American coast. [Data source: NOAA OISST V2.1]

Beyond anomalies, the absolute SST distribution provides context for understanding ocean thermal structure:

Figure 7: Absolute sea surface temperature (°C) across the tropical Pacific for January 2026. Temperatures range from approximately 24°C in the eastern upwelling zones to over 30°C in the western warm pool. This thermal gradient drives the Walker Circulation atmospheric cell. [Data source: NOAA OISST V2.1] The absolute temperature map reveals the fundamental structure of the Pacific thermal field: the warm pool (waters exceeding 28°C) concentrates west of the dateline, while the cold tongue—normally present along the equator in the eastern Pacific—appears enhanced during La Niña conditions. January 2026 SST values in the cold tongue region reach as low as [24°C](NOAA OISST V2.1 pixel values), approximately 2-3°C below western warm pool temperatures at equivalent latitudes.

• El Niño: ONI ≥ +0.5°C for five consecutive overlapping seasons
• La Niña: ONI ≤ −0.5°C for five consecutive overlapping seasons
• Neutral: −0.5°C < ONI < +0.5°C For January 2026, the single-month Niño 3.4 anomaly of [−0.63°C](NOAA OISST V2.1 analysis) indicates that, assuming continuity with preceding months, La Niña conditions meet the operational threshold. The Climate Prediction Center's operational determinations for the preceding NDJ (November-December-January) season would formally confirm La Niña classification.

The January 2026 anomaly classifies the current La Niña as ,[object Object],, below the threshold of [−1.0°C](NOAA classification standards) that typically designates "strong" events. This moderate intensity suggests that while La Niña teleconnection impacts will manifest, they are unlikely to reach the severity associated with events like 1998-99 or 2010-11.

• Maritime Continent and Australia: The strengthened Walker Circulation increases convective activity over Indonesia, Malaysia, and northern Australia. The Australian Bureau of Meteorology historically correlates La Niña conditions with above-normal rainfall across eastern and northern Australia, with January-March periods showing [30-50% enhanced precipitation probability](BOM climatological statistics).
• Southeast Asia: Monsoon systems receive energized moisture transport from the enhanced warm pool convection, increasing flood risk across Vietnam, Thailand, and the Philippines.
• Amazon Basin: La Niña typically correlates with above-normal rainfall across northern Brazil, supporting hydroelectric generation and agricultural productivity. Reduced Precipitation Regions:
• Southwestern United States: The subtropical jet stream typically shifts northward during La Niña, reducing Pacific storm system penetration into California and the American Southwest. The NOAA Climate Prediction Center seasonal outlooks reflect elevated drought probability for this region.
• Southern South America: Argentina's Pampas region and southern Brazil experience below-normal rainfall during La Niña, creating significant risk for soybean and corn production. The USDA Foreign Agricultural Service monitors these conditions for global commodity supply implications.
• East Africa: The "short rains" season (October-December) and subsequent months often experience deficit conditions during La Niña, impacting food security across Kenya, Somalia, and Ethiopia.

• Northern tier United States and Canada: Amplified meridional flow during La Niña winter seasons increases cold air outbreak frequency across the northern Great Plains and Midwest.
• Japan and Korea: Enhanced Siberian High pressure systems increase cold air advection potential. Warmer-than-normal regions:
• Southern United States: The northward-shifted jet stream reduces cold front penetration, creating milder winter conditions from Texas to Florida.
• Northern South America: Venezuela and Colombia typically experience above-normal temperatures.

• CBOT soybean futures curve structure for backwardation signals
• Brazilian crop progress reports from CONAB
• Argentine rainfall observations from INTA Australian Wheat:

• Northern South America (Colombia, Venezuela, Brazil): Enhanced rainfall supports reservoir levels and generation capacity. Brazil's hydroelectric system, supplying approximately [65% of national electricity](Empresa de Pesquisa Energética), benefits from La Niña conditions.
• Southwestern United States: Reduced precipitation threatens reservoir levels at facilities including Hoover Dam, Glen Canyon Dam, and California's State Water Project facilities. Natural Gas and Heating Demand:

• AVHRR (Advanced Very High Resolution Radiometer): Primary thermal infrared sensor aboard NOAA polar-orbiting satellites, providing global coverage since 1981.
• ACSPO (Advanced Clear Sky Processor for Oceans): Processing algorithm for AVHRR data that retrieves sea surface temperature from calibrated radiance measurements. Bias Correction and Interpolation:

• In situ adjustment: Calibration against buoy, ship, and Argo float measurements
• Cloud contamination masking: Removal of spurious cold pixels from undetected clouds
• Sea ice masking: Exclusion of measurements over sea ice surfaces
• Optimal interpolation: Spatial gap-filling using correlation functions to produce complete daily fields The optimal interpolation methodology can be expressed as: $T_{analyzed}(x,y,t) = T_{background}(x,y,t) + \sum_{i=1}^{N} w_i \cdot [T_{obs,i} - T_{background}(x_i, y_i, t)]$ Where $T_{analyzed}$ is the final SST estimate, $T_{background}$ is the first-guess field (often previous day's analysis), $T_{obs,i}$ are observation points, and $w_i$ are optimal weights determined by correlation structure.

• Absolute accuracy: Approximately [0.3-0.5°C](NOAA NCEI documentation) in well-observed regions
• Precision: Approximately [0.1°C](sub-pixel consistency) for relative changes over time
• Spatial resolution limitations: The 0.25° grid (approximately 25km) smooths mesoscale oceanic features For ENSO monitoring applications—where basin-scale anomalies of 0.5°C or greater define event thresholds—these uncertainty levels are acceptable. The January 2026 Niño 3.4 anomaly of [−0.63°C](This analysis) exceeds measurement uncertainty by a comfortable margin, lending confidence to the La Niña classification.

• No forecast capability: The analysis describes current conditions, not future evolution. La Niña events can persist, decay, or intensify over subsequent months.
• No atmospheric verification: SST anomalies are the oceanic component of ENSO; complete characterization requires atmospheric data (Southern Oscillation Index, trade wind anomalies) not examined here.
• No subsurface analysis: SST measurements capture surface conditions only. Thermocline depth anomalies—critical for ENSO evolution prediction—require subsurface observations from Argo floats or TAO/TRITON buoy arrays.

1. Establish short positions in Argentine soybean production expectations: The La Niña conditions, while moderate, create non-trivial drought risk for the February-March 2026 critical pod-fill period. Monitor weekly precipitation observations and consider options strategies protecting against supply disruption scenarios.
2. Position for Australian wheat surplus: Long-dated wheat exposure through ASX futures or direct Australian physical contracts may capture La Niña-enhanced production potential for the 2026 crop year.
3. Monitor palm oil supply optimism: Southeast Asian production conditions favor above-trend output; consumer sectors may find attractive pricing windows.

1. Northern hemisphere utilities: Prepare load forecasting models for elevated cold outbreak probability through February-March 2026. La Niña patterns increase the likelihood of severe cold air mass penetrations into northern US and Canadian markets.
2. South American hydroelectric operators: Favorable water year conditions are probable; optimize maintenance scheduling and consider deferred spill operations to maximize generation capture.
3. Renewable energy developers: Wind resource patterns shift during La Niña; consult regional climatological guidance for capacity factor projections.

1. Agricultural supply chain exposure: Assess concentration risk in South American grain sourcing; establish contingent supply arrangements from alternative origins (United States, Brazil northern regions less affected by La Niña).
2. Weather derivative positioning: La Niña conditions increase skill for seasonal forecasts; consider structured weather derivatives capturing temperature and precipitation tail risks.
3. Insurance sector: Elevated Atlantic hurricane activity probability warrants review of Gulf Coast and Caribbean exposure aggregation limits.

1. Track Niño 3.4 evolution: Monthly updates will determine whether current La Niña persists into second half 2026 or transitions toward neutral conditions.
2. Subsurface heat content: Kelvin wave propagation and thermocline depth anomalies will govern ENSO evolution; prioritize Argo and TAO array data assimilation.
3. Teleconnection verification: Compare observed global precipitation and temperature patterns against La Niña composites to assess forecast skill and identify regional forecast busts.

• NOAA OISST V2.1 Dataset - Primary SST data source
• Google Earth Engine Data Catalog - Cloud computing access platform ENSO Monitoring Resources:
• NOAA Climate Prediction Center ENSO Advisory
• NOAA Oceanic Niño Index (ONI)
• NOAA PMEL ENSO Page
• Australian Bureau of Meteorology ENSO Outlook Climate Impact Research:
• IRI ENSO Impacts Map Room
• Colorado State University Tropical Meteorology Project
• World Meteorological Organization Climate Normals Agricultural Commodity Intelligence:
• USDA Foreign Agricultural Service IPAD
• CONAB - Brazilian Agricultural Statistics
• INTA - Argentine Agricultural Research Energy Sector Resources:
• CME Group Henry Hub Natural Gas Futures
• Empresa de Pesquisa Energética - Brazil Marine Fisheries:
• Instituto del Mar del Perú (IMARPE)

1. Google Earth Engine Access: Create account at code.earthengine.google.com
2. OISST V2.1 Collection ID: NOAA/CDR/OISST/V2_1
3. Direct Data Download: NOAA NCEI OISST Archive