In science, what we don’t know can matter just as much as what we do. Just as important—and less often admitted—is what we choose not to ask. In climate science, certain findings are repeated so often they begin to feel like settled truth. Others fade from focus, rarely explored, or gradually left out of public discussion. The shape of the conversation isn’t only determined by evidence. It’s shaped, too, by omission.
The climate system resists simplification. It’s vast, dynamic, and only partly observable. While some areas are well studied, others remain uncertain—either because they’re difficult to measure or because they no longer align with the prevailing carbon-centered framework. That doesn’t make them unimportant. But it does mean they’re often underrepresented in most public summaries of the science.
The historical temperature record is one of the most fundamental datasets in climate science—and one of the most constrained. Modern, reliable surface measurements only extend back about 150 years, and even those early records are uneven in coverage and quality. For earlier periods, scientists rely on proxy data: tree rings, ice cores, corals, sediments. These help reconstruct past climates, but they are not direct measurements. They rely on theory-guided interpretation, statistical modeling, and calibration against relatively recent observational baselines. Different methods sometimes yield different pictures. When modern warming is described as “unprecedented,” that conclusion rests on reconstructions with substantial uncertainty. That doesn’t erase the trend—but it should shape how confidently we present it.
Climate models are another place where evidence meets assumption. These models are sophisticated and useful, but they are not experiments in the traditional sense. They are tools—simulations of a system we do not fully understand, built using inputs we can’t always measure directly. To function, they simplify processes like cloud behavior, ocean currents, and energy exchange at the Earth’s surface. Many variables are tuned to match historical trends. And when they diverge from observed data—as they sometimes do—the mismatch is often downplayed or rationalized. In a healthy scientific culture, such gaps should provoke scrutiny, not be dismissed. They may point to mechanisms we’ve missed or interactions we’ve underestimated.
There are also entire areas of the climate system that receive far less attention than they arguably deserve. The role of solar variability is one. Though total output has stayed roughly stable in recent decades, the sun’s influence on Earth is not purely a matter of wattage. Solar cycles affect ultraviolet radiation, magnetic activity, and possibly even cloud formation through cosmic ray modulation, which may influence cloud nucleation. These are not fringe hypotheses—they’ve been studied for decades—but their marginal status in climate communication narrows the perceived range of possible climate influences. That absence isn’t always intentional—but it still shapes what gets seen, funded, or believed. Sometimes, institutional momentum or research incentives steer attention away from valid but unfashionable questions. Yet if we say the science is “settled,” we should be clear about which parts have been explored thoroughly—and which still carry real uncertainty.
Other overlooked influences are more human in origin. From the 1940s through the early 1960s, nuclear weapons tests injected dust and aerosols into the upper atmosphere on a global scale. These events were brief, but they altered atmospheric composition and circulation in ways that have not been fully revisited. Likewise, documented efforts at weather and climate modification—cloud seeding, stratospheric aerosol trials, and geoengineering experiments—date back over half a century. Projects like Project Stormfury and early U.S. military cloud manipulation attempts are part of the historical record. Their scale was modest—but they reveal a broader pattern: human influence on climate has always extended beyond carbon emissions. That’s not a reason for alarm, but it is a reason to include them in the picture.
Land use changes are another important driver often relegated to the background. Agriculture, irrigation, deforestation, and urbanization all affect how the land interacts with sunlight, moisture, and wind. These changes alter albedo, increase surface roughness, and shift water cycles. Cities, for example, routinely produce their own microclimates through the urban heat island effect. These effects are significant and measurable—but often minimized in mainstream discussions. Perhaps it’s because they don’t map neatly onto the carbon framework that shapes most public messaging. Still, their influence is real and deserves more than a footnote.
The oceans, too, are central to climate—yet until very recently, they remained profoundly undermeasured. Most of the heat trapped by Earth’s energy imbalance ends up in the oceans. However, global-scale subsurface temperature records only became available in the early 2000s with the rollout of the Argo float system. Before that, observations were sparse and uneven. That imbalance matters—because without knowing how heat is distributed below the surface, we risk mistaking assumptions for evidence when interpreting the planet’s response to radiative forcing.
Beyond the data gaps, the oceans also give rise to patterns of natural variability that complicate attribution. Large-scale oscillations like El Niño, the Pacific Decadal Oscillation, and the Atlantic Multidecadal Oscillation emerge from internal dynamics and can shift global climate patterns for years or even decades. These cycles don’t require any human trigger, and they can temporarily mask or amplify long-term trends. That makes attribution studies—especially ones trying to assign percentages of cause—much more difficult. The result is that many attribution claims rely on probabilities, not direct evidence. That distinction is rarely made clear to the public—yet it shapes how people interpret certainty, causation, and risk.
Calling attention to what’s missing is not a case against action. It’s a case for clarity. The science of climate is still evolving. That’s not a flaw—it’s how science works. When gaps are ignored, and complexity is flattened to fit a simplified public message, trust begins to fray. And when skepticism arises, it’s often blamed on ignorance or denial. Sometimes, though, it’s simply a rational response to mixed signals and missing context.
None of this means we should do nothing. It means we should stop treating assumptions as settled facts—and uncertainty as a reason to do nothing. When we overstate our confidence, we encourage backlash. When we simplify the science to fit the politics, we invite doubt. The better path is not more certainty—it’s more honesty. Not because the science is broken, but because trust in it depends on our willingness to admit where it still falls short.
We cannot eliminate every unknown. But we can choose to treat uncertainty as part of the process, not as a threat. If climate science is to remain credible—not just among experts, but with the public—it must resist the temptation to treat open questions as if they’ve already been answered. The gaps aren’t where science fails. They’re where the next questions live.